Patent Publication Number: US-2007112827-A1

Title: Abstract rule sets

Description:
CROSS-RELATED APPLICATIONS  
      This application is related to the following commonly owned applications: U.S. patent application Ser. No. 10/083,075, filed Feb. 26, 2002, entitled “APPLICATION PORTABILITY AND EXTENSIBILITY THROUGH DATABASE SCHEMA AND QUERY ABSTRACTION”, U.S. patent application Ser. No. 11/035,710, filed Jan. 14, 2005, entitled “TIMELINE CONDITION SUPPORT FOR AN ABSTRACT DATABASE”, U.S. patent application Ser. No. 11/083,208, filed Mar. 17, 2005, entitled “SEQUENCE SUPPORT OPERATORS FOR AN ABSTRACT DATABASE”, U.S. patent application Ser. No. ______, filed ______, 2005, entitled “DYNAMIC DISCOVERY OF ABSTRACT RULE SET REQUIRED INPUTS” and U.S. patent application Ser. No. ______, filed ______, 2005, entitled “STRICT VALIDATION OF INFERENCE RULE BASED ON ABSTRACTION ENVIRONMENT”, which are hereby incorporated herein in their entirety. 
    
    
     BACKGROUND OF THE INVENTION  
      1. Field of the Invention  
      The present invention generally relates to rule sets and, more particularly, to abstract rule sets having one or more abstract rules.  
      2. Description of the Related Art  
      Databases are computerized information storage and retrieval systems. A relational database management system is a computer database management system (DBMS) that uses relational techniques for storing and retrieving data. The most prevalent type of database is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses.  
      Regardless of the particular architecture, a DBMS can be structured to support a variety of different types of operations for a requesting entity (e.g., an application, the operating system or an end user). Such operations can be configured to retrieve, add, modify and delete information being stored and managed by the DBMS. Standard database access methods support these operations using high-level query languages, such as the Structured Query Language (SQL). The term “query” denominates a set of commands that cause execution of operations for processing data from a stored database. For instance, SQL supports four types of query operations, i.e., SELECT, INSERT, UPDATE and DELETE. A SELECT operation retrieves data from a database, an INSERT operation adds new data to a database, an UPDATE operation modifies data in a database and a DELETE operation removes data from a database.  
      Data that is collected and stored in a database can be used as input to analysis routines for various purposes, including know-how management, decision making and statistical analysis. For instance, in a broad variety of applications analysis routines are executed on query results obtained in response to execution of corresponding queries against an underlying database.  
      Analysis routines can be defined by rule sets including one or more rules, each having predicates and actions. A rule predicate is evaluated in a rules engine and if the predicate is satisfied, then the associated rule action is executed. In other words, a set of rules can be used to implement an analysis routine and a rules engine will run on the set of rules to evaluate predicates and fire or execute actions defined in the rules. Where actions of rules are defined to provide recommendations for users, such as treatment recommendations for doctors in medical institutions, the rules can be defined such that corresponding predicates reflect expert-based knowledge of possible diagnoses and evaluations of patient conditions. In other words, rules can be implemented to assist doctors by making diagnosis recommendations, drug recommendations, providing reminders of required verifications and checks, etc.  
      However, the creation of rules is generally a complex and difficult process which requires detailed knowledge of a corresponding database(s). More specifically, a predicate of a given rule frequently defines a condition on a column in an underlying database table. In order to create the predicate, the name or some other identifier of the column must be known to the user. In other words, for each predicate and each action of the given rule that the user wants to create, the user requires an understanding of the database schema in order to look up a corresponding column name in the underlying database table(s). Accordingly, the creation of rules is often time consuming and cumbersome.  
      Therefore, there is a need for an effective technique for creating rules that are suitable to implement analysis routines.  
     SUMMARY OF THE INVENTION  
      The present invention generally is directed to a method, system and article of manufacture for processing rule sets and, more particularly, for processing abstract rule sets having one or more abstract rules.  
      One embodiment provides a computer-implemented method of generating recommendations using a suitable rules engine. The method comprises retrieving an abstract rule having a conditional statement and a consequential statement. The consequential statement defines a particular recommendation that is returned when the conditional statement is satisfied. The conditional statement and the consequential statement are defined using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model. The method further comprises transforming the abstract rule into a transformed rule, and executing, by the rules engine, the transformed rule. If the conditional statement is resolved to true for the executed transformed rule, the particular recommendation is returned.  
      Another embodiment provides a computer-implemented method of creating an abstract rule. The method comprises creating a conditional statement using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model. The method further comprises creating, using the logical fields of the abstraction model, a consequential statement that defines a particular recommendation that is returned when the conditional statement is satisfied. The conditional statement is associated with the consequential statement to generate the abstract rule.  
      Still another embodiment provides a computer-readable medium containing a program which, when executed by a processor, performs operations for generating recommendations using a suitable rules engine. The operations comprise retrieving an abstract rule having a conditional statement and a consequential statement. The consequential statement defines a particular recommendation that is returned when the conditional statement is satisfied. The conditional statement and the consequential statement are defined using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model. The operations further comprise transforming the abstract rule into a transformed rule, and executing, by the rules engine, the transformed rule. If the conditional statement is resolved to true for the executed transformed rule, the particular recommendation is returned.  
      Still another embodiment provides a computer-readable medium containing a program which, when executed by a processor, performs operations for creating an abstract rule. The operations comprise creating a conditional statement using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model. The operations further comprise creating, using the logical fields of the abstraction model, a consequential statement that defines a particular recommendation that is returned when the conditional statement is satisfied. The conditional statement is associated with the consequential statement to generate the abstract rule. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      So that the manner in which the above recited features, advantages and objects of the present invention are attained and can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings.  
      It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.  
       FIG. 1  is one embodiment of a computer system utilized in accordance with the invention;  
       FIG. 2  is a relational view of software components of one embodiment of the invention;  
       FIGS. 3-4B  are relational views of software components in one embodiment;  
       FIGS. 5-6  are flow charts illustrating the operation of a runtime component, according to one embodiment of the invention;  
       FIGS. 7-18  are screen shots illustrating creation of an exemplary abstract rule set;  
       FIG. 19  is a screen shot illustrating selection of an abstract rule set for execution on a query result;  
       FIG. 20  is a flow chart illustrating a method of generating recommendations using a suitable rules engine in one embodiment;  
       FIG. 21  is a flow chart illustrating a method of transforming an abstract rule into a transformed rule in one embodiment;  
       FIGS. 22-24  are schematic diagrams illustrating an exemplary execution of the method of  FIG. 21  in one embodiment;  
       FIG. 25  is a flow chart illustrating a method of executing an analysis routine on a query result;  
       FIG. 26  is a flow chart illustrating a method of retrieving suitable data required as input to an analysis routine;  
       FIG. 27  is a flow chart illustrating a method of executing an analysis routine on valid inputs; and  
       FIG. 28  is a flow chart illustrating a method of validating inputs to an analysis routine. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
      Introduction  
      The present invention is generally directed to a method, system and article of manufacture for processing rule sets and, more particularly, for processing abstract rule sets having one or more abstract rules. In the context of the invention, a rule is a set of logical statements including a conditional statement and a consequential statement. The conditional statement defines at least one requirement that must be satisfied by inputs to the rule and the consequential statement defines a predefined action that is executed by the rule when the conditional statement is satisfied. According to one aspect, an abstract rule is a rule having a conditional statement and a consequential statement that are created using logical field definitions defined in an abstraction model. The abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model.  
      In one embodiment, a rules engine is used to generate a recommendation by executing an abstract rule having a conditional statement and a consequential statement on suitable inputs. To this end, the consequential statement of the abstract rule is configured to return a particular recommendation(s) when the conditional statement is satisfied. For execution, the abstract rule is transformed into a transformed rule that is executable on the suitable inputs by the rules engine. The transformed rule is then executed on the suitable inputs and, if the conditional statement is resolved to true for the executed transformed rule, the particular recommendation(s) is returned.  
      In one embodiment, the suitable inputs are determined by selecting suitable field values from a query result set which is obtained in response to execution of a query against a database. The query can be created by a user using a query creation form that is displayed to the user in response to selection of a required abstract rule for execution. Specifically, the query creation form can be configured to guide the user through selection of result fields for the query that are configured to retrieve the suitable field values.  
      In the following, embodiments of the invention may be described with respect to abstract queries. However, it should be noted that the invention is not limited to abstract queries and that embodiments of the invention may use any suitable queries, known or unknown, in order to generate query result sets having result data that is suitable as input(s) to abstract rules according to embodiments of the invention.  
     Preferred Embodiments  
      In the following, reference is made to embodiments of the invention. However, it should be understood that the invention is not limited to specific described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the invention. Furthermore, in various embodiments the invention provides numerous advantages over the prior art. However, although embodiments of the invention may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the invention. Thus, the following aspects, features, embodiments and advantages are merely illustrative and, unless explicitly present, are not considered elements or limitations of the appended claims.  
      One embodiment of the invention is implemented as a program product for use with a computer system such as, for example, computer system  110  shown in  FIG. 1  and described below. The program(s) of the program product defines functions of the embodiments (including the methods described herein) and can be contained on a variety of computer-readable media. Illustrative computer-readable media include, but are not limited to: (i) information permanently stored on non-writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive); (ii) alterable information stored on writable storage media (e.g., floppy disks within a diskette drive or hard-disk drive); or (iii) information conveyed to a computer by a communications medium, such as through a computer or telephone network, including wireless communications. The latter embodiment specifically includes information to/from the Internet and other networks. Such computer-readable media, when carrying computer-readable instructions that direct the functions of the present invention, represent embodiments of the present invention.  
      In general, the routines executed to implement the embodiments of the invention, may be part of an operating system or a specific application, component, program, module, object, or sequence of instructions. The software of the present invention typically is comprised of a multitude of instructions that will be translated by the native computer into a machine-readable format and hence executable instructions. Also, programs are comprised of variables and data structures that either reside locally to the program or are found in memory or on storage devices. In addition, various programs described hereinafter may be identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular nomenclature that follows is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature.  
     An Exemplary Computing Environment  
       FIG. 1  shows a computer  100  (which is part of a computer system  110 ) that becomes a special-purpose computer according to an embodiment of the invention when configured with the features and functionality described herein. The computer  100  may represent any type of computer, computer system or other programmable electronic device, including a client computer, a server computer, a portable computer, a personal digital assistant (PDA), an embedded controller, a PC-based server, a minicomputer, a midrange computer, a mainframe computer, and other computers adapted to support the methods, apparatus, and article of manufacture of the invention. Illustratively, the computer  100  is part of a networked system  110 . In this regard, the invention may be practiced in a distributed computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. In another embodiment, the computer  100  is a standalone device. For purposes of construing the claims, the term “computer” shall mean any computerized device having at least one processor. The computer may be a standalone device or part of a network in which case the computer may be coupled by communication means (e.g., a local area network or a wide area network) to another device (i.e., another computer).  
      In any case, it is understood that  FIG. 1  is merely one configuration for a computer system. Embodiments of the invention can apply to any comparable configuration, regardless of whether the computer  100  is a complicated multi-user apparatus, a single-user workstation, or a network appliance that does not have non-volatile storage of its own.  
      The computer  100  could include a number of operators and peripheral systems as shown, for example, by a mass storage interface  137  operably connected to a storage device  138 , by a video interface  140  operably connected to a display  142 , and by a network interface  144  operably connected to the plurality of networked devices  146  (which may be representative of the Internet) via a suitable network. Although storage  138  is shown as a single unit, it could be any combination of fixed and/or removable storage devices, such as fixed disc drives, floppy disc drives, tape drives, removable memory cards, or optical storage. The display  142  may be any video output device for outputting viewable information.  
      Computer  100  is shown comprising at least one processor  112 , which obtains instructions and data via a bus  114  from a main memory  116 . The processor  112  could be any processor adapted to support the methods of the invention. In particular, the computer processor  112  is selected to support the features of the present invention. Illustratively, the processor is a PowerPC® processor available from International Business Machines Corporation of Armonk, N.Y.  
      The main memory  116  is any memory sufficiently large to hold the necessary programs and data structures. Main memory  116  could be one or a combination of memory devices, including Random Access Memory, nonvolatile or backup memory, (e.g., programmable or Flash memories, read-only memories, etc.). In addition, memory  116  may be considered to include memory physically located elsewhere in the computer system  110 , for example, any storage capacity used as virtual memory or stored on a mass storage device (e.g., direct access storage device  138 ) or on another computer coupled to the computer  100  via bus  114 . Thus, main memory  116  and storage device  138  could be part of one virtual address space spanning multiple primary and secondary storage devices.  
     An Exemplary Rule Creation and Execution Environment  
      Referring now to  FIG. 2 , a relational view of software components in one embodiment is illustrated. The software components illustratively include a user interface  210 , a DBMS  220 , one or more applications  240  (only one application is illustrated for simplicity), a rules engine  280 , an abstract model interface  290  and an abstract rule translator  298 . The abstract model interface  290  illustratively includes a data abstraction model  292  and a runtime component  294 . The DBMS  220  illustratively includes a database  230  and a query execution unit  236  having a query engine  234 .  
      The database  230  is shown as a single database having the data  232 , for simplicity. However, the database  230  can also be implemented by multiple databases which can be distributed relative to one another. Moreover, one or more databases can be distributed to one or more networked devices (e.g., networked devices  146  of  FIG. 1 ). The database  230  is representative of any collection of data regardless of the particular physical representation of the data. A physical representation of data defines an organizational schema of the data. By way of illustration, the database  230  may be organized according to a relational schema (accessible by SQL queries) or according to an XML schema (accessible by XML queries). However, the invention is not limited to a particular schema and contemplates extension to schemas presently unknown. As used herein, the term “schema” generically refers to a particular arrangement of the data  232 .  
      According to one aspect, the application  240  (and more generally, any requesting entity including, at the highest level, users) issues queries against the data  232  in the database  230 . In general, the queries issued by the application  240  are defined according to an application query specification  250  and may be predefined (i.e., hard coded as part of the application  240 ) or generated in response to input (e.g., user input). The application query specification(s)  250  is further described below with reference to  FIGS. 3-6 .  
      Illustratively, the queries issued by the application  240  are created by users using the user interface  210 , which can be any suitable user interface configured to create/submit queries. According to one aspect, the user interface  210  is a graphical user interface. However, it should be noted that the user interface  210  is only shown by way of example; any suitable requesting entity may create and submit queries against the database  230  (e.g., the application  240 , an operating system or an end user). Accordingly, all such implementations are broadly contemplated.  
      In one embodiment, the requesting entity accesses a suitable database connectivity tool such as a Web application, an Open DataBase Connectivity (ODBC) driver, a Java DataBase Connectivity (JDBC) driver or a Java Application Programming Interface (Java API) for creation of a query. A Web application is an application that is accessible by a Web browser and that provides some function beyond static display of information, for instance by allowing the requesting entity to query the database  230 . An ODBC driver is a driver that provides a set of standard application programming interfaces to perform database functions such as connecting to the database  230 , performing dynamic SQL functions, and committing or rolling back database transactions. A JDBC driver is a program included with a database management system (e.g., DBMS  220 ) to support JDBC standard access between the database  230  and Java applications. A Java API is a Java-based interface that allows an application program (e.g., the requesting entity, the ODBC or the JDBC) that is written in a high-level language to use specific data or functions of an operating system or another program (e.g., the application  240 ).  
      Accordingly, the queries issued by the application  240  can be in physical form, such as SQL and/or XML queries, which are consistent with the physical representation of the data  232  for execution against the database  230 . Alternatively, the queries issued by the application  240  are composed using the abstract model interface  290 . Such queries are referred to herein as “abstract queries”. The abstract model interface  290  is further described below with reference to  FIGS. 3-6 . The abstract queries are transformed into a form consistent with the physical representation of the data  232  for execution against the database  230 . In the illustrated example, an abstract query  260  is created on the basis of logical fields defined by the data abstraction model  292 .  
      In one embodiment, the abstract query  260  is translated by the runtime component  294  into a concrete (i.e., executable) query. The executable query is submitted to the query execution unit  236  for execution. It should be noted that the query execution unit  236  illustratively only includes the query engine  234 , for simplicity. However, the query execution unit  236  may include other components, such as a query parser and a query optimizer. A query parser is generally configured to accept a received query input from a requesting entity, such as the application(s)  240 , and then parse the received query. The query parser may then forward the parsed query to the query optimizer for optimization. A query optimizer is an application program which is configured to construct a near optimal search strategy (known as an “access plan”) for a given set of search parameters, according to known characteristics of an underlying database (e.g., the database  230 ), an underlying system on which the search strategy will be executed (e.g., computer system  110  of  FIG. 1 ), and/or optional user specified optimization goals. But not all strategies are equal and various factors may affect the choice of an optimum search strategy. However, in general such search strategies merely determine an optimized use of available hardware/software components to execute respective queries. The query optimizer may then forward the optimized query to the query engine  234  for execution.  
      Illustratively, the executable query is executed by the query engine  234  against the data  232  of the database  230  to determine a result set  282  for the abstract query  260 . The result set  282  includes field values which can be used as inputs to an abstract rule  265 . However, it should be noted that the present invention is not limited to use of field values obtained from query results as inputs to the abstract rule  265 . Instead, any suitable inputs to the abstract rule  265  are broadly contemplated including, for instance, input data provided by a user using the user interface  210 .  
      In one embodiment, the abstract rule  265  is created by a user using a suitable user interface configured to create abstract rules, e.g., the user interface  210 . By way of example, the user interface  210  can be configured to display a graphical user interface that guides the user through creation of the abstract rule  265 . An exemplary graphical user interface is illustrated in  FIGS. 7-18 .  
      According to one aspect, creation of the abstract rule  265  is similar to creation of the abstract query  260 . More specifically, the abstract query  260  is created by creating a results specification and, if required, selection criteria having one or more query conditions, as explained in more detail below with reference to  FIGS. 3-4B . The abstract rule  265  is created by creating a conditional statement and a consequential statement, as explained in more detail below with reference to  FIGS. 7-18 . According to one aspect, the conditional statement and the consequential statement of the abstract rule  265  are created by performing operations which are used to create the query conditions of the abstract query  260 . Accordingly, software components such as the application query specification  250  and the abstract model interface  290  that are used to create the abstract query  260  are also used to create the abstract rule  265 . More specifically, the abstract rule  265  is also defined according to the application query specification  250  and may be predefined (i.e., hard coded as part of the application  240 ) or generated in response to input (e.g., user input). Furthermore, the abstract rule  265  is also composed using the abstract model interface  290 , i.e., on the basis of the logical fields defined by the data abstraction model  292 .  
      The abstract rule  265  is transformed into a transformed rule which is executable by the rules engine  280 . More specifically, the abstract rule  265  is transformed by the abstract rule translator  298  using the data abstraction model  292 . Transformation of the abstract rule  265  into the transformed rule by the abstract rule translator  298  is described in more detail below with reference to  FIGS. 21-24 .  
      In one embodiment, the transformed rule is configured for execution on the result set  282  which is obtained in response to execution of the abstract query  260 . In order to guarantee that the result set  282  includes field values for all required inputs of the abstract rule  265  to enable execution of the transformed rule, the abstract query  260  can be created using a suitable data request form  270 . The data request form  270  is configured to guide the user through selection of suitable result fields for the abstract query  260 , for which fields corresponding field values are required as the inputs to the transformed rule. Thus, it can be guaranteed that the rules engine  280  executes the transformed rule on a valid result set.  
      It should be noted that the abstract rule translator  298  and the rules engine  280  are illustratively shown as separate software components. However, the functionality of the abstract rule translator  298  can also be implemented by the abstract model interface  290  or the rules engine  280 . Furthermore, the functionality of the rules engine  280  can alternatively be implemented by the DBMS  220 . All such implementations are broadly contemplated.  
      In response to execution of the transformed rule on the result set  282 , the rules engine  280  outputs a rule output  284  to the application  240 . By way of example, assume that all required inputs provided by the result set  282  to the transformed rule describe aspects of a medical condition of a patient of a medical institution. Assume further that the abstract rule  265  and, thus, the transformed rule is configured to provide a recommendation for treatment of the patient if the medical condition of the patient satisfies the conditional statement of the transformed rule. In other words, the recommendation is returned as the rule output  284  by the rules engine  280  if the conditional statement is satisfied. Exemplary methods illustrating operation of the rules engine are described below with reference to  FIGS. 20 and 25 - 28 .  
     Logical/Runtime View of Environment  
       FIGS. 3-4B  show an illustrative relational view of the applications  240 , the application query specifications  250  and the data abstraction model  292  of  FIG. 2  and other components of the invention. A requesting entity (e.g., one of the applications  240  or a user) issues the query  260  as defined by the respective application query specification  250  of the requesting entity. The resulting query  260  is generally referred to herein as an “abstract query” because the query is composed according to abstract (i.e., logical) fields rather than by direct reference to the underlying physical data entities in the database  230  of  FIG. 2 . As a result, abstract queries may be defined that are independent of the particular underlying data representation used. In one embodiment, the application query specification  250  may include both criteria used for data selection (selection criteria  304 ) and an explicit specification of the fields to be returned (return data specification  306 ) based on the selection criteria  304 , as illustrated in  FIG. 4A -B.  
      As was noted above, the logical fields specified by the application query specification  250  and used to compose the abstract query  260  are defined by the data abstraction model  292 . In general, the data abstraction model  292  exposes information as a set of logical fields that may be used within a query (e.g., the abstract query  260 ) issued by the application  240  to specify criteria for data selection and specify the form of result data returned from a query operation. Furthermore, the logical fields may be used within a rule (e.g., abstract rule  265  of  FIG. 2 ) to specify a conditional and a consequential statement. The logical fields are defined independently of the underlying data representation being used in a corresponding database (e.g., database  230  of  FIG. 2 ), thereby allowing queries to be formed that are loosely coupled to the underlying data representation.  
      In one embodiment, illustrated in  FIG. 4A -B, the data abstraction model  292  comprises a plurality of field specifications  308   1 ,  308   2 ,  308   3 ,  308   4 ,  308   5 ,  308   6  and  308   7  (seven shown by way of example), collectively referred to as the field specifications  308  (also referred to hereinafter as “field definitions”). Specifically, a field specification is provided for each logical field available for composition of an abstract query. Each field specification may contain one or more attributes. Illustratively, the field specifications  308  include a logical field name attribute  320   1 ,  320   2 ,  320   3 ,  320   4 ,  320   5 ,  320   6 ,  320   7  (collectively, field name  320 ) and an associated access method attribute  322   1 ,  322   2 ,  322   3 ,  322   4 ,  322   5 ,  322   6 ,  322   7  (collectively, access methods  322 ). Each attribute may have a value. For example, logical field name attribute  320 , has the value “FirstName” and access method attribute  322   1  has the value “Simple”. Furthermore, each attribute may include one or more associated abstract properties. Each abstract property describes a characteristic of a data structure and has an associated value. In the context of the invention, a data structure refers to a part of the underlying physical representation that is defined by one or more physical entities of the data corresponding to the logical field. In particular, an abstract property may represent data location metadata abstractly describing a location of a physical data entity corresponding to the data structure, like a name of a database table or a name of a column in a database table. Illustratively, the access method attribute  322   1  includes data location metadata “Table” and “Column”. Furthermore, data location metadata “Table” has the value “contact” and data location metadata “Column” has the value “f_name”. Accordingly, assuming an underlying relational database schema in the present example, the values of data location metadata “Table” and “Column” point to a table “contact” having a column “f_name”.  
      It should be noted that various other attributes are contemplated for the field specifications  308 . For instance, each field specification may include particular metadata, such as timeline metadata. Providing field specifications with timeline data allows for creation of measurement fields in abstract queries, such as the abstract query  260 . A measurement field is a result field that corresponds to a logical field of an underlying data abstraction model and that can be associated in an abstract query with a chronological condition that specifies a requested point in time for which values for the result field should be retrieved from an underlying database. More generally, timeline metadata can be used to order data elements for a logical field according to a chronological sequence. By way of example, commonly owned U.S. patent application Ser. No. 11/083,208, filed Mar. 17, 2005, entitled “SEQUENCE SUPPORT OPERATORS FOR AN ABSTRACT DATABASE”, which is incorporated herein by reference in its entirety, describes timeline metadata which includes type metadata and time-ordering metadata. The timeline type metadata indicates that data retrieved for a given logical field may be ordered into a sequence of discrete events based on the order in which the events occurred, or when the data came into being. The time-ordering metadata specifies where to locate the data used to order data elements for the given logical field into a sequence. Other examples of timeline metadata are described in commonly owned U.S. patent application Ser. No. 11/035,710, filed Jan. 14, 2005 entitled, “TIMELINE CONDITION SUPPORT FOR AN ABSTRACT DATABASE”, which is also incorporated herein by reference in its entirety.  
      In one embodiment, groups (i.e. two or more) of logical fields may be part of categories. Accordingly, the data abstraction model  292  includes a plurality of category specifications  310   1 ,  310   2 ,  310   3  and  310   4  (four shown by way of example), collectively referred to as the category specifications. In one embodiment, a category specification is provided for each logical grouping of two or more logical fields. For example, logical fields  308   1-3  and  308   4-5  are part of the category specifications  310   1  and  310   2 , respectively. Furthermore, logical field  308   6  is part of the category specification  310   3  and logical field  308   7  is part of the category specification  310   4 . A category specification is also referred to herein simply as a “category”. The categories are distinguished according to a category name, e.g., category names  330   1 ,  330   2 ,  330   3  and  330   4  (collectively, category name(s)  330 ). In the present illustration, the logical fields  308   1-3  are part of the “Demographic” category, logical fields  308   4-5  are part of the “Birth and Age” category, the logical field  308   6  is part of the “Gene Expression” category and the logical field  308   7  is part of the “Recommendations” category.  
      The access methods  322  generally associate (i.e., map) the logical field names to data in the database (e.g., database  230  of  FIG. 2 ). As illustrated in  FIG. 3 , the access methods associate the logical field names to a particular physical data representation  214   1 ,  214   2 , . . .  214   N  in the database. By way of illustration, two data representations are shown, an XML data representation  214   1  and a relational data representation  214   2 . However, the physical data representation  214   N  indicates that any other data representation, known or unknown, is contemplated. In one embodiment, a single data abstraction model  292  contains field specifications (with associated access methods) for two or more physical data representations  214 . In an alternative embodiment, a different single data abstraction model  292  is provided for each separate physical data representation  214 .  
      Any number of access methods is contemplated depending upon the number of different types of logical fields to be supported. In one embodiment, access methods for simple fields, filtered fields and composed fields are provided. The field specifications  308   1 ,  308   2 ,  308   5 ,  308   6  and  308   7  exemplify simple field access methods  322   1 ,  322   2 ,  322   5 ,  322   6  and  322   7 , respectively. Simple fields are mapped directly to a particular entity in the underlying physical representation (e.g., a field mapped to a given database table and column). By way of illustration, as described above, the simple field access method  322 , shown in  FIG. 4A  maps the logical field name  320   1  (“FirstName”) to a column named “f_name” in a table named “contact”. In one embodiment, a simple field access method may include an enumeration of possible values for the mapped particular entity in the underlying physical representation. By way of example, the simple field access method  322   6  shown in  FIG. 4B  includes an enumeration  325  that defines as possible values for a column named “endothlin-1” in a table named “tests” the values “up”, “weak”, and “down”. The simple field access method  322   7  shown in  FIG. 4B  includes an enumeration  327  that defines as possible values for a column named “primary_drug” in a table named “recommendations” the values “5FU”, “6-MP”, “6TG” and “53”. The field specification  308   3  in  FIG. 4A  exemplifies a filtered field access method  322   3 . Filtered fields identify an associated physical entity and provide filters used to define a particular subset of items within the physical representation. An example is provided in  FIG. 4A  in which the filtered field access method  322   3  maps the logical field name  320   3  (“AnyTownLastName”) to a physical entity in a column named “I_name” in a table named “contact” and defines a filter for individuals in the city of “Anytown”. Another example of a filtered field is a New York ZIP code field that maps to the physical representation of ZIP codes and restricts the data only to those ZIP codes defined for the state of New York. The field specification  3084  exemplifies a composed field access method  322   4 . Composed access methods compute a logical field from one or more physical fields using an expression supplied as part of the access method definition. In this way, information which does not exist in the underlying physical data representation may be computed. In the example illustrated in  FIG. 4A  the composed field access method  322   4  maps the logical field name  320   4  “AgeInDecades” to “AgeInYears/10”. Another example is a sales tax field that is composed by multiplying a sales price field by a sales tax rate.  
      It is contemplated that the formats for any given data type (e.g., dates, decimal numbers, etc.) of the underlying data may vary. Accordingly, in one embodiment, the field specifications  308  include a type attribute which reflects the format of the underlying data. By way of example, field specification  308   5  in  FIG. 4A  has a type attribute  324  having a value “Integer” that indicates that the underlying data of the logical field “AgeInYears” is defined as integer values. The field specifications  308   6  and  308   7  in  FIG. 4B  have type attributes  326 ,  328 , both having a value “Categorical” that indicates that the underlying data of the logical fields “Endothlin — 1” and “Primary Drug” is defined using categorical values. However, in another embodiment, the data format of the field specifications  308  is different from the associated underlying physical data, in which case a conversion of the underlying physical data into the format of the logical field is required.  
      By way of example, the field specifications  308  of the data abstraction model  292  shown in FIGS.  4 A-B are representative of logical fields mapped to data represented in the relational data representation  214   2  shown in  FIG. 3 . However, other instances of the data abstraction model  292  map logical fields to other physical representations, such as XML.  
      An illustrative abstract query corresponding to the abstract query  260  shown in  FIG. 4A  is shown in Table I below. By way of illustration, the illustrative abstract query is defined using XML. However, any other language may be used to advantage.  
               TABLE I                       ABSTRACT QUERY EXAMPLE                                        001    &lt;?xml version=“1.0”?&gt;       002   &lt;!--Query string representation: (AgeInYears &gt; “55”--&gt;       003   &lt;QueryAbstraction&gt;                     004   &lt;Selection&gt;                     005   &lt;Condition internalID=“4”&gt;       006   &lt;Condition field=“AgeInYears”           operator=“GT” value=“55”                     007   internalID=“1”/&gt;                     008   &lt;/Selection&gt;       009   &lt;Results&gt;                     010   &lt;Field name=“FirstName”/&gt;       011   &lt;Field name=“LastName”/&gt;       012   &lt;Field name=“AnyTownLastName”/&gt;                     013   &lt;/Results&gt;                     014   &lt;/QueryAbstraction&gt;                  
 
      Illustratively, the abstract query shown in Table I includes a selection specification (lines 004-008) containing selection criteria and a results specification (lines 009-013). In one embodiment, a selection criterion consists of a field name (for a logical field), a comparison operator (=, &gt;, &lt;, etc) and a value expression (what is the field being compared to). In one embodiment, the results specification is a list of abstract fields that are to be returned as a result of query execution. A results specification in the abstract query may consist of a field name and sort criteria.  
      An illustrative data abstraction model (DAM) corresponding to the data abstraction model  292  shown in FIGS.  4 A-B is shown in Table II below. By way of illustration, the illustrative Data Abstraction Model is defined using XML. However, any other language may be used to advantage.  
               TABLE II                       DATA ABSTRACTION MODEL EXAMPLE                                        001   &lt;?xml version=″1.0″?&gt;       002   &lt;DataAbstraction&gt;                     003   &lt;Category name=″Demographic″&gt;                     004   &lt;Field queryable=″Yes″ name=″FirstName″ displayable=″Yes″&gt;                     005   &lt;AccessMethod&gt;                     006   &lt;Simple columnName=″f_name″ tableName= ″contact″&gt;&lt;/Simple&gt;                     007   &lt;/AccessMethod&gt;                     008   &lt;/Field&gt;       009   &lt;Field queryable=″Yes″ name=″LastName″ displayable=″Yes″&gt;                     010   &lt;AccessMethod&gt;                     011   &lt;Simple columnName=″l_name″ tableName= ″contact″&gt;&lt;/Simple&gt;                     012   &lt;/AccessMethod&gt;                     013   &lt;/Field&gt;       014   &lt;Field queryable=″Yes″ name=″AnyTownLastName″ displayable=″Yes″&gt;                     015   &lt;AccessMethod&gt;                     016   &lt;Filter columnName=″l_name″ tableName=″contact″                     017   ”contact.city=Anytown”&gt; &lt;/Filter&gt;                     018   &lt;/AccessMethod&gt;                     019   &lt;/Field&gt;                     020   &lt;/Category&gt;       021   &lt;Category name=″Birth and Age″&gt;                     022   &lt;Field queryable=″Yes″ name=″AgeInDecades″ displayable=″Yes″&gt;                     023   &lt;AccessMethod&gt;                     024   &lt;Composed columnName=″age″ tableName=″contact″       025   Expression=″columnName/10″&gt;&lt;/Composed&gt;                     026   &lt;/AccessMethod&gt;                     027   &lt;/Field&gt;       028   &lt;Field queryable=″Yes″ name=″AgeInYears″ type=”Integer”                     029   displayable=″Yes″&gt;                     030   &lt;AccessMethod&gt;                     031   &lt;Simple columnName=″age″ tableName=″contact″&gt;&lt;/Simple&gt;                     032   &lt;/AccessMethod&gt;                     033   &lt;/Field&gt;                     034   &lt;/Category&gt;       035   &lt;Category name=″Gene Expression″&gt;                     036   &lt;Field queryable=″Yes″ name=″Endothlin_1″ type=”Categorical”                     037   displayable=″Yes″&gt;                     038   &lt;AccessMethod&gt;                     039   &lt;Simple columnName=″endothlin-1″ tableName=″tests″&gt;                     040   Values=”up”,”weak”,”down”&gt; &lt;/Simple&gt;                     041   &lt;/AccessMethod&gt;                     042   &lt;/Field&gt;                     043   &lt;/Category&gt;       044   &lt;Category name=″Recommendations″&gt;                     045   &lt;Field queryable=″Yes″ name=″Primary Drug″ type=”Categorical”                     046   displayable=″Yes″&gt;                     047   &lt;AccessMethod&gt;                     048   &lt;Simple columnName=″primary_drug″                     049   tableName=″recommendations″&gt;       050   Values=”5FU”,”6-MP”,”6TG”,”53”&gt; &lt;/Simple&gt;                     051   &lt;/AccessMethod&gt;                     052   &lt;/Field&gt;                     053   &lt;/Category&gt;                     054   &lt;/DataAbstraction&gt;                  
 
      By way of example, note that lines 004-008 correspond to the first field specification  308   1  of the DAM  292  shown in  FIG. 4  and lines 009-013 correspond to the second field specification  308   2 .  
      As was noted above, the abstract query of Table I can be transformed into a concrete query for query execution. An exemplary method for transforming an abstract query into a concrete query is described below with reference to  FIGS. 5-6 .  
     Transforming an Abstract Query Into a Concrete Query  
      Referring now to  FIG. 5 , an illustrative runtime method  400  exemplifying one embodiment of the operation of the runtime component  294  of  FIG. 2  is shown. The method  400  is entered at step  402  when the runtime component  294  receives as input an abstract query (such as the abstract query shown in Table I). At step  404 , the runtime component  294  reads and parses the abstract query and locates individual selection criteria and desired result fields. At step  406 , the runtime component  294  enters a loop (defined by steps  406 ,  408 ,  410  and  412 ) for processing each query selection criteria statement present in the abstract query, thereby building a data selection portion of a concrete query. In one embodiment, a selection criterion consists of a field name (for a logical field), a comparison operator (=, &gt;, &lt;, etc) and a value expression (what is the field being compared to). At step  408 , the runtime component  294  uses the field name from a selection criterion of the abstract query to look up the definition of the field in the data abstraction model  292 . As noted above, the field definition includes a definition of the access method used to access the data structure associated with the field. The runtime component  294  then builds (step  410 ) a concrete query contribution for the logical field being processed. As defined herein, a concrete query contribution is a portion of a concrete query that is used to perform data selection based on the current logical field. A concrete query is a query represented in languages like SQL and XML Query and is consistent with the data of a given physical data repository (e.g., a relational database or XML repository). Accordingly, the concrete query is used to locate and retrieve data from the physical data repository, represented by the database  230  shown in  FIG. 2 . The concrete query contribution generated for the current field is then added to a concrete query statement (step  412 ). The method  400  then returns to step  406  to begin processing for the next field of the abstract query. Accordingly, the process entered at step  406  is iterated for each data selection field in the abstract query, thereby contributing additional content to the eventual query to be performed.  
      After building the data selection portion of the concrete query, the runtime component  294  identifies the information to be returned as a result of query execution. As described above, in one embodiment, the abstract query defines a list of result fields, i.e., a list of logical fields that are to be returned as a result of query execution, referred to herein as a result specification. A result specification in the abstract query may consist of a field name and sort criteria. Accordingly, the method  400  enters a loop at step  414  (defined by steps  414 ,  416 ,  418  and  420 ) to add result field definitions to the concrete query being generated. At step  416 , the runtime component  294  looks up a result field name (from the result specification of the abstract query) in the data abstraction model  292  and then retrieves a result field definition from the data abstraction model  292  to identify the physical location of data to be returned for the current logical result field. The runtime component  294  then builds (at step  418 ) a concrete query contribution (of the concrete query that identifies physical location of data to be returned) for the logical result field. At step  420 , the concrete query contribution is then added to the concrete query statement. Once each of the result specifications in the abstract query has been processed, the concrete query is executed at step  422 .  
      One embodiment of a method  500  for building a concrete query contribution for a logical field according to steps  410  and  418  is described with reference to  FIG. 6 . At step  502 , the method  500  queries whether the access method associated with the current logical field is a simple access method. If so, the concrete query contribution is built (step  504 ) based on physical data location information and processing then continues according to method  400  described above. Otherwise, processing continues to step  506  to query whether the access method associated with the current logical field is a filtered access method. If so, the concrete query contribution is built (step  508 ) based on physical data location information for a given data structure(s). At step  510 , the concrete query contribution is extended with additional logic (filter selection) used to subset data associated with the given data structure(s). Processing then continues according to method  400  described above.  
      If the access method is not a filtered access method, processing proceeds from step  506  to step  512  where the method  500  queries whether the access method is a composed access method. If the access method is a composed access method, the physical data location for each sub-field reference in the composed field expression is located and retrieved at step  514 . At step  516 , the physical field location information of the composed field expression is substituted for the logical field references of the composed field expression, whereby the concrete query contribution is generated. Processing then continues according to method  400  described above.  
      If the access method is not a composed access method, processing proceeds from step  512  to step  518 . Step  518  is representative of any other access method types contemplated as embodiments of the present invention. However, it should be understood that embodiments are contemplated in which less than all the available access methods are implemented. For example, in a particular embodiment only simple access methods are used. In another embodiment, only simple access methods and filtered access methods are used.  
     Creating an Abstract Rule Set  
      Referring now to  FIGS. 7-18 , an exemplary graphical user interface (GUI) is described which illustrates one embodiment of the user interface  210  of  FIG. 2 . According to one aspect, the GUI displays a plurality of GUI screens which are configured for composing abstract queries, such as the abstract query  260  of  FIG. 2 , and/or abstract rule sets having one or more abstract rules, such as the abstract rule  265  of  FIG. 2 . The GUI may further display a plurality of GUI screens which are configured for accessing and/or processing existing abstract queries and/or existing abstract rule sets. In one embodiment, the GUI is implemented using Web-based technologies, such as hyperlinks.  
      Referring first to  FIG. 7 , the exemplary GUI screen  700  is shown illustrating an exemplary welcome screen with a display area  710  that is configured to inform the user about available functionalities. The area  710  includes four selectable graphical elements  720 ,  730 ,  740  and  750 , each being configured to allow selection of the available functionality and having an associated short description of an available functionality. Illustratively, the elements  720 - 750  are shown as hyperlinks each pointing to an underlying application. For example, the hyperlink  720  points to an application (e.g., the application  240  of  FIG. 2 ) that generates GUI screens for creating a new query and the hyperlink  730  points to an application that generates GUI screens for processing existing (i.e., persistently stored) queries. Accordingly, when the user selects the hyperlink  720 , the corresponding application is launched and a plurality of GUI screens is displayed which guide the user through creation of a new abstract query. Exemplary GUI screens which are configured for composing abstract queries are, by way of example, illustrated in commonly owned U.S. patent application Ser. No. 10/083,073, filed Feb. 26, 2002 entitled, “GRAPHICAL USER INTERFACE FOR BUILDING QUERIES WITH HIERARCHICAL CONDITIONS”, which is incorporated herein by reference in its entirety. Selection of the hyperlink  730  is not described in more detail, for brevity.  
      The hyperlink  740  points to an application that generates GUI screens for creating a new abstract rule set and the hyperlink  750  points to an application that generates GUI screens for processing existing (i.e., persistently stored) rule sets. Selecting the hyperlink  750  is also not described in more detail, for brevity.  
      Assume now that the user selects the hyperlink  740  to create a new abstract rule set. Accordingly, an underlying application is launched for displaying a plurality of GUI screens for creation of the new abstract rule set, as described below with reference to  FIGS. 8-18 .  
      Referring now to  FIG. 8 , the illustrative GUI screen  700  is shown displaying a panel  800  after selection of the hyperlink  740  “Create a new Rule Set” in  FIG. 7 . The panel  800  is configured to allow user-specification of a new rule set and includes at its top-left end a selectable tab  810  “Define Rules”. By way of example, two other selectable tabs are shown, a “Modify Input” tab  820  and a “Manage Rule Set” tab  830 . When the “Modify Input” tab  820  is selected, a panel for user modification of created abstract rules is displayed. When the “Manage Rule Set” tab  830  is selected, a panel is displayed which allows the user to manage the created abstract rules, as described below with reference to  FIG. 18 .  
      Illustratively, the panel  800  includes a display area  840  “Rule Set Summary” that displays summary information with respect to the new empty rule set object. The display area  840  includes graphical selection elements  842  and  844  (illustrated as drop-down lists) for selection of a rule specification method and a requested use of the new abstract rule set. By way of example, the rule specification method is set to “Script” using the graphical selection element  842 , such that the new abstract rule set is created in script form. All rules of a given rule set that is created in script form are executed in order from top to bottom as a program block or set of scripting language commands would normally be. However, other rule specification methods are also broadly contemplated and include, by way of example, the so-called “backward chaining” technique. Backward chaining is a technique well known in the art that involves running through all rules of the given rule set repeatedly until a steady state is reached or a so-called “goal” is accomplished. A goal in a backward chaining algorithm is the setting of a specific variable. Upon reaching a given value for the specific variable, the given rule set can terminate and return the given value. This is a class of algorithms used in technologies such as inference engines. By way of example, assume a series of lab tests and demographic characteristics which are input to an underlying analysis routine created in the form of a backward chaining algorithm. The lab tests and demographic characteristics allow for making preliminary diagnosis assertions using a first plurality of rules of the given rule set. As each assertion is made, other factors may become significant and other rules of the given rule set can be fired. Eventually, a point is reached where a given rule can identify a preliminary diagnosis which is the goal of the algorithm and the analysis routine returns at that point. Again, it should be noted that backward chaining algorithms and other similar artificial intelligence techniques are well known in the art. Using the graphical selection element  844 , the requested use is set to “required” indicating that use of the new abstract rule set is required.  
      The panel  800  further includes a rule summary area  846  displaying summaries of all rules which are created for the new abstract rule set. In the given example, no abstract rule was created for the new abstract rule set. Accordingly, the rule summary area  846  merely includes an indication  860  that “The Rule Set has no rules currently defined”. In order to create an abstract rule for the new abstract rule set, a pushbutton  852  “New Rule” can be clicked.  
      Illustratively, the GUI screen  700  further includes three pushbuttons  802 ,  804  and  806 . By clicking the pushbutton  802  “Save”, a new rule set data object can be created for the new abstract rule set and persistently stored. If the pushbutton  804  “Save and Close” is clicked, a new rule set data object is created for the new abstract rule set and persistently stored and the GUI screen  700  is closed. The pushbutton  806  “Close” can be used to close the GUI screen  700  without taking any further action.  
      Referring now to  FIG. 9 , the illustrative GUI screen  700  is shown displaying a panel  900  “Define Conditions” for creation of an abstract rule after clicking the pushbutton  852  “New Rule” in  FIG. 8 . More specifically, the panel  900  is configured to allow user-specification of a conditional statement for the abstract rule. The conditional statement consists of one or more abstract conditions that are defined using logical fields of an underlying data abstraction model (e.g., data abstraction model  292  of  FIG. 2 ). Accordingly, the panel  900  is configured to allow user-specification of the one or more abstract conditions for the conditional statement.  
      In one embodiment, the panel  900  is implemented using a panel that is configured for creation of a query condition(s) for an abstract query (e.g., abstract query  260  of  FIG. 2 ). Accordingly, the panel  900  includes a first display area  902  “Select Condition Field:” that is configured to allow user-selection of a condition field for creation of an abstract condition. The first display area  902  displays a list of user-selectable condition fields, each corresponding to a logical field of the underlying data abstraction model. Illustratively, the list is represented as a tree  910  of folders and files, each folder representing a category of the underlying data abstraction model and each file representing a logical file included with a given category. The files of a given folder can be viewed by selecting the given folder. By way of example, the tree  910  includes a folder  920  which represents a “Demographic” category (e.g., category  310   1  of  FIG. 4A ) and a folder  930  which represents a “Gene Expression” category (e.g., category  310   3  of  FIG. 4B ), and some other exemplary folders. By selecting the folder  920 , illustratively three files  922 ,  924 ,  926 , respectively representing logical fields “ID”, “Gender” and “Age” (e.g., logical field specifications  308   1 - 308   3  of  FIG. 4A ) are displayed.  
      The panel  900  further includes a second display area  904  “Condition Summary” which displays summary information with respect to the conditional statement that is created using the panel  900 . However, in the given example no abstract condition was created for the conditional statement. Accordingly, the second display area  904  merely includes an indication  906  that “No conditions are defined for this rule”. In order to create an abstract condition for the conditional statement, the user needs to select a condition field from the tree  910  in the first display area  902 .  
      Referring now to  FIG. 10 , the illustrative GUI screen  700  is shown displaying a condition definition window  1000  “Condition Details” for specification of an abstract condition after selection of the file  926  corresponding to the condition field “Age” in the tree  910  of  FIG. 9 . The window  1000  displays an indication  1012  “Age” of the condition field that was selected for creation of the abstract condition. The indication  1012  is associated with a checkbox  1010  which is marked as being selected to indicate that the “Age” field is selected for specification of the abstract condition.  
      The window  1000  further displays a descriptor  1020  of a requested condition type for the abstract condition. Illustratively, the requested condition type is defined by a comparison operation “Compare” that is performed on the selected “Age” field. Furthermore, the descriptor  1020  is associated with a radio button  1022  which, when selected, enables the user to create a comparison condition using the “Age” field. In particular, when the user selects the radio button  1022  (as shown), the additional elements are enabled, allowing the user to specify a comparison operator and a value. It should be noted that only a single available condition type is shown in the window  1000 , for simplicity. Accordingly, the radio button  1022  is selected by default, in one embodiment. However, in one embodiment different possible condition types are displayed in the window  1000  and can, thus, be selected using associated radio buttons. Such different possible condition types are similar to possible query condition types. One example is a range condition in which a given value is included within two other values. From a free text perspective there can be “contains” conditions and a host of others, such as “contains a synonym of”, “one value ‘same sentence as’ another value” (the latter finds any data for which two values can be found within the same sentence), etc. Another possible condition type is “exist” which requires that a corresponding value is not null. All such different possible condition types are broadly contemplated. To this end, each possible condition type has an associated radio button and only a single condition type can be selected for the abstract condition using a corresponding associated radio button. All such implementations are broadly contemplated.  
      In order to allow user-specification of parameters defining the comparison operation that should be performed on the basis of the abstract condition on field values which are provided for the “Age” field, an operation definition area  1030  is displayed in the window  1000 . The operation definition area  1030  displays (i) the indication  1012  of the condition field “Age” for which the comparison operation is defined, (ii) a drop-down list  1040  which is configured for selection of a comparison operator, and (iii) a text field  1050  for specification of a requested comparison value. By way of example, assume that the user selects “&lt; less than” as the comparison operator and “40” as the comparison value. In other words, using the abstract condition each field value which is provided for the “Age” field and which is less than 40 can be identified.  
      Illustratively, the GUI screen  700  further includes two pushbuttons  1060  and  1070 . By clicking the pushbutton  1060  “Apply”, the abstract condition is created on the basis of the selections/specifications which have been determined using the window  1000 . If the pushbutton  1070  “Cancel” is clicked, the window  1000  is closed without taking any further action.  
      Referring now to  FIG. 11 , the illustrative GUI screen  700  is shown displaying the panel  900  “Define Conditions” after selection of the pushbutton  1060  “Apply” in  FIG. 10 . According to  FIG. 11 , the second display area  904  “Condition Summary” of the panel  900  now displays summary information with respect to the abstract condition which was created for the conditional statement using the window  1000  of  FIG. 10 . More specifically, the second display area  904  includes an indication  1110  which represents the created abstract condition, i.e., “(Age&lt;40)”. The indication  1110  is associated with a checkbox  1120  which allows the user to indicate whether the abstract condition according to the indication  1110  should be included with the conditional statement.  
      The second display area  904  further displays a pushbutton  1130  “Delete” and another pushbutton  1140  “NOT”. The pushbutton  1130  can be clicked to delete a selected abstract condition from the conditional statement and the pushbutton  1140  can be clicked to negate a selected abstract condition. In other words, the user needs to select the created abstract condition using the checkbox  1120  in order to perform an operation according to one of the pushbuttons  1130  and  1140  thereon.  
      If the user has specified all abstract conditions that are required for the conditional statement, the user may click a pushbutton  1150  “DONE” to return to display of the panel  800  of  FIG. 8  to continue specification of the abstract rule. Otherwise, the user may specify one or more additional abstract conditions for the conditional statement. To this end, the user simply needs to select another condition field from the tree  910  in the first display area  902  in order to create a next abstract condition for the conditional statement.  
      Referring now to  FIG. 12 , the illustrative GUI screen  700  is shown displaying the panel  900  “Define Conditions” after specification of a next abstract condition. For instance, assume that the user selected as condition field a file  1210  “Endothlin — 1” from the folder  930  representing the category “Gene Expression” in the tree  910  displayed in the first display area  902 . In response to selection of the condition field “Endothlin — 1”, a GUI screen is displayed that is similar to the GUI screen  700  of  FIG. 10 . For brevity, this GUI screen is not described in more detail. However, assume now that the user specifies as comparison operator “= equal to” and as comparison value “Down” using this GUI screen.  
      Accordingly, the second display area  904  “Condition Summary” of the panel  900  now displays (i) the indication  1110  which represents the abstract condition “(Age&lt;40)” with the associated checkbox  1120 , and (ii) an indication  1220  which represents the next abstract condition, i.e., “(Endothlin — 1=Down)” with an associated checkbox  1230 .  
      The second display area  904  further includes a group operator indication  1250  “AND” which indicates that both abstract conditions are logically combined using a Boolean AND operator. Illustratively, the AND operator can be selected by clicking a pushbutton  1240  “Group AND”. However, other operators can be selected and are, thus, broadly contemplated. By way of example, the user may click a pushbutton  1260  “Group OR” to combine the abstract conditions using a Boolean OR operator or a pushbutton  1270  “Ungroup” to ungroup selected abstract conditions.  
      Assume now that the user has specified all abstract conditions that are required for the conditional statement. Accordingly, the user clicks pushbutton  1150  “DONE” to return to display of the panel  800  of  FIG. 8  to continue specification of the abstract rule.  
      Referring now to  FIG. 13 , the illustrative GUI screen  700  is shown displaying the panel  800  after selection of the pushbutton  1150  “Done” in  FIG. 12 . According to  FIG. 13 , the rule summary area  846  now displays an indication of the abstract rule which is in the process of being created. More specifically, the rule summary area  846  displays an indication  1310  of the conditional statement (hereinafter referred to as the “conditional statement 1310”, for brevity) that was created for the abstract rule of the new abstract rule set according to  FIGS. 9-12 . However, in the given example no consequential statement was created for the abstract rule. Accordingly, the rule summary area  846  includes an indication  1320  that prompts the user to “Add a consequence to this condition . . . ”. The indication  1320  is illustratively implemented as a hyperlink which, upon selection, causes display of a plurality of GUI screens described below with reference to  FIGS. 14-16 . The screens of  FIGS. 14-16  are configured for specification of a consequential statement for the abstract rule.  
      Referring now to  FIG. 14 , the illustrative GUI screen  700  is shown displaying the panel  900  “Define Conditions” after selection of the hyperlink  1320  “Add a consequence to this condition . . . ” in  FIG. 13 . In one embodiment, the panel  900  is configured to allow user-specification of a consequential statement for the abstract rule. The consequential statement consists of one or more abstract recommendations (also referred to herein as “consequences”) that are defined using logical fields of an underlying data abstraction model (e.g., data abstraction model  292  of  FIG. 2 ). Each abstract recommendation can be defined in a manner that is similar to defining an abstract condition for a conditional statement of an underlying abstract rule. Accordingly, the panel  900  which was used for creation of the conditional statement according to  FIGS. 9-12  can be used for creation of the consequential statement, in one embodiment. However, according to  FIG. 14  the second display area  904  “Condition Summary” of the panel  900  now displays summary information with respect to the consequential statement that is created using the panel  900 .  
      In the given example no abstract recommendation was created for the consequential statement. Accordingly, the second display area  904  merely includes an indication  1406  that “No consequences are defined for this rule”. In order to create an abstract recommendation for the consequential statement, the user needs to select a condition field from the tree  910  in the first display area  902 . Assume now that the user selects a file  1420  from a folder  1410  of the tree  910 . Illustratively, the folder  1420  corresponds to a category “Recommendations” (e.g., category  310   4  of  FIG. 4B ) and the file corresponds to a condition field “Primary Drug” (e.g., logical field specification  308   7  of  FIG. 4B )”.  
      Referring now to  FIG. 15 , the illustrative GUI screen  700  is shown displaying the condition definition window  1000  “Condition Details” of  FIG. 10  after selection of the condition field “Primary Drug” in  FIG. 14 . According to  FIG. 15 , the window  1000  now displays an indication  1512  “Primary Drug” of the condition field that was selected for creation of the abstract recommendation. The indication  1512  is associated with a checkbox  1510  which is marked as being selected to indicate that the “Primary Drug” field is selected for specification of the abstract recommendation.  
      The window  1000  further displays a descriptor  1520  of a requested condition type for the abstract recommendation, which is illustratively defined by a comparison operation “Compare”. The descriptor  1520  is associated with a radio button  1522  which is shown as being selected by default, as described in more detail above with reference to  FIG. 10 . However, it should be noted that with respect to the abstract recommendation the comparison operation is merely used to assign a recommended value to the selected condition field. In other words, in the given example the comparison operation is used to assign a recommended value to the “Primary Drug” field, as explained in more detail below. To this end, the comparison operation is set by default to the assignment parameter “==” (i.e., equal to), in one embodiment.  
      In order to allow user-selection of the recommended value for the selected condition field, a list  1530  of available values (e.g., values  327  of  FIG. 4B ) is displayed for the field, each having an associated radio button for selection thereof. Illustratively, a radio button  1540  was selected by the user to assign a value “5FU” to the selected condition field “Primary Drug”. Then, by clicking the pushbutton  1060  “Apply” the abstract recommendation is created on the basis of the selections/specifications which have been determined using the window  1000  of  FIG. 15 .  
      Referring now to  FIG. 16 , the illustrative GUI screen  700  is shown displaying the panel  900  “Define Conditions” after selection of the pushbutton  1060  “Apply” in  FIG. 15 . According to  FIG. 16 , the second display area  904  “Condition Summary” of the panel  900  now displays summary information with respect to the abstract recommendation which was created for the consequential statement using the window  1000  of  FIG. 15 . More specifically, the second display area  904  includes an indication  1610  which represents the created abstract recommendation, i.e., “(Primary Drug=5FU)”. The indication  1610  is associated with a checkbox  1620  which allows the user to indicate whether the abstract recommendation according to the indication  1610  should be included with the consequential statement. If not, the pushbutton  1130  “Delete” can be clicked to delete a selected abstract recommendation from the second display area  904 .  
      If the user has specified all abstract recommendations that are required for the consequential statement, the user may click the pushbutton  1150  “DONE” to return to display of the panel  800  of  FIG. 13  to continue and/or complete specification of the abstract rule. However, in one embodiment the user may specify one or more additional abstract recommendations for the consequential statement, as described above with reference to  FIGS. 14-15 .  
      Referring now to  FIG. 17 , the illustrative GUI screen  700  is shown displaying the panel  800  after selection of the pushbutton  1150  “Done” in  FIG. 16 . According to  FIG. 17 , the rule summary area  846  now displays the conditional statement  1310  and an indication  1710  of the consequential statement (hereinafter referred to as the “consequential statement 1710”, for brevity) that was created for the abstract rule of the new abstract rule set according to  FIGS. 14-16 .  
      The conditional statement  1310  is associated with a checkbox  1720  and the consequential statement  1710  is associated with a checkbox  1730 . The checkboxes  1720  and  1730  allow separate selection of the conditional and/or the consequential statement for a required processing. For instance, if the checkbox  1720  is selected and a pushbutton  1740  “Delete” is clicked, the conditional statement  1310  is deleted. If a pushbutton  1750  “Edit” is clicked, a GUI screen is opened which allows editing of the conditional statement. Furthermore, if a pushbutton  1760  “Copy” is clicked, the conditional statement is copied, for instance, for an additional abstract rule for the new abstract rule set. The provision of copying functions made available by, e.g., the pushbutton  1760 , allows the user to reuse previously composed statements in building other abstract rules. Such additional abstract rules can also be created by selecting the pushbutton  852  “New Rule”. In this case, the additional abstract rules can be created as described above with reference to  FIGS. 9-16 .  
      However, for simplicity assume that the new abstract rule set should only include the abstract rule that was created as described above with reference to  FIGS. 8-17 . An exemplary abstract rule corresponding to this abstract rule is shown in Table III below, which, for simplicity, is described in natural language without reference to a particular encoding language.  
               TABLE III                       ABSTRACT RULE EXAMPLE                                                001   IF                             002   ((Age &lt; 40) AND           003    (Endothlin_1 == “Down”))                             004   THEN                             005   Primary Drug = 5FU                      
 
      Illustratively, the exemplary abstract rule shown in Table III includes in lines 002-003 the conditional statement which was created according to  FIGS. 9-12 . The consequential statement that was created according to  FIGS. 14-16  is shown in line 005. By way of example, the exemplary abstract rule of Table III is implemented as an IF/THEN rule. In other words, if the conditional statement in lines 002-003 is resolved to true with respect to underlying inputs to the exemplary abstract rule, then the recommendation included with the consequential statement in line 005 is returned.  
      As was noted above with reference to  FIG. 8 , the user may now click one of the pushbuttons  802  “Save”,  804  “Save and Close”, or  806  “Close”. If one of the pushbuttons  802  or  804  is clicked after creation of a new abstract rule(s), or if the selectable tab “Manage Rule Set” is clicked, the GUI screen  700  of  FIG. 18  is displayed.  
      Referring now to  FIG. 18 , the illustrative GUI screen  700  is shown displaying a panel  1800  after selection of the selectable tab  830  “Manage Rule Set” in  FIG. 17 . The panel  1800  is configured to allow the user to manage the new abstract rule set which has been created as described above with reference to  FIGS. 8-17 .  
      Illustratively, the panel  1800  includes a first input field  1810  and a second input field  1820 . The first input field  1810  is configured to receive user input specifying a name for the new abstract rule set. Illustratively, “My RuleSet” was entered into the first input field  1810  as name for the new abstract rule set. The second input field  1820  is configured to receive user input for a short description of the new abstract rule set. Illustratively, “This is a demo rule set that will set a primary drug recommendation based on a very simple rule.” was entered into the second input field  1820  as short description for the new abstract rule set.  
      The user may then, again, click one of the pushbuttons  802 ,  804  and  806 . Assume now that the user clicks the pushbutton  802  “Save” to create a new rule set data object. Accordingly, in the given example a rule set data object “My RuleSet” is created which is persistently stored. The persistently stored rule set data object “My RuleSet” includes the abstract rule illustrated in  FIG. 17 . Subsequently, the rule set data object “My RuleSet” can be retrieved for execution on suitable inputs, as explained in the following with reference to  FIG. 19 .  
     Generating Recommendations Using a Suitable Rules Engine  
      Referring now to  FIG. 19 , an illustrative GUI screen  1900  is shown displaying a query result  1910  and a graphical selection element  1930  for selection of an analysis routine. By way of example, the query result  1910  defines a medical condition of a patient of a medical institution. Assume now that a doctor in the medical institution wants to determine a treatment recommendation for the patient on the basis of the medical condition which is defined by the query result  1910 .  
      According to one aspect, the query result  1910  was obtained in response to execution of an abstract query (e.g., abstract query  260  of  FIG. 2 ) against an underlying database (e.g., database  230  of  FIG. 2 ) having required result data. The abstract query may have been created using a suitable data request form (e.g., data request form  270  of  FIG. 2 ), as described above with reference to  FIG. 2 . Accordingly, the abstract query was configured for retrieval of field values from the required result data for each condition field of the conditional statement of the exemplary abstract rule of Table III. However, it should be noted that the invention is not limited to execution of an abstract query in order to determine a suitable query result. Instead, any suitable query type including SQL and XML queries is broadly contemplated.  
      Illustratively, the query result  1910  includes only a single data record  1920  having three different result fields, “ID”, “Age” and “Endothlin — 1”. In the given example, the data record  1920  includes a field value  1922  “30” for the result field “Age”, which corresponds to the condition field “Age” in line 002 of Table III. The data record  1920  further includes a field value  1924  “Down” for the result field “Endothlin — 1”, which corresponds to the condition field in line 003 of Table III.  
      The query result  1910  can be stored, deleted or otherwise processed. To this end, the GUI screen  1900  illustratively displays suitable pushbuttons  1950  which are configured to initiate a requested processing. Furthermore, CSV and/or XML output can be created on the basis of the query result  1910  by clicking a corresponding one of displayed hyperlinks  1960 .  
      In one embodiment, the query result  1910  is used as input to a requested analysis routine that is defined by one or more abstract rule sets. However, it should be noted that in one embodiment the input to the analysis routine is provided without execution of a query against an underlying database. For instance, the input is provided by a user using the user interface  210  of  FIG. 2 . All such different implementations are broadly contemplated.  
      Illustratively, the requested analysis routine can be selected by selecting an underlying abstract rule set from the graphical selection element  1930 , which is illustratively implemented as a drop-down list. The drop-down list  1930  includes a list of available persistently stored abstract rule sets which can be selected for execution on the query result  1910 . By way of example, the abstract rule set “My RuleSet” that was created according to  FIGS. 8-18  above is selected. Assume now that the abstract rule set “My RuleSet” is configured to return a suitable treatment recommendation. Assume further that the doctor in the given medical institution executes the requested analysis routine which is defined by the abstract rule set “My RuleSet” on the query result  1910  to determine the treatment recommendation for the given patient.  
      The requested analysis routine is executed on the query result  1910  by clicking a pushbutton  1940  “RUN”. As was noted above, the abstract rule set “My RuleSet” which defines the requested analysis routine only includes the exemplary abstract rule of Table III. In other words, in the given example execution of the exemplary abstract rule of Table III is initiated by clicking the pushbutton  1940 . An exemplary method of executing the abstract rule of Table III on the query result  1910  is described below with reference to  FIG. 20 .  
      It should be noted that the GUI screen  1900  is merely illustrated by way of example and that various other implementations are possible. For instance, in one embodiment the user may initially select the requested analysis routine by selecting the underlying abstract rule set from the drop-down list  1930 . Then, in response to clicking the pushbutton  1940  “RUN”, a data request form (e.g., data request form  270  of  FIG. 2 ) is displayed to the user. As was noted above, the data request form can be configured to guide the user through selection of suitable result fields for an abstract query which are configured to retrieve all field values that are required as inputs to the selected analysis routine. After user-specification of the abstract query using the data request form, the abstract query is executed and the query result  1910  is obtained. The selected analysis routine is then executed on the query result  1910  as described above. It is understood that all such different implementations are broadly contemplated.  
      Referring now to  FIG. 20 , an exemplary method  2000  of generating recommendations using a suitable rules engine (e.g., rules engine  280  of  FIG. 2 ) is illustrated. At least part of the steps of method  2000  are performed using an abstract rule translator (e.g., abstract rule translator  298  of  FIG. 2 ), an application (e.g., application  240  of  FIG. 2 ) and an abstract model interface (e.g., abstract model interface  290  of  FIG. 2 ). In one embodiment, the method  2000  is performed in response to a click on pushbutton  1940  of the exemplary GUI screen  1900  of  FIG. 19 . Method  2000  starts at step  2010 .  
      At step  2020 , all abstract rules defining a selected analysis routine are retrieved for execution on provided inputs. Each retrieved abstract rule has a conditional statement and a consequential statement. In the given example, the selected analysis routine is defined by the abstract rule set “My RuleSet” and the provided inputs are defined by the field values  1922  and  1924  of the query result  1920  according to  FIG. 19 . Accordingly, only the abstract rule of Table III is retrieved for execution on the field values “30” and “Down”. By way of example, the following steps of the method  2000  are explained with reference to the given example.  
      At step  2030 , the abstract rule of Table III is transformed into a transformed rule. In one embodiment, the transformed rule is executable by the suitable rules engine. An exemplary method of transforming an abstract rule into a transformed rule is described in more detail below with reference to  FIGS. 21-24 .  
      According to one aspect, transforming the abstract rule into the transformed rule includes generating an output template for the selected analysis routine. The output template includes a plurality of tags, one for each possible output of the selected analysis routine. Thus, the output template can be completed with suitable outputs defined by the selected analysis routine after execution of the selected analysis routine on the provided inputs, and returned as analysis result. However, any suitable implementation that allows an obtained analysis result to be returned is broadly contemplated.  
      At step  2040 , the transformed rule is executed on the query result of Table III by the suitable rules engine. At step  2050 , it is determined whether the conditional statement in lines 002-003 of the abstract rule of Table III resolves to true with respect to the field values  1922  and  1924  of  FIG. 19 . If the conditional statement is not resolved to true, method  2000  exits at step  2070 . If, however, the conditional statement is resolved to true, method  2000  continues at step  2060 . In the given example, the field values  1922  and  1924  of the query result  1910  satisfy the conditional statement of lines 002-003 of Table III, i.e., the Age is less than 40 years and the Endothlin-1 gene is down. Accordingly, the conditional statement is resolved to true and the method  2000  continues at step  2060 .  
      At step  2060 , the abstract rule of Table III fires. In other words, a particular recommendation defined by the consequential statement of the abstract rule of Table III is returned. In the given example, the recommendation of line 005 of Table III is returned to the doctor, i.e., the recommendation to prescribe for the patient as primary drug the drug 5FU. Method  2000  then exits at step  2070 .  
     Transforming an Abstract Rule Into a Transformed Rule  
      Referring now to  FIG. 21 , an exemplary method  2100  of transforming an abstract rule (e.g., abstract rule  265  of  FIG. 2 ) into a transformed rule is illustrated. At least part of the steps of method  2100  are performed using a suitable abstract rule translator (e.g., abstract rule translator  298  of  FIG. 2 ) and an underlying data abstraction model (e.g., data abstraction model  292  of  FIG. 2 ). In one embodiment, the method  2100  is entered from step  2030  of the method  2000  of  FIG. 20 . By way of example, the method  2100  is explained with respect to transformation of the abstract rule of Table III above into a transformed rule that is accepted by a suitable rules engine (e.g., rules engine  280  of  FIG. 2 ). The transformed rule is described with reference to the ABLE rule language (ARL) which is accepted by a multiplicity of available rules engines. However, any other suitable language is broadly contemplated. Method  2100  starts at step  2110 .  
      At step  2120 , the abstract rule of Table III is retrieved for transformation. For instance, the suitable abstract rule translator retrieves the abstract rule from an underlying database (e.g., database  230  of  FIG. 2 ). In one embodiment, the retrieved abstract rule is defined in a computer-readable language, such as XML. However, any suitable computer-readable language is broadly contemplated.  
      At step  2130 , all conditions fields are identified from the conditional statement in lines 002-003 and the consequential statement in line 005 of the exemplary abstract rule of Table III. In the given example, the condition fields “Age” and “Endothlin — 1” are identified from the conditional statement. The condition field “Primary Drug” is identified from the consequential statement. Furthermore, all logical field specifications from the underlying data abstraction model which are referenced by the identified condition fields are determined. For instance, the logical field specification  308   6  “Endothlin — 1” of  FIG. 4B  is determined with respect to the condition field “Endothlin — 1” and the logical field specification  308   7  “Primary Drug” of  FIG. 4B  is determined with respect to the condition field “Primary Drug”.  
      At step  2140 , a variable declaration is created for each identified condition field. To this end, all data types of the referenced logical field specifications are determined from the underlying data abstraction model. In the given example, the data type “Categorical” is determined for the logical field specifications “Endothlin — 1” and “Primary Drug” according to the type attributes  326  and  328  in  FIG. 4B . Assume further that the data type “Integer” is determined for the logical field specification “Age” (e.g., data type  324  of  FIG. 4A ). Furthermore, any retrievable enumeration of valid values for each one of the referenced logical field specifications is determined from the underlying data abstraction model. For instance, the valid values “up”, “weak” and “down” are determined from the “Endothlin — 1” field specification  308   6  of  FIG. 4B . On the basis of the determined referenced logical field specifications, the determined data types and enumerations of valid values, corresponding variable declarations are generated as described in more detail below with reference to  FIG. 22 .  
      At step  2150 , an inputs specification is created for the identified condition fields of the conditional statement on the basis of the referenced logical field specifications. Furthermore, at step  2160  an outputs specification is created for the identified condition fields of the consequential statement. Creation of the inputs specification and the outputs specification in the given example is described in more detail below with reference to  FIG. 23 .  
      At step  2170 , a rule specification is created for the IF/THEN statement defined by the abstract rule of Table III. Creation of the rule specification in the given example is described in more detail below with reference to  FIG. 24 .  
      The variable declaration, the inputs specification, the outputs specifications and the rule specification define the transformed rule which is accepted by the suitable rules engine. Method  2100  then exits at step  2180 .  
      Referring now to  FIG. 22 , a schematic diagram  2200  is shown which illustrates generation of variable declarations for the identified condition fields “Age”, “Endothlin — 1” and “Primary Drug” in the given example. The schematic diagram  2200  includes an illustration  2210  of the abstract rule of Table III above, having the conditional statement  1310  according to  FIG. 13  and the consequential statement  1710  according to  FIG. 17 . The schematic diagram further includes a schematic illustration of the data abstraction model  292  of  FIGS. 2-4B  with the logical field specifications  3086  and  3087 . Furthermore, the schematic diagram  2200  includes a variable declaration  2220  for the identified condition fields. As was noted above, the variable declaration  2220  is generated in the ABLE rule language (ARL) by way of example.  
      The variable declaration  2220  includes a generic section  2250  which indicates that all enclosed code in the declaration  2220  defines variables of the transformed rule. For each condition field which has no enumeration of valid values, a variable is declared according to the determined data type of the field. In the given example, a variable  2242  “Integer Age” is declared for the condition field “Age”, as illustrated by an arrow  2232 . For each condition field which has an enumeration of valid values, a corresponding definition including the enumeration of valid values is declared according to the determined data type. In the given example, a definition  2244  “Categorical Endothlin — 1=new Categorical(new String{“Down”, “Up”, “Weak”}))” is created for the condition field “Endothlin — 1”, as illustrated by an arrow  2234 . Similarly, a definition  2246  is created for the condition field “Primary Drug”, as illustrated by an arrow  2236 . The definitions  2244  and  2246  include enumerations of corresponding valid values, which are retrieved from the referenced logical field specifications  308   6  and  308   7 , as illustrated by arrows  2262  and  2264 .  
      Referring now to  FIG. 23 , a schematic diagram  2300  is shown which illustrates generation of an inputs and an outputs specification for the identified condition fields “Age”, “Endothlin — 1” and “Primary Drug” in the given example. The schematic diagram  2300  includes the illustration  2210  of the abstract rule having the conditional statement  1310  and the consequential statement  1710  according to  FIG. 22 . The schematic diagram further includes an inputs and outputs declaration  2310  for the identified condition fields. As was noted above, the inputs and outputs declaration  2310  is generated in the ABLE rule language (ARL), by way of example.  
      The inputs and outputs declaration  2220  illustratively includes an inputs specification  2332  and an outputs specification  2334 . In the inputs specification  2332 , all logical field specifications that are referenced by condition fields included with the conditional statement  1310  are declared, as illustrated by arrows  2322  and  2324 . Accordingly, in the given example the inputs specification  2332  is defined as “inputs{Age, Endothlin — 1};”. In the outputs specification  2334 , all logical field specifications that are referenced by condition fields included with the consequential statement  1710  are declared, as illustrated by an arrow  2326 . Accordingly, in the given example the outputs specification  2334  is defined as “outputs{Primary Drug);”.  
      Referring now to  FIG. 24 , a schematic diagram  2400  is shown which illustrates generation of a rules specification for the IF/THEN statement that is defined by the abstract rule of Table III in the given example. The schematic diagram  2400  includes the illustration  2210  of the abstract rule having the conditional statement  1310  and the consequential statement  1710  according to  FIG. 22 , which form the IF/THEN statement. The schematic diagram further includes a rules specification  2410  of the IF/THEN statement in the ABLE rule language (ARL), by way of example.  
      The rules specification  2410  illustratively includes a generic section  2420  which indicates that all enclosed code in the specification  2410  defines IF/THEN statements of the transformed rule. Furthermore, the specification  2410  includes a statement  2430  defining the IF/THEN statement that is defined by the abstract rule of Table III in the given example. More specifically, the statement  2430  includes a specification  2432  of the conditional statement  1310 , as illustrated by an arrow  2442 , and a specification  2434  of the consequential statement  1710 , as illustrated by an arrow  2444 .  
      It should be noted that rule creation using the ABLE rule language is well-known in the art. Therefore, a more detailed description of the transformation of the abstract rule into the transformed rule is not necessary.  
     Managing Execution of an Analysis Routine  
      Referring now to  FIG. 25 , an exemplary method  2500  of managing execution of an analysis routine on a query result (e.g., query result  1910  of  FIG. 19 ) is illustrated. At least part of the steps of method  2500  are performed using a suitable user interface (e.g., user interface  210  of  FIG. 2 ). Method  2500  starts at step  2510 .  
      At step  2520 , a query (e.g., abstract query  260  of  FIG. 2 ) is executed against an underlying database (e.g., database  230  of  FIG. 2 ) to obtain a query result (e.g., result set  282  of  FIG. 2 ). By way of example, assume that a user such as a doctor in a medical institution creates a query to obtain age information for a given patient from a database of the medical institution. Accordingly, if the doctor identifies the given patient using an associated patient identifier such as “2”, and if the given patient is 30 years old, the exemplary query result of Table IV below can be obtained.  
               TABLE IV                          QUERY RESULT EXAMPLE                                 001   ID   Age                       002   2   30                      
 
      Illustratively, the exemplary query result shown in Table IV comprises identifier (“ID”) and age (“Age”) information for the given patient in line 002.  
      At step  2530 , user-selection of an analysis routine for execution on the query result is received. For simplicity, assume that the analysis routine is selected as described above with reference to  FIG. 19 . In other words, in the given example the selected analysis routine is defined by the abstract rule set “My RuleSet” which is defined by the abstract rule of Table III above.  
      At step  2540 , the selected analysis routine is executed on the query result. In other words, in the given example the abstract rule of Table III is executed on the query result of Table IV. An exemplary method for managing execution of the selected analysis routine on the query result is described in more detail below with reference to  FIG. 26 . Method  2500  then exits at step  2550 .  
      Referring now to  FIG. 26 , an exemplary method  2600  of managing execution of a selected analysis routine is illustrated. By way of example, method  2600  is explained with reference to the given example, in which the abstract rule of Table III is executed on the query result of Table IV.  
      Method  2600  starts at step  2610 , where all required inputs are identified for each abstract rule defining the selected analysis routine. As was noted above, in the given example required inputs to the abstract rule of Table III are field values for the condition fields “Age” (line 002 of Table III) and “Endothlin — 1” (line 003 of Table III).  
      At step  2620 , it is determined whether the query result includes result data for all required inputs. If the query result includes result data for all required inputs, corresponding field values are determined from the result data for each required input. Processing then continues at step  2660 . If, however, the query result does not include result data for each required input, step  2630  is performed for all field values included with the result data which can be used as inputs to the abstract rule and processing then continues at step  2640 . In the given example, only the field value “30” for the “Age” field according to line 002 of the exemplary query result of Table IV can be determined as input to the “Age” condition field of the abstract rule of Table III (line 002) at step  2630 , before processing continues at step  2640 .  
      At step  2640 , suitable data is retrieved for inputs of the abstract rule for which no field values can be determined from the query result. In the given example, the exemplary query result of Table IV does not include a field value which is suitable as input to the condition field “Endothlin — 1” of the abstract rule of Table III (line 003). In one embodiment, this field value can be determined by issuing a suitable query against the underlying database. In the given example, a query which requests for “Endothlin — 1” gene expression values for the given patient having the identifier “2” can be generated and issued against the database of the medical institution. Thus, suitable data having at least one data value which can be used as field value defining the input to the “Endothlin — 1” condition field of the abstract rule of Table III (line 003) can be retrieved.  
      At step  2650 , the field value defining the input to the “Endothlin — 1” condition field is determined from the suitable data. Assume now, that the suitable data only includes a single data value “Down”, which is thus determined as the field value. Processing then continues at step  2660 . However, it should be noted that the suitable data may include a multiplicity of data values which may potentially be used as the field value defining the input. Accordingly, in one embodiment the user, i.e., in the given example the doctor can be prompted to select one of the multiplicity of data values as the field value. In another embodiment, a point in time of current execution of the analysis routine can be identified. Then, a data value of the multiplicity of data values that was obtained at a point in time being the closest point in time before the identified point in time can be identified as the field value defining the required input. Still another embodiment is described below with reference to  FIGS. 27-28 . All such implementations are broadly contemplated.  
      At step  2660 , the selected analysis routine is run on all determined field values which define the required inputs to the analysis routine. In the given example, the abstract rule of Table III is run on the field value “30” for the “Age” condition field and the field value “Down” for the “Endothlin — 1” condition field as described above. Processing then returns to step  2550  of  FIG. 25 .  
     Validating Inputs to an Analysis Routine  
      Referring now to  FIG. 27 , an exemplary method  2700  of managing execution of a selected analysis routine on one or more inputs is illustrated. The method  2700  is configured to validate at least one of the one or more inputs as valid input(s) to the selected analysis routine.  
      In one embodiment, the method  2700  is performed when the pushbutton  1940  “Run” in the exemplary GUI screen  1900  of  FIG. 19  is clicked as described above. In this case, the one or more inputs are defined by the query result  1910  of  FIG. 19  and the selected analysis routine is defined by the abstract rule of Table III. In the following, the method  2700  is exemplified with respect to this example. However, it should be noted that the one or more inputs can be any suitable inputs, such as inputs provided by a user using the user interface  210  of  FIG. 2 . All such implementations are broadly contemplated. Method  2700  starts at step  2710 .  
      At step  2720 , the selected analysis routine which is configured to process the one or more inputs is accessed. In the given example, the abstract rule of Table III is retrieved, e.g., from an underlying database (e.g., database  230  of  FIG. 2 ), and accessed.  
      At step  2730 , a predefined validating condition that needs to be satisfied by at least one of the one or more inputs is determined from the analysis routine. In one embodiment, the predefined validating condition is determined from at least one abstract rule of an abstract rule set defining the selected analysis routine. For instance, assume that in the given example the abstract rule of Table III includes a predefined validating condition on the “Endothlin — 1” condition field (line 003 of Table III). Assume further that the predefined validating condition requests that only a most recent field value of an available series of field values should be used as input to the selected analysis routine with respect to the “Endothlin — 1” condition field. Alternatively, the predefined validating condition may request that the field value which is used as the input should be included within a predefined valid value range. All such embodiments are broadly contemplated.  
      At step  2740 , a particular data value defining the at least one of the one or more inputs is validated using the predefined validating condition. In the given example, assume that the particular data value is the field value  1924  of  FIG. 19 , i.e., the value “down” which is determined by the query result  1910  as input to the selected analysis routine with respect to the “Endothlin — 1” condition field in line 003 of Table III. An exemplary method of validating a data value using a predefined validating condition is described in more detail below with reference to  FIG. 28 .  
      At step  2750 , it is determined whether the particular data value was validated using the predefined validating condition, i.e., whether the particular data value is a valid value. If the particular data value is a valid value, processing continues at step  2760 , where the selected analysis routine is executed on the one or more inputs. For instance, assume that in the given example the predefined validating condition is configured to validate the particular data value only if the value is a most recent value. Accordingly, if the particular data value “Down” is the most recent value, the abstract rule of Table III is executed on the query result  1910  of  FIG. 19 . Executing the abstract rule of Table III on the query result  1910  of  FIG. 19  is described in above. Method  2700  then exits at step  2770 .  
      If, however, the particular data value is not a valid value, processing continues at step  2780 . By way of example, assume that in the given example the most recent value which can be retrieved with respect to the “Endothlin — 1” condition field is “Up”. Accordingly, the particular data value “Down” is an invalid value.  
      At step  2780 , a predefined action is performed that is configured to prevent execution of the selected analysis routine on an input that is defined by the invalid value “Down”. By way of example, the predefined action includes (i) disabling execution of the selected analysis routine on the invalid value; (ii) issuing a notification indicating that the particular data value is invalid, and (iii) replacing the particular data value with a valid data value that satisfies the predefined validating condition, as explained in more detail below with reference to  FIG. 28 . Method  2700  then exits at step  2770 .  
      Referring now to  FIG. 28 , an exemplary method  2800  of validating the particular data value using the predefined validating condition according to step  2740  of the method  2700  of  FIG. 27  is illustrated. By way of example, method  2700  is explained with reference to the given example, in which the predefined validating condition is configured to validate the particular data value “down” as input to the selected analysis routine with respect to the “Endothlin — 1” condition field only if the value is a most recent value.  
      Method  2800  starts at step  2810 , where a query (e.g., abstract query  260  of  FIG. 2 ) against an underlying database (e.g., database  230  of  FIG. 2 ) is generated. In one embodiment, the generated query includes a specific result field that corresponds to the condition field that is referenced by the predefined validating condition. The query is configured to retrieve a most recent value for the specific result field. Accordingly, in the given example the query is configured to retrieve a most recent value with respect to an “Endothlin — 1” result field. In one embodiment, timeline type metadata such as described in commonly owned U.S. patent application Ser. No. 11/035,710, filed Jan. 14, 2005 entitled, “TIMELINE CONDITION SUPPORT FOR AN ABSTRACT DATABASE”, which is also incorporated herein by reference in its entirety, can be used to retrieve the most recent value for the specific result field.  
      At step  2820 , the generated query is executed against the underlying database to retrieve the most recent value. Assume now, that in the given example the value “up” is retrieved as the most recent value for the “Endothlin — 1” result field, as described above.  
      At step  2830 , the retrieved most recent value is compared to the particular data value in order to determine whether the particular data value is the most recent one. If the particular data value is the most recent value, the particular data value is validated at step  2840  and processing continues at step  2750  of the method  2700  of  FIG. 27 . If, however, the particular data value is not the most recent value, processing continues at step  2850 .  
      At step  2850 , the particular data value is replaced with the most recent value and validated. However, other operations can be performed instead of or together with replacing the particular data value with the most recent value. For instance, the user can be prompted to indicate whether the particular data value should be replaced. Furthermore, the user can be notified that the particular data value is not valid without replacing the particular data value. All such implementations are broadly contemplated. Processing then continues at step  2750  of the method  2700  of  FIG. 27 .  
      While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.