Patent Publication Number: US-7584178-B2

Title: Query condition building using predefined query objects

Description:
CROSS-RELATED APPLICATION 
   This application is related to the following commonly owned application: US Patent: U.S. Pat. No. 6,996,558, issued Feb. 7, 2006, entitled “APPLICATION PORTABILITY AND EXTENSIBILITY THROUGH DATABASE SCHEMA AND QUERY ABSTRACTION”, which is hereby incorporated herein in its entirety. 
   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   The present invention generally relates to query processing and, more particularly, to creating query conditions for queries against data in a database using predefined query objects. 
   2. Description of the Related Art 
   Databases are computerized information storage and retrieval systems. 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 database management system (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. 
   Any requesting entity, including applications, operating systems and, at the highest level, users, can issue queries against data in a database to obtain required information. Queries may be predefined (i.e., hard coded as part of an application) or generated in response to input (e.g., user input). Queries may include both an explicit specification of result fields for which data is to be returned upon execution of the queries, and criteria used for selection of the data. The data selection criteria are generally represented as query conditions that serve to filter the data returned for the result fields upon execution of the query. Accordingly, a query may be thought of as group of filters put together to sift out only the result field data of interest. 
   One common technique in query condition building consists in defining subqueries that are configured to define suitable data selection criteria. More specifically, for a given outer query an inner query, or subquery, can be defined such that a corresponding result set of the subquery is suitable to filter the data returned for the result fields of the outer query. In SQL, this can be performed using a so-called IN condition that links a field of a query condition of the given outer query to the subquery. Thus, by executing the subquery valid values for the field of the query condition can be identified. Such a subquery is particularly useful in cases where the valid values need to be retrieved from a different database table than the data that is to be returned for the result field(s) of the given outer query. 
   However, several difficulties occur in creating and using subqueries as query conditions for SQL queries against underlying databases. First, the users generally need some knowledge of the layout of an underlying database and of SQL to be able to accurately create a subquery for a given outer SQL query. This can be difficult and is error-prone if the outer SQL query and/or the subquery are complex. Furthermore, while a data type check may be performed on each query condition of the outer SQL query, it is not determined whether the values included with a retrieved query result for the subquery are suitable for an associated field of the outer SQL query. For instance, assume that a “patient_id” field of a query condition of a given outer query requires integer values and that an associated subquery returns integer values for a “weight” field. In this case, the data types of the “patient_id” field and the “weight” field are compatible and the outer SQL query is validated. However, a result set obtained in executing the outer SQL query may not be useful as the weight values may not be suitable for the “patient_id” field. Thus, merely confirming that the data type of the returned result set (i.e., weight values, which are integer values) corresponds to the data type of the outer query does not ensure that the returned values are, in fact, the values needed (since weight values are not patient IDs). Thus, mere data type checking is inadequate. Moreover, the subquery must return values only for a result field that matches the field of the query condition to which the subquery is linked. For instance, assume that the subquery returns values for the “patient_id” field and for another result field such as a “LastName” result field or the “weight” field as described above. In this case, the subquery would return too many output values and therefore the outer SQL query would result in an error when being executed. 
   Therefore, there is a need for an efficient technique for processing queries that include subqueries. 
   SUMMARY OF THE INVENTION 
   The present invention is generally directed to a method, system and article of manufacture for query processing and, more particularly, for managing execution of a query having a query condition that is defined using a predefined query object against data in a database. 
   One embodiment provides a computer-implemented method of managing execution of a query against data in a database. The method comprises receiving a first query having (i) at least one result field configured to return data from at least one data record included with the database, and (ii) a query condition comprising a field and a query object associated with the field by an operator configured to select values for the field from the query object. The method further comprises determining whether the query object is configured to provide one or more valid values for the field. If the query object is not configured to provide one or more valid values for the field, the query object is transformed into a transformed query object that is configured to provide one or more valid values for the field. Then, the first query is executed against the database, the first query including the transformed query object if the transforming was performed. 
   Another embodiment provides a computer-readable medium containing a program which, when executed by a processor, performs operations for managing execution of a query against data in a database. The operations comprise receiving a first query having (i) at least one result field configured to return data from at least one data record included with the database, and (ii) a query condition comprising a field and a query object associated with the field by an operator configured to select values for the field from the query object. The operations further comprise determining whether the query object is configured to provide one or more valid values for the field. If the query object is not configured to provide one or more valid values for the field, the query object is transformed into a transformed query object that is configured to provide one or more valid values for the field. Then, the first query is executed against the database, the first query including the transformed query object if the transforming was performed. 

   
     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 a computer system illustratively utilized in accordance with the invention; 
       FIGS. 2-3  are relational views of software components in one embodiment; 
       FIGS. 4-5  are flow charts illustrating the operation of a runtime component; 
       FIG. 6  is a relational view of software components in one embodiment; 
       FIG. 7  is a flow chart illustrating query execution management in one embodiment; 
       FIG. 8  is a flow chart illustrating subquery processing in one embodiment; and 
       FIG. 9  is a flow chart illustrating query result processing in one embodiment. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   Introduction 
   The present invention is generally directed to a method, system and article of manufacture for query processing and, more particularly, for managing execution of a query against data in a database. In general, queries are executed against one or more underlying databases, each having a multiplicity of data records. Each query includes at least one result field for which data from the underlying database(s) is to be returned in a corresponding result set. A query may further include one or more query conditions for filtering which of the data records contained in the underlying database(s) are returned for each result field. 
   In one embodiment, a given query includes at least one query condition having a field and a query object. The query object is associated with the field by an operator configured to select values for the field from the query object. By way of example, the at least one query condition is a so-called IN condition that is defined using SQL. 
   According to one aspect, different types of query objects such as subqueries or predefined data sets can be associated with the field of the query condition. A subquery can be created by a user using a suitable query building user interface. Alternatively, the subquery can be retrieved from a repository of persistently stored queries. A predefined data set can be retrieved from a repository of persistently stored data sets that may include one or more query results. 
   In one embodiment, it is determined which type of query object is included with the given query. Then, it is determined whether the query object is configured to provide one or more valid values for the field of the at least one query condition. For instance, if the field of the at least one query condition is a “patient_id” field that requires integer values and if the query object is a subquery, it is determined whether the subquery returns integer values that are suitable for the “patient_id” field. If the query object is not configured to provide one or more valid values for the field, the query object is transformed on the basis of the determined type of the query object into a transformed query object that is configured to provide one or more valid values for the field. For instance, if the subquery in the given example returns character values for a “LastName” field, the subquery is modified to return suitable integer values for the “patient_id” field. Then, the query is executed against the underlying database(s). 
   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. 
   Logical/Runtime View of Environment 
     FIGS. 2-3  show an illustrative relational view of software components in one embodiment. According to one aspect, the software components are configured for query execution management and illustratively include one or more applications  120 , a data abstraction model  132  and a database  214 . By way of example, the database  214  includes a plurality of exemplary physical data representations  214   1 ,  214   2 , . . .  214   N . 
   The application(s)  120  is configured to issue queries against the database  214 . However, it should be noted that any suitable requesting entity including an operating system and, at the highest level, users may issue queries against the database  214 . Accordingly, all such different implementations are broadly contemplated. 
   The queries issued by the application(s)  120  may be predefined (i.e., hard coded as part of the application(s)  120 ) or may be generated in response to input (e.g., user input). In one embodiment, the application(s)  120  issues a query  202  as defined by a corresponding application query specification  122 . The resulting query  202  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 underlying physical data entities in the database  214 . The logical fields are defined by the data abstraction model  132  which generally exposes information as a set of logical fields that may be used within a query (e.g., the abstract query  202 ) issued by the application(s)  120  to specify criteria for data selection and specify the form of result data returned from a query operation. In one embodiment, the application query specification  122  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. 3 . 
   The logical fields of the data abstraction model  132  are defined independently of the underlying data representation (i.e., one of the plurality of exemplary physical data representations  214   1-N ) being used in the database  214 , thereby allowing queries to be formed that are loosely coupled to the underlying data representation. More specifically, a logical field defines an abstract view of data whether as an individual data item or a data structure in the form of, for example, a database table. As a result, abstract queries such as the query  202  may be defined that are independent of the particular underlying data representation used. Such abstract queries can be transformed into a form consistent with the underlying physical data representation  214   1-N  for execution against the database  214 . By way of example, the abstract query  202  is translated by a runtime component  150  into a concrete (i.e., executable) query which is executed against the database  214  to determine a corresponding result set for the abstract query  202 . 
   In one embodiment, illustrated in  FIG. 3 , the data abstraction model  132  comprises a plurality of field specifications  308   1 ,  308   2 ,  308   3 ,  308   4  and  308   5  (five 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  (collectively, field name  320 ) and an associated access method attribute  322   1 ,  322   2 ,  322   3 ,  322   4 ,  322   5  (collectively, access methods  322 ). Each attribute may have a value. For example, logical field name attribute  320   1  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”. 
   In one embodiment, groups (i.e. two or more) of logical fields may be part of categories. Accordingly, the data abstraction model  132  includes a plurality of category specifications  310   1  and  310   2  (two 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. 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  and  330   2  (collectively, category name(s)  330 ). In the present illustration, the logical fields  308   1-3  are part of the “Name and Address” category and logical fields  308   4-5  are part of the “Birth and Age” category. 
   The access methods  322  generally associate (i.e., map) the logical field names to data in the database (e.g., database  214  of  FIG. 2 ). As illustrated in  FIG. 2 , the access methods associate the logical field names to a particular physical data representation  214   1-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  132  contains field specifications (with associated access methods) for two or more physical data representations  214   1-N . In an alternative embodiment, a different single data abstraction model  132  is provided for each separate physical data representation  214   1-N . 
   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  and  308   5  exemplify simple field access methods  322   1 ,  322   2 , and  322   5 , 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   1  shown in  FIG. 3  maps the logical field name  320   1  (“FirstName”) to a column named “f_name” in a table named “contact”. The field specification  308   3  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. 3  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 “l_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  308   4  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. 3  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. 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  132  shown in  FIG. 3  are representative of logical fields mapped to data represented in the relational data representation  214   2  shown in  FIG. 2 . However, other instances of the data abstraction model  132  map logical fields to other physical representations, such as XML. 
   An illustrative abstract query corresponding to the abstract query  202  shown in  FIG. 3  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=“AnyTownLastName”/&gt; 
             
             
                 
               012    &lt;Field name=“Street”/&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, result specification is a list of abstract fields that are to be returned as a result of query execution. A result 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  132  shown in  FIG. 3  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=“Name and Address”&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        Filter=“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” displayable=“Yes”&gt; 
             
             
               029     &lt;AccessMethod&gt; 
             
             
               030       &lt;Simple columnName=“age” tableName=“contact”&gt;&lt;/Simple&gt; 
             
             
               031     &lt;/AccessMethod&gt; 
             
             
               032   &lt;/Field&gt; 
             
             
               033   &lt;/Category&gt; 
             
             
               034  &lt;/DataAbstraction&gt; 
             
             
                 
             
          
         
       
     
   
   By way of example, note that lines 004-008 correspond to the first field specification  308   1  of the DAM  132  shown in  FIG. 3  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. 4-5 . 
   Transforming an Abstract Query into a Concrete Query 
   Referring now to  FIG. 4 , an illustrative runtime method  400  exemplifying one embodiment of transforming an abstract query (e.g., abstract query  202  of  FIGS. 2-3 ) into a concrete query using the runtime component  150  of  FIG. 2  is shown. The method  400  is entered at step  402  when the runtime component  150  receives the abstract query (such as the abstract query shown in Table I) as input. At step  404 , the runtime component  150  reads and parses the abstract query and locates individual selection criteria and desired result fields. At step  406 , the runtime component  150  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  150  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  132 . 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  150  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  214  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  150  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  150  looks up a result field name (from the result specification of the abstract query) in the data abstraction model  132  and then retrieves a result field definition from the data abstraction model  132  to identify the physical location of data to be returned for the current logical result field. The runtime component  150  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. 5 . 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 then 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. 
   An Exemplary Query Creation and Execution Environment 
   Referring now to  FIG. 6 , a relational view of software components in one embodiment is illustrated. The software components illustratively include a user interface  610 , a DBMS  650 , one or more applications  620  (only one application is illustrated for simplicity) and an abstract model interface  630 . The abstract model interface  630  illustratively provides an interface to a data abstraction model  632  (e.g., data abstraction model  132  of  FIG. 2 ) and a runtime component  634  (e.g., runtime component  150  of  FIG. 2 ). The DBMS  650  illustratively includes a database  652  (e.g., database  214  of  FIG. 2 ) having one or more database tables  655 , and a query execution unit  654  having a query engine  656  and a query rewriter  658 . 
   According to one aspect, the application  620  (and more generally, any requesting entity including, at the highest level, users) issues queries against the database  652 . The database  652  is shown as a single database for simplicity. However, a given query can be executed against 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  652  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  652  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 data. 
   In one embodiment, the queries issued by the application  620  are created by users using the user interface  610 , which can be any suitable user interface configured to create/submit queries. According to one aspect, the user interface  610  is a graphical user interface. However, it should be noted that the user interface  610  is only shown by way of example; any suitable requesting entity may create and submit queries against the database  652  (e.g., the application  620 , 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  652 . 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  652 , 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  650 ) to support JDBC standard access between the database  652  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  620 ). 
   Accordingly, the queries issued by the application  620  can be in physical form, such as SQL and/or XML queries, which are consistent with the physical representation of the data in the database  652 . Alternatively, the queries issued by the application  620  are composed using the abstract model interface  630 . In other words, the queries are created on the basis of logical fields defined by the data abstraction model  632  and translated by the runtime component  634  into concrete (i.e., executable) queries for execution. As was noted above, such queries are referred to herein as “abstract queries”. An exemplary abstract model interface is described above with reference to  FIGS. 2-5 . 
   Illustratively, the application  620  issues a query  640  against the database  652 , as illustrated by a dashed arrow  692 . In one embodiment, the query  640  is specified by a user using the user interface  610 . The query  640  includes at least one result field  642  for which data from the database  652  is to be returned, and one or more query conditions  644 . The query conditions  644  are configured for filtering which data record(s) contained in the database  652  is(are) returned for each of the result fields  642 . At least one of the query conditions  644  includes a field  645  that is associated with a query object  649  using an operator  647  which is configured to select values for the field  645  from the query object  649 . By way of example, the field  645  can be a column of a database table, such as the table  655 , or a logical field of an underlying data abstraction model, such as the data abstraction model  632 . The query object  649  is an object that is usable to determine values for the field  645 . 
   More specifically, in one embodiment the query object  649  is a subquery of the query  640  that is either created upon specification of the query  640  or retrieved from a query repository  682  having one or more predefined queries, as indicated by a dashed arrow  696 . By way of example, a user may use the user interface  610  to create the subquery using a corresponding query building application. Alternatively, the user interface  610  may display a plurality of predefined queries of the query repository  682  to the user in order to allow user selection of the subquery. In both cases, the user interface  610  can be configured to allow only specification or selection of a suitable query object  649 . 
   The subquery may include a single result field that matches the field  645 . In this case, the query  640  can be executed using techniques that are well-known in the art and, therefore, not explained in more detail. However, in one embodiment the subquery includes a plurality of result fields that either include a particular result field that matches the field  645 , or not. If no matching result field is included with the subquery, the subquery may still access the same database table as the query  640 , i.e., table  655 , as indicated by a dashed arrow  698 . In these cases, the subquery can be processed such that it returns only valid values for the field  645 . An exemplary method of processing a subquery in one embodiment is described below with reference to  FIG. 8 . 
   Furthermore, in one embodiment the query object  649  can be defined using a predefined data set. For instance, the query object  649  can be defined by a query result included with a result repository  684 , as illustrated by a dashed arrow  694 . In these cases, the predefined data set can be processed in order to determine the valid values for the field  645  therefrom. An exemplary method of processing a predefined data set in one embodiment is described below with reference to  FIG. 9 . 
   In one embodiment, processing a subquery or a predefined data set that defines the query object  649  includes determining whether the query object  649  is configured to provide one or more valid values for the field  645 . Exemplary methods for determining whether the query object  649  is configured to provide the valid value(s) are described by way of example below with reference to  FIGS. 8-9 . If so, the query object is transformed into a transformed query object, whereby the query object  649  is rewritten using the query rewriter  658  such that is returns valid values for the field  645 . Thereby a rewritten query  686  is created that can be executed against the database  652 . Operation of the query rewriter  658  is described in more detail by way of example with reference to  FIGS. 8-9  below. 
   However, it should be noted that the query rewriter  658  is merely described by way of example to illustrate a component which is suitable to implement aspects of the invention. In other words, the functions of the query rewriter  658  can be implemented into other functional components. For instance, in one embodiment the functions of the query rewriter  658  are implemented by the query engine  656  or a component which is implemented separate from the query execution unit  654 . All such implementations are broadly contemplated. 
   The rewritten query  686  is executed by the query execution unit  654  against the database  652  using the query engine  656  to determine a query result  670 . It should be noted that the query execution unit  654  illustratively only includes the query engine  656  and the query rewriter  658 , for simplicity. However, the query execution unit  654  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)  620 , and then parse the received query. In one embodiment, the query parser may be configured to identify the type of the query object  649  and then forward the parsed query  640  to the query rewriter  658  for rewriting the query  640 , if required. The query parser may then parse the rewritten query  686  and forward the parsed rewritten 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  652 ), 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. Once an access plan is selected, the query engine  656  then executes the rewritten query  686  according to the selected access plan. 
   When executing the rewritten query  686  against the database  652 , the query engine  656  identifies each data record of the database  652  that satisfies the query condition(s)  644  to identify the result set  670  for the query  640 . In one embodiment, the result set  670  is persistently stored for subsequent retrieval in the result repository  684 . The result set  670  is then returned from the query execution unit  654  to the application  620 . Operation of the query execution unit  654  is described in more detail below with reference to  FIG. 7 . 
   Managing Query Execution 
   Referring now to  FIG. 7 , one embodiment of a method  700  for managing execution of a query (e.g., query  640  of  FIG. 6 ) having a query condition with a query object is illustrated. In one embodiment, at least part of the steps of the method  700  is performed by the query execution unit  654  of  FIG. 6 . Furthermore, at least several steps of the method  700  can be performed on the basis of user input received via the user interface  610  of  FIG. 6 . Method  700  starts at step  710 . 
   At step  720 , a query against an underlying database (e.g., database  652  of  FIG. 6 ) is received from a requesting entity (e.g., application  620  of  FIG. 6 ). The query includes at least one result field (e.g., result fields  642  of  FIG. 6 ) and one or more query conditions (e.g., query condition  644  of  FIG. 6 ). At least one query condition includes a field (e.g., field  645  of  FIG. 6 ) and a query object (e.g., query object  649  of  FIG. 6 ) associated with the field by an operator (e.g., operator  647  of  FIG. 6 ) configured to select values for the field from the query object. 
   At step  730 , it is determined whether the query object is a subquery. For purposes of illustration, assume that the received query was composed by a user using the user interface  610  of  FIG. 6 . For instance, assume a researcher who wants to perform a study concerning employees of a hospital that have been treated by medical staff of the hospital in order to determine whether the treated employees received better care than customers. To this end, the researcher wants to determine whether specific types of expensive treatments or diagnosis tests that are suitable to detect certain diseases are more frequently performed on employees. Illustratively, an exemplary disease is pancreatic cancer and exemplary expensive treatments therefore are chemotherapy and node removal surgery. Accordingly, the researcher defines the query in order to retrieve information for patients that have had a pancreatic cancer diagnosis, node removal surgery and chemotherapy. The researcher further defines the subquery such that it restricts the retrieved information to information for patients that are also employees of the hospital. 
   An exemplary query is illustrated in Table III below. By way of example, the query of Table III below is defined using SQL. However, note that the exemplary SQL query of Table III has been simplified such that the relevant parts thereof are not obscured by irrelevant code language. Furthermore, persons skilled in the art will readily recognize complete SQL and/or corresponding XML representations, such as used to describe the exemplary abstract query of Table I. Accordingly, it should be noted that implementation of the exemplary query of Table III is not limited to a particular machine-readable language and that an implementation in any machine-readable language, known or unknown, is broadly contemplated. 
   
     
       
         
             
           
             
               TABLE III 
             
             
                 
             
             
               SQL QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
          
             
               001  Select Patient_ID, Diagnosis, treatment_option 
             
             
               002  From Patient_Table &lt;joined to&gt; Treatment_Table &lt;joined to&gt; 
             
             
                   Treatment_Table 
             
             
               003  Where 
             
             
               004   diagnosis = ‘pancreatic cancer’ 
             
             
               005   and treatment_option = ‘node removal surgery’ 
             
             
               006   and treatment_option = ‘chemotherapy’ 
             
             
               007   and patient_id IN 
             
             
               008     Select * 
             
             
               009     From Patient_Table &lt;joined to&gt; Employee_Table 
             
             
               010     Where 
             
             
               011      Patient_ID exists 
             
             
               012      and (Employee_Job_Class == ‘Doctor’ 
             
             
               013        or Employee_Job_Class == ‘Administrator’) 
             
             
               014      and Hire_Date &lt; 1/1/2002 
             
             
                 
             
          
         
       
     
   
   The exemplary SQL query of Table III is configured to retrieve the required information for particular patients from a database table “Treatment_Table” (line 002). The particular patients, which are also employees of the hospital, are identified using an IN condition (lines 007-014) that links an illustrative subquery (lines 008-014) to a field “patient_id” of one (lines 007-014) of the query conditions defined in lines 003-014 using the SQL operator “IN” (line 007). The subquery is configured to retrieve values for any field (line 008) included with a database table “Employee_Table” (line 009) having data for each employee of the hospital. Note that in the given example the “Patient_Table” (line 002) is merely used to provide the “patient_id” field that is used to join the different tables. However, as SQL is well-known in the art, the exemplary query of Table III is readily understood by persons skilled in the art and is, therefore, not explained in more detail. 
   In the given example, it is determined at step  730  that the query object is a subquery that includes a multiplicity of result fields (“Select *” in line 008 of Table III). Accordingly, processing continues at step  740 , where the subquery is processed such that it returns valid values only for a single result field that matches the field of the at least one query condition, i.e., the “patient_id” field (line 007). An exemplary method of processing a subquery is described below with reference to  FIG. 8 . In one embodiment, processing the subquery includes rewriting the received query in order to obtain a rewritten query (e.g., rewritten query  686  of  FIG. 6 ) that is executable against the underlying database. The method  700  then proceeds with step  780 . 
   If, however, it is determined at step  730  that the query object is not a subquery, processing continues at step  750 . At step  750 , it is determined whether the query object specifies a reference to a persistently stored query (e.g., a query included with query repository  682  of  FIG. 6 ). If so, the stored query is retrieved and included as a subquery with the received query at step  752 . For instance, assume that in the given example the exemplary query illustrated in Table IV below is received at step  720 . By way of example, the query of Table IV below is also defined using SQL and simplified as described above with reference to Table III. 
   
     
       
         
             
           
             
               TABLE IV 
             
             
                 
             
             
               SQL QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
          
             
               001  Select Patient_ID, Diagnosis, treatment_option 
             
             
               002  From Patient_Table &lt;joined to&gt; Treatment_Table &lt;joined to&gt; 
             
             
                   Treatment_Table 
             
             
               003  Where 
             
             
               004   diagnosis = ‘pancreatic cancer’ 
             
             
               005   and treatment_option = ‘node removal surgery’ 
             
             
               006   and treatment_option = ‘chemotherapy’ 
             
             
               007   and patient_id IN 
             
             
               008     (saved query “Research Candidate List”) 
             
             
                 
             
          
         
       
     
   
   It should be noted that lines 001-007 of the exemplary query of Table IV correspond to lines 001-007 of Table III above. However, instead of an explicit subquery as included with lines 008-014 of Table III above, the exemplary query of Table IV only includes a reference to a persistently stored query (“saved query”) referred to as “Research Candidate List” in line 008. 
   Assume now that the “Research Candidate List” query is defined by the exemplary query illustrated in Table V below. By way of example, the query of Table V below is also defined using SQL and simplified as described above with reference to Table III. 
   
     
       
         
             
           
             
               TABLE V 
             
             
                 
             
             
               SAVED QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
             
          
             
                 
               001  Select LastName, FirstName, Address 
             
             
                 
               002  From Patient_Table &lt;joined to&gt; Employee_Table 
             
             
                 
               003  Where 
             
             
                 
               004   Patient_ID exists 
             
             
                 
               005   and (Employee_Job_Class == ‘Doctor’ 
             
             
                 
               006     or Employee_Job_Class == ‘Administrator’) 
             
             
                 
               007   and Hire_Date &lt; 1/1/2002 
             
             
                 
                 
             
          
         
       
     
   
   It should be noted that the exemplary query of Table V essentially corresponds to the subquery defined in lines 008-014 of Table III. However, instead of having any field of the “Employee_Table” as result field (“Select *” in line 008 of Table III), the exemplary query of Table V is configured to retrieve only values for a “LastName”, “FirstName” and “Address” field (line 007 of Table V). 
   If, at step  752 , the “Research Candidate List” query is retrieved and included with the exemplary query of Table IV above, the query illustrated in Table VI below is obtained. 
   
     
       
         
             
           
             
               TABLE VI 
             
             
                 
             
             
               SQL QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
          
             
               001  Select Patient_ID, Diagnosis, treatment_option 
             
             
               002  From Patient_Table &lt;joined to&gt; Treatment_Table &lt;joined to&gt; 
             
             
                   Treatment_Table 
             
             
               003  Where 
             
             
               004   diagnosis = ‘pancreatic cancer’ 
             
             
               005   and treatment_option = ‘node removal surgery’ 
             
             
               006   and treatment_option = ‘chemotherapy’ 
             
             
               007   and patient_id IN 
             
             
               008     Select LastName, FirstName, Address 
             
             
               009     From Patient_Table &lt;joined to&gt; Employee_Table 
             
             
               010     Where 
             
             
               011      Patient_ID exists 
             
             
               012      and (Employee_Job_Class == ‘Doctor’ 
             
             
               013        or Employee_Job_Class == ‘Administrator’) 
             
             
               014      and Hire_Date &lt; 1/1/2002 
             
             
                 
             
          
         
       
     
   
   In the given example, the exemplary query of Table VI is then processed at step  740 , as described above. If, however, it is determined at step  750  that the query object is not a reference to a persistently stored query, processing continues at step  760 . 
   It should be noted that including the retrieved “Research Candidate List” query of Table V with the exemplary query of Table IV above is merely described by way of example. Alternatively, the “Research Candidate List” query of Table V can simply be joined to the exemplary query of Table IV. Thus, any subsequent changes to the “Research Candidate List” query of Table V are reflected in the exemplary query of Table IV. For instance, assume that the researcher stores the exemplary query of Table IV for future use. Assume further that the researcher then wants to limit the search only to doctors of the hospital and therefore removes the query condition “or Employee_Job_Class==‘Administrator’” (line 006 of Table V) from the “Research Candidate List” query of Table V. If the “Research Candidate List” query of Table V is only joined to the query of Table IV, the performed change is automatically reflected by the query of Table IV, as the query of Table IV re-accesses in this case the query of Table V for each execution. However, in the example described above where both queries were combined to the exemplary query of Table VI, the researcher would also need to change the exemplary combined query of Table VI by removing the query condition in line 013 thereof. All such implementations are broadly contemplated. 
   At step  760 , it is determined whether the query object specifies a reference to a predefined data set (e.g., a query result included with result repository  684  of  FIG. 6 ). For instance, assume that in the given example the exemplary query illustrated in Table VII below is received at step  720 . By way of example, the query of Table VII below is also defined using SQL and simplified as described above with reference to Table III. 
   
     
       
         
             
           
             
               TABLE VII 
             
             
                 
             
             
               SQL QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
          
             
               001  Select Patient_ID, Diagnosis, treatment_option 
             
             
               002  From Patient_Table &lt;joined to&gt; Treatment_Table &lt;joined to&gt; 
             
             
                   Treatment_Table 
             
             
               003  Where 
             
             
               004   diagnosis = ‘pancreatic cancer’ 
             
             
               005   and treatment_option = ‘node removal surgery’ 
             
             
               006   and treatment_option = ‘chemotherapy’ 
             
             
               007   and patient_id IN 
             
             
               008     (saved data set “Research Candidate List Output”) 
             
             
                 
             
          
         
       
     
   
   It should be noted that the exemplary query of Table VII corresponds to the exemplary query of Table IV above. However, instead of a reference to a persistently stored query as included with line 008 of Table IV above, the exemplary query of Table VII includes a reference to a persistently stored data set (“saved data set”) referred to as “Research Candidate List Output” in line 008 of Table VII. Assume now that the “Research Candidate List Output” data set is defined by the exemplary query result illustrated in Table VIII below. 
   
     
       
         
             
           
             
               TABLE VIII 
             
           
          
             
                 
             
             
               SAVED QUERY RESULT EXAMPLE 
             
          
         
         
             
             
             
             
             
             
          
             
               001 
               Patient_ID 
               Employee_ID 
               LastName 
               FirstName 
               Employee_Job_Class 
             
             
                 
             
          
         
         
             
             
             
             
             
             
          
             
               002 
               1 
               1001 
               Miller 
               John 
               Doctor 
             
             
               003 
               12 
               1003 
               Smith 
               Lea 
               Doctor 
             
             
               004 
               35 
               1017 
               Jackson 
               Fred 
               Administrator 
             
             
                 
             
          
         
       
     
   
   By way of example, the query result of Table VIII includes three exemplary data records (lines 002-004), each having a unique patient identifier “Patient_ID” associated with a unique employee identifier “Employee_ID” to uniquely identify the patients that are also employees of the hospital. Furthermore, each data record includes first and last names (“FirstName”, “LastName”) and a corresponding job class description (“Employee_Job_Class”) for each employee. It should be noted that the exemplary query result may include one or more additional fields, such as an “Address” or “Hire_Date” field. However, for simplicity such fields are not included with Table VIII. 
   If it is determined at step  760  that the query object is a reference to a persistently stored data set, processing continues at step  770 , where the data set is processed. Otherwise, processing continues at step  780 . An exemplary method of processing a predefined data set in one embodiment is described below with reference to  FIG. 9 . In one embodiment, processing the predefined data set includes rewriting the query that was received at step  720  in order to obtain a rewritten query (e.g., rewritten query  686  of  FIG. 6 ) that is executable against the underlying database. The method  700  then proceeds with step  780 . 
   At step  780 , the query is executed against the underlying database to obtain a corresponding result set (e.g., result set  670  of  FIG. 6 ). The obtained result set is output to the requesting entity at step  790 . Method  700  then exits at step  799 . 
   Processing a Subquery 
   Referring now to  FIG. 8 , one embodiment of a method  800  for processing a subquery according to step  740  of  FIG. 7  is illustrated. According to one aspect, the steps of the method  800  are performed by the query rewriter  658  of  FIG. 6 . In one embodiment, the method  800  is performed in order to determine whether the subquery is configured to return valid values for the field (e.g., field  645  of  FIG. 6 ) of the query condition (e.g., query condition  644  of  FIG. 6 ) that is associated therewith. The method  800  is further configured to perform suitable processing in order to ensure that the subquery returns the valid value(s), as described below by way of example. 
   Method  800  starts at step  810 , where it is determined whether the subquery includes one or more result fields. If so, processing continues at step  820 , where the one or more result fields are identified from the subquery. Otherwise, processing continues at step  860 . 
   After identification of the one or more result fields from the subquery at step  820 , it is determined at step  830  whether the identified result fields include a given result field that matches the field of the query condition. If so, processing proceeds with step  840 . Otherwise, processing proceeds with step  850 . 
   Assume now that the subquery corresponds to lines 008-014 of Table III above. In this case, all fields of the “Employee_Table” are result fields of the subquery (“Select *” in line 008 of Table III). Assume now that the “Employee_Table” includes a “patient_id” field. Accordingly, it is determined at step  830  that the subquery includes a given result field, i.e., the “patient_id” field that matches the “patient_id” field of the query condition (line 007 of Table III). Thus, processing continues at step  840 , where all non-matching result fields are removed from the subquery. In other words, the subquery is rewritten such that it only includes the “patient_id” field as result field. Thereby, the exemplary query of Table III is rewritten and the rewritten query (e.g., rewritten query  686  of  FIG. 6 ) illustrated in Table IX below is obtained. 
   
     
       
         
             
           
             
               TABLE IX 
             
             
                 
             
             
               REWRITTEN QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
          
             
               001  Select Patient_ID, Diagnosis, treatment_option 
             
             
               002  From Patient_Table &lt;joined to&gt; Treatment_Table &lt;joined to&gt; 
             
             
                   Treatment_Table 
             
             
               003  Where 
             
             
               004   diagnosis = ‘pancreatic cancer’ 
             
             
               005   and treatment_option = ‘node removal surgery’ 
             
             
               006   and treatment_option = ‘chemotherapy’ 
             
             
               007   and patient_id IN 
             
             
               008     Select patient_id 
             
             
               009     From Patient_Table &lt;joined to&gt; Employee_Table 
             
             
               010     Where 
             
             
               011      Patient_ID exists 
             
             
               012      and (Employee_Job_Class == ‘Doctor’ 
             
             
               013        or Employee_Job_Class == ‘Administrator’) 
             
             
               014      and Hire_Date &lt; 1/1/2002 
             
             
                 
             
          
         
       
     
   
   Note that the subquery in lines 008-014 of Table IX only includes a single result field “patient_id” (line 008) that matches the field “patient_id” of the query condition (line 007). Processing then continues at step  780  of  FIG. 7 . 
   Assume now that the subquery corresponds to lines 008-014 of Table VI above. In this case, the subquery includes the result fields “LastName”, “FirstName” and “Address”, as noted above. Accordingly, in this case it is determined at step  830  that the subquery does not include a given result field that matches the “patient_id” field of the query condition (line 007 of Table VI). Thus, processing continues at step  850 , where all result fields are removed from the subquery. Processing then proceeds with step  860  of  FIG. 8 . 
   At step  860 , it is determined whether the received query and the subquery access an identical database table or an identical table instance. In one embodiment, this includes determining whether the subquery accesses a table or table instance that has a field that matches the field of the query condition. If so, processing proceeds with step  870 . Otherwise, a notification is issued at step  890  indicating that the subquery cannot be processed and the method  800  then exits at step  895 . In one embodiment, issuing a notification includes prompting a user for further input. For instance, the user can be requested to modify the subquery such that it is suitable to determine valid values for the field of the query condition. 
   In the given example, it is determined at step  860  that the subquery accesses the “Employee_Table” (line 009 of Table VI) that includes a “patient_id” field, as was noted above. Accordingly, the “Employee_Table” is accessed at step  870  to identify the “patient_id” field therefrom. In one embodiment, identifying the “patient_id” field from the “Employee_Table” includes verifying whether this field and the “patient_id” field of the query condition (line 007 of Table VI) have an identical data type and purpose. For instance, corresponding metadata can be checked in order to determine whether both fields relate to unique patient identifiers. Thus, it can be avoided that the data types of both fields match, but that the fields themselves serve different purposes (e.g., a patient identifier field versus a weight field). Other verifications that are performed to determine the suitability of the “patient_id” field of the “Employee_Table” are also broadly contemplated. Furthermore, such verifications can also be performed in the context of step  830  described above. 
   At step  880 , the identified result field is included with the subquery. In the given example, the exemplary query of Table VI would thus also be rewritten to the rewritten query of Table IX above. Processing then continues at step  780  of  FIG. 7 . 
   Processing a Predefined Data Set 
   Referring now to  FIG. 9 , one embodiment of a method  900  for processing a predefined data set according to step  770  of  FIG. 7  is illustrated. According to one aspect, the steps of the method  900  are performed by the query rewriter  658  of  FIG. 6 . By way of example, the steps of the method  900  are explained with respect to a query result defining the predefined data set, such as a query result included with result repository  684  of  FIG. 6 . 
   Method  900  starts at step  910 , where it is determined whether the query result includes valid values for the field (e.g., field  645  of  FIG. 6 ) of the query condition (e.g., query condition  644  of  FIG. 6 ). In one embodiment, determining at step  910  whether the query result includes valid values for the field includes performing corresponding verifications to ensure that values included therewith are suitable, such as described above with reference to step  870  of  FIG. 8 . 
   If it is determined at step  910  that the query result includes valid values for the field, processing proceeds with step  920 . Otherwise, a notification is issued at step  940  indicating that the query result cannot be processed and the method  900  then exits at step  950 . In one embodiment, issuing a notification includes prompting a user for further input. For instance, the user can be requested to indicate another query result that it is suitable to determine valid values for the field of the query condition. 
   For instance, assume that the received query is defined by the exemplary query of Table VII above. Assume further that the query result is defined by the exemplary query result of Table VIII above, which includes a “Patient_ID” column having the patient identifier values “1”, “12” and “35” that are valid values for the field “patient_id” (line 007 of Table VII) of the query condition defined in lines 007-008 of Table VII. 
   At step  920 , the valid values are identified from the query result. At step  930 , the identified values are included with the query. Accordingly, in the given example the exemplary query of Table VII is rewritten at step  930  and the rewritten query (e.g., rewritten query  686  of  FIG. 6 ) illustrated in Table X below is obtained. 
   
     
       
         
             
           
             
               TABLE X 
             
             
                 
             
             
               REWRITTEN QUERY EXAMPLE 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
          
             
               001  Select Patient_ID, Diagnosis, treatment_option 
             
             
               002  From Patient_Table &lt;joined to&gt; Treatment_Table &lt;joined to&gt; 
             
             
                   Treatment_Table 
             
             
               003  Where 
             
             
               004   diagnosis = ‘pancreatic cancer’ 
             
             
               005   and treatment_option = ‘node removal surgery’ 
             
             
               006   and treatment_option = ‘chemotherapy’ 
             
             
               007   and patient_id IN 1, 12, 35 
             
             
                 
             
          
         
       
     
   
   It should be noted that the rewritten query of Table X explicitly includes all identified valid values for the “patient_id” field in line 007. Processing then continues at step  780  of  FIG. 7  where the rewritten query is executed to determine a corresponding result set (e.g., result set  670  of  FIG. 6 ). 
   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.