Abstract:
Provided are techniques for the manipulation of semantic objects within a semantic store, including a semantic reasoning apparatus comprising a processor; a non-transitory computer-readable storage medium; a semantic store comprising a plurality of semantic objects; a semantic model; a rule, comprising a condition part and an action part; wherein the rule is based upon the semantic model and configured to execute the action part in response to a determination that the condition part is satisfied by one or more objects of the plurality of semantic objects and a semantic driver that employs the semantic model as input for driving behavior, comprising logic for determining that the condition part is satisfied by the one or more objects of the plurality of objects; and modifying a semantic object of the plurality of semantic objects in conformity to the action part in response to the determining that the condition part is satisfied.

Description:
FIELD OF DISCLOSURE 
       [0001]    The claimed subject matter relates generally to semantic modeling and, more specifically, to techniques for the manipulation of semantic objects within a semantic store. 
       BACKGROUND OF THE INVENTION 
       [0002]    Current state of the art allows semantic modeling of a business domain and the conversion of such a semantic model to a data model that represents information about the domain. Rule engines can leverage semantic models as the foundation for verbalization of domain specific rules and can do rule inference on domain data loaded in the rule engine, based upon a domain data model and type system mapping. In addition, specialized inference engines, or semantic reasoners, can infer semantic facts from existing semantic statement. These capabilities provide a semantic foundation for reasoning on information and data stored in databases and processed in information systems. 
         [0003]    Beyond data stored in a database, semantic objects are themselves becoming increasingly important to business and information technology (IT) systems in that they capture operational business contexts and intentions in an actionable fashion. Historically, business entities and certain other aspects of operational business systems have only been captured in terms of the information and data they convey, thereby fitting the paradigm of rules and programs acting on data stored in databases. 
       SUMMARY 
       [0004]    As the inventors herein have realized, the conversion of domain objects to data stored in a database comes at the expense of a certain necessity of adaptation as well as a disassociation of business logic from the object to which it applies. For the purpose of operating, monitoring and managing operational business systems, business objects can and will change dynamically and independently of any action by a rule or inference engine. It should be noted that such changes can even include creation or deletion of semantic objects in their entirety. This exposes a gap in current rule engine capabilities; specifically, although current state of the art supports semantic reasoning on domain data, the state of the art does not enable rule engines to directly infer on and manipulate stored semantic objects in their current state in a persisted semantic store. In other words, current rule engines do not have the ability to dynamically interact with and directly manipulate objects in a semantic store, which implies not just mapping of the type system, but requires actually operating on the semantic store as the underlying “runtime” of the rule engine. 
         [0005]    Provided are techniques for the manipulation of semantic objects within a semantic store, including a semantic reasoning apparatus, comprising a processor; a non-transitory computer-readable storage medium (CRSM) coupled to the processor; a semantic store comprising a plurality of semantic objects; a semantic model; a rule, comprising; a condition part; and an action part; wherein the rule is based upon the semantic model and configured to execute the action part in response to a determination that the condition part is satisfied by one or more objects of the plurality of semantic objects; and a semantic driver, stored on the CRSM and executed on the processor, that employs the semantic model as input for driving behavior, comprising logic for determining that the condition part is satisfied by the one or more objects of the plurality of objects; and modifying a semantic object of the plurality of semantic objects in conformity to the action part in response to the determining that the condition part is satisfied. 
         [0006]    This summary is not intended as a comprehensive description of the claimed subject matter but, rather, is intended to provide a brief overview of some of the functionality associated therewith. Other systems, methods, functionality, features and advantages of the claimed subject matter will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    A better understanding of the claimed subject matter can be obtained when the following detailed description of the disclosed embodiments is considered in conjunction with the following figures. 
           [0008]      FIG. 1  is a block diagram of a computing system architecture that may support the claimed subject matter. 
           [0009]      FIG. 2  is a block diagram showing the relationships among various elements of a semantic system that may implement the claimed subject matter. 
           [0010]      FIG. 3  is a block diagram of a semantic driver, first introduced in  FIG. 1 , that may implement aspects of the claimed subject matter. 
           [0011]      FIG. 4  is a flowchart of an example of an “Author Semantic Object (SO) Rules” process that may implement aspects of the claimed subject matter. 
           [0012]      FIG. 5  is a flowchart of an example of an “Evaluate Rules on Semantic Objects (SOs)” process that may implement aspects of the claimed subject matter. 
           [0013]      FIG. 6  is a flowchart of an example of a “Derive New Data” process that may implement aspects of the claimed subject matter. 
           [0014]      FIG. 7  is a flowchart of an example of a “Generate Constraint Rules” process that may implement aspects of the claimed subject matter. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
         [0016]    Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection, having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
         [0017]    A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
         [0018]    Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
         [0019]    Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
         [0020]    Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0021]    These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0022]    The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational actions to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0023]    Turning now to the figures,  FIG. 1  is a block diagram of one example of a computing system architecture  100  on which the claimed subject matter may be implemented. A computing system  102  includes a central processing unit (CPU)  104 , coupled to a monitor  106 , a keyboard  108  and a pointing device, or “mouse,”  110 , which together facilitate human interaction with computing system  100  and computing system  102 . Also included in computing system  102  and attached to CPU  104  is a computer readable storage medium (CRSM)  112 , which may either be incorporated into computing system  102  i.e. an internal device, or attached externally to CPU  104  by means of various, commonly available connection devices such as but not limited to, a universal serial bus (USB) port (not shown). 
         [0024]    CRSM  112  is illustrated storing logic associated with an operating system (OS)  114  and an example of a database management system (DBMS)  116 , which includes a semantic store  118 , a semantic model  121 , semantic rules, or simply “rules,”  122  and data  124 . CRSM  112  also is illustrated storing logic associated with a semantic driver  132  and a semantic rule engine, or simply “rule engine,”  134 . DBMS  116  and data  124  should be familiar to those with skill in the relevant arts. Semantic store  118 , semantic model  121 , rules  122 , semantic driver  132  and rule engine  134  represent components of a semantic system that implements the claimed subject matter and are explained in more detail below in conjunction with  FIGS. 2-7 . 
         [0025]    Computing system  102  and is connected to the Internet  140 , which is also connected to a server computer  142 . Like computing system  102 , server would typically include a processor, CPU, monitor, keyboard and mouse, which are not illustrated for the sake of simplicity. Also coupled to server  142  is a CRSM  144 . Further, although in this example, computing system  102  and server  142  are communicatively coupled via the Internet  140 , they could also be coupled through any number of communication mediums such as, but not limited to, a local area network (LAN) (not shown). Further, it should be noted there are many possible computing system configurations, of which computing system architecture  100  is only one simple example. 
         [0026]      FIG. 2  is a block diagram showing the relationships among various elements of a semantic system  150  that may implement the claimed subject matter. Semantic system  150  includes semantic store  118 , semantic model  121 , rules  122 , semantic driver  132  and rule engine  134 , all of which were introduced above in conjunction with  FIG. 1 . With respect to the terminology of the relationship between elements  118 ,  121 ,  122 ,  132  and  134  of system  150 , semantic store  118  is based on semantic model  121 ; semantic driver  132  is built on semantic model  121 ; rule engine  134  runs on rules  122  and invokes semantic driver  132 ; and rules  122  are verbalized on semantic model  121  and semantic driver  132 . 
         [0027]    Semantic store  118  supports semantic reasoning and stores semantic objects, which in this example include two semantic objects, i.e., a SO_ 1   119  and a SO_ 2   120 , which capture aspects of a business operating model, such as, but not limited to, business entities, properties of business entities and relationships between business entities. Semantic model  121  is a representation of domain knowledge and the “controller” for bow rule engine  134  interprets and interacts with semantic store  118 . 
         [0028]    Rules  122  express conditions and actions that are taken if the conditions are met. In the presence of semantic driver  132 , rules  122  is based upon semantic model  121 , which underlies semantic store  118  and is expressed in terms of classes, properties and instances of semantic model  121 . Rule engine evaluates domain data to determine if rules conditions are met and takes defined actions if conditions are met. Semantic driver  132  enables rule engine  134  to dynamically interact with and directly manipulate objects such as SO_ 1   119  and SO_ 2   120  in semantic store  118  and allows rule conditions to be evaluated directly against objects in semantic store  118  and rule actions to change objects in semantic store  118 . Both semantic driver  132  and rule engine  134  are domain agnostic in the described embodiment. 
         [0029]    Each rule may be either a “constraint” or “non-constraint” rule. Non-constraint rules, or “general action rules,” are rules that are based on the current state of semantic store  118 . In other words, if a condition associated with a non-constraint rule is TRUE based upon the current state of semantic store  118 , then the corresponding action is executed. Both constraint and non-constraint rules may be employed in accordance with the claimed subject matter to maintain the underlying integrity of semantic store  118  and semantic model  121  ( FIGS. 1 and 2 ), i.e. modify objects in semantic store  118  based upon conditions defined in semantic model  121 . In addition, a constraint rule may be automatically generated based upon information in semantic model  121 , i.e. not generated by an administrator. For example, semantic model  121  may specify that there is a one-to-one relationship between SO_ 1   119  ( FIG. 2 ) and SO_ 2   120  ( FIG. 2 ). This constraint may be used to generate a rule that automatically enforces the constrain on semantic store  121  by modifying SO_ 1   119 , SO_ 2   120  or both. In short, constraint rules may modify semantic store  121  to “fix” a non-compliance with an asserted constraint, whereas a typical semantic reasoning system would simply report an inconsistency. 
         [0030]      FIG. 3  is a block diagram of semantic driver  132 , described in  FIGS. 1 and 2 , which implements aspects of the claimed subject matter. In this example, semantic driver  132  is associated with logic stored on CRSM  112  ( FIG. 1 ) and executed on one or more processors (not shown) of CPU  104  ( FIG. 1 ) and computing system  102  ( FIG. 1 ). 
         [0031]    Semantic driver  132  includes an input/output (I/O) module  140 , a data module  142 , a translation module  144 , a rule engine interface module  146  and operation logic  148 . It should be understood that the claimed subject matter can be implemented in many types of computing systems and data storage structures but, for the sake of simplicity, is described only in terms of computer  102  and system architecture  100  ( FIG. 1 ). Further, the representation of semantic driver  132  in  FIG. 3  is a logical model. In other words, components  140 ,  142 ,  144 ,  146  and  148  may be stored in the same or separates files and loaded and/or executed within computing system  102  and architecture  100  either as a single system or as separate processes interacting via any available inter process communication (IPC) techniques. 
         [0032]    I/O module  149  handles any communication semantic driver  132  has with other components of system  100 . Data module  142  is a data repository for information, including settings and information, that semantic driver  132  requires during normal operation. Examples of the types of information stored in data module  142  include semantic system data  152 , which enable semantic driver  132  to identify and connect with other components of semantic system  150  ( FIG. 2 ) such as semantic store  118  ( FIG. 2 ), semantic model  121 , rules  122  and rule engine  134 . Option data  154  includes information on various user preferences that have been set. For example, a user may define various parameters that control the operation of semantic driver  132  such as, but not limited to, “optimistic” or “pessimistic” modes for locks on semantic data updates. 
         [0033]    Translation nodule  144  translates from the rules  122  ( FIGS. 1 and 2 ) of rule engine  134  ( FIGS. 1 and 2 ), which is responsible of the evaluation of rule expression and derivation of conditions and actions that may result in data updates, to operations on semantic store  118  ( FIGS. 1 and 2 ). Such derived conditions and actions are processed through rule engine interface  146 . In this manner, rule engine interface module  146  enables rule engine  134  to run “live” on semantic store  118 , mitigating the need to load snapshot data from semantic store  118  directly into rule engine  134 . One particular benefit of this approach is that semantic driver  132  can be engineered generally such that its behavior may be based upon definitions in semantic model  121  ( FIGS. 1 and 2 ). Operation logic  148  includes logic for coordinating the operation of modules  140 ,  142 ,  144  and  146 . Components  142 ,  144 ,  146 ,  148 ,  150 ,  152  and  154  are described in more detail below in conjunction with  FIGS. 4-7 . 
         [0034]      FIG. 4  is a flowchart of an example of an “Author Semantic Object (SO) Rules” process  200  that may implement aspects of the claimed subject matter. Typically, actions associated with process  209  would be executed by system architect, analyst or administrator with results stored, in this example, in CRSM  112  ( FIG. 1 ) and executed on one or more processors (not shown) of CPU  104  ( FIG. 1 ) of computing, system  102  ( FIG. 1 ). 
         [0035]    Process  200  starts in a “Begin Author Semantic Object (SO) Rules” block  292  and proceeds immediately to an “Organize Semantic Model (SM) classes and properties (C&amp;P)” block  204 . During processing associated with block  204 , a subset of classes and properties associated with semantic model  121  ( FIGS. 1 and 2 ) are selected for timber processing. During processing associated with a “Describe Conditions on Semantic Content (SC) Based on C&amp;P)” block  206 , condition portions of rules  122  ( FIGS. 1 and 2 ) are defined with respect to the classes and properties selected during processing, associated with  204 . During processing associated with a “Define Actions Associated With Conditions” block  208 , each set of conditions described during processing associated with block  206  is associated with one or more actions, which may include updates to semantic data  124  ( FIG. 1 ), and are defined, using a verbalization language defined as part of semantic driver  132  ( FIGS. 1-3 ). In this manner, the typical vocabulary of the action portion of rules  122  provided as part of a standard rule engine is extended. 
         [0036]    During processing associated with a “Save Conditions and Corresponding Actions” block  210 , the conditions described during processing associated with block  206  and the corresponding actions defined during processing associated with block  208  are saved, in this example, to CRSM  112  as part of the environment of rule engine  134  ( FIGS. 1 and 2 ). Finally, process  200  proceeds to an “End Author SO Rules” block  219  in which process  200  is complete. 
         [0037]      FIG. 5  is a flowchart of an example of an “Evaluate Rule on Semantic Objects (SOs)” process  230  that may implement aspects of the claimed subject matter. In this example, process  230  is associated with logic stored on CRSM  112  ( FIG. 1 ) in conjunction with semantic driver  132  ( FIGS. 1-3 ) and executed on one or more processors (not shown) of CPU  104  ( FIG. 1 ) of computing system  102  ( FIG. 1 ). It should be understood that, although explained in conjunction with the evaluation of one rule in accordance with the claimed subject matter, i.e., the “current” rule, process  230  may typically be applied to many if not all rules in rules  122  ( FIGS. 1 and 2 ) in an iterative or batch fashion. 
         [0038]    Process  230  starts in a “Begin Evaluate Rule on Semantic Objects (SOs)” block  232  and proceeds immediately to an “Invoke Semantic Driver (SD) to Access Semantic Store (SS)” block  234 . During processing associated with block  234 , process  230  invokes semantic driver  132  to evaluate semantic store  118  ( FIGS. 1 and 2 ) with respect to the current rule. During processing associated with an “Evaluate Condition(s) Based Upon SS” block  236 , the condition part of the current rule is evaluated with respect to the data in semantic store  118 . During processing associated with a “Condition (Con.) Fulfilled?” block  238 , a determination is made as to whether or not the facts in semantic store  118  indicate that the condition of the current rule is satisfied. If so, control proceeds to an “Action on Semantic Store (SS)?” block  240 . 
         [0039]    During processing associated with block  240 , a determination is made as to whether or not the action associated with the current rule acts on, or modifies, an object of semantic store  118 . If a determination is made that the current rule does not act on objects in SS  118 , control proceeds to an “Execute Action” block  242 . During processing associated with block  242 , the action associated with the current rule is executed. If during processing associated with block  240 , a determination is made that the current rule does act on one or more objects of SS  118 , then control proceeds to an “Invoke Semantic Driver (SD) to Execute Action on Semantic Store (SS)” block  244 . During processing associated with block  244 , semantic driver  132  may change semantic objects, such as SO_ 1   119  and SO_ 2   120 , to ensure consistency between semantic store  118  and semantic model  121 . Processing associated with block  244  is described in more detail below in conjunction with  FIG. 6 . Finally, control proceeds to an “End Evaluate Rule SOs” block  249  in which process  230  is complete. 
         [0040]      FIG. 6  is a flowchart of an example of a “Derive New Data” process  260  that may implement aspects of the claimed subject matter. Like process  230 , in this example, process  260  is associated with logic stored on CRSM  112  ( FIG. 1 ) in conjunction with semantic driver  132  ( FIGS. 1-3 ) and executed on one or more processors (not shown) of CPU  104  ( FIG. 1 ) of computing system  102  ( FIG. 1 ). In addition, in this example, process  260  is associated with block  244  ( FIG. 5 ) of process  230  ( FIG. 5 ) in that certain actions in accordance with the claimed subject matter may alter data stored in semantic store  118  ( FIGS. 1 and 2 ). 
         [0041]    Process  260  starts in a “Begin Derive New Data” block  262  and proceeds immediately to an “Evaluate Condition(s) of Rule on Data” block  234 . During processing associated with block  264 , the conditional portion of the current rule (see  FIG. 5 ) is evaluated. It should be noted that the data necessary to evaluate a condition may be retrieved from sources other than merely semantic store  118 . For example, one condition associated with a rule may rely upon the current time and date in Tokyo, Japan with a definition of “current time” defined as part of semantic model  121  ( FIGS. 1 and 2 ). Another example of a condition may be whether or not it is nighttime in Tokyo, which would depend upon both the current time in Tokyo and a definition that “nighttime” is between 10 PM and 7 AM. In these examples, the definition of how to determine current time and nighttime is defined as part of semantic model  121  but actual information needed to evaluate the condition must be retrieved from an external source, defined within the model based upon the vocabulary. 
         [0042]    Another example would be a condition that relies upon whether or not a particular student is “full time.” If a full time student is defined in semantic model  121  as a degree seeking student, enrolled in three (3) or more classes that are not audits and are for a total of more than eleven (11) credit hours, then the definition would be defined using the vocabulary of semantic model  121  but information about the student necessary to arrive at a conclusion may be stored externally, for example in a student database. 
         [0043]    During processing associated with a “Condition (Con.) True?” block  266  a determination is made as to whether or not the condition evaluated during processing associated with block  264  is TRUE. If so, control proceeds to a “Derive New Data” block  268 . During processing associated with block  268 , the action portion of the rule is executed and new data is derived. As explained above, a rule may necessitate modification of objects such as SO_ 1   119  ( FIG. 2 ) and SO_ 2   120  ( FIG. 2 ) of semantic store  118 . 
         [0044]    During processing associated with a “Store New Data in Semantic Store (SS)” block  270 , the new data derived during processing associated with block  268  is stored in semantic store  118 . During processing associated with a “Rerun All Rules” block  272 , the rules in rule  122  ( FIGS. 1 and 2 ), including the current rule, are rerun because the objects of semantic store  118  have been changed during processing associated with block  268  and  270 . In this manner, SS  118  may be “auto adjusted” until a particular constraint is satisfied. Finally, during processing associated with an “End Derive New Data” block  279 , process  260  is complete. 
         [0045]      FIG. 7  is a flowchart of an example of a “Generate Constraint Rules” process  300  that may implement aspects of the claimed subject matter. Like process  200  ( FIG. 4 ), some actions associated with process  300  may be executed by system architect, analyst or administrator with results stored, in this example, in CRSM  112  ( FIG. 1 ) and executed on one or more processors (not shown) of CPU  104  ( FIG. 1 ) of computing system  102  ( FIG. 1 ). 
         [0046]    Process  300  starts in a “Begin Generate Constraint Rules” block  302  and proceeds immediately to an “Determine List of Semantic Constraints” block  304 . During processing associated with block  304 , a list of constraints, defined as part of semantic model  121  ( FIGS. 1 and 2 ), is generated. Such a list may be generated by a user such as a system architect or administrator or auto-generated programmatically. Some examples of such constraints include, but are not limited to, the cardinality of a relationship among objects in semantic store  118 , the type of the relationship, the type of an attribute associated with an object and so on. During processing associated with a “Get Next Constraint in List” block  306 , processing of the list generated during processing associated with block  304  is initiated by selecting an unprocessed constraint from the list of constraints. 
         [0047]    During processing associated with a “Generate Rule” block  308 , the constraint selected during processing associated with block  306  is associated with a rule. For example, for an instance of attribute type that specifies that the type of attribute X must be type Y, the rule may be “if type of attribute X is not Y.” Typically, a constraint rule is TRUE if a corresponding semantic constraint is not fulfilled, i.e., the rule “fires” when the constraint is broken. During, processing associated with “Select Action From Action List” block  310 , and an action for the rule defined during processing associated with block  308 . In this example, a list of possible action is provided. For example, possible actions may include “remove inappropriately typed attribute,” “change the remove inappropriately typed attribute” or “flag the inconsistency so that the issue may be resolved by an external system intervention.” 
         [0048]    During processing associated with an “Add Action to Rule” block  312 , the action selected during processing associated with block  310  is paired with the rule generated during processing associated with block  308 . In other words, the generated rule is paired with the selected action. During processing, associated with a “Store Rule/Action” block  314 , the rule action pair generated during processing, associated with block  312 is stored in rules  122  ( FIGS. 1 and 2 ). 
         [0049]    During processing associated with a “More Constraints?” block  316 , a determination is made as to whether or not there are unprocessed rules in the list generated during processing associated with block  304 . If so, control returns to block  306 , the next unprocessed rule is selected and processing continues as described above. In not, control proceeds to an “End Generate Constraint Rules” block  319  during which process  300  is complete. 
         [0050]    The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
         [0051]    The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing, the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 
         [0052]    The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block, in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending, upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.