Patent Document

BACKGROUND 
     This disclosure relates to systems and techniques for the creation and use of a thesaurus for identifiers of complex data assemblies. 
     A data assembly is a collection of associated information that is treated as an entity in data processing activities. Data assemblies include complex data structures, as well as abstractions such as data objects that can include information drawn from one or more complex data structures. Hereinafter, for the sake of convenience, the term “complex data structures” includes such abstractions even though the abstractions themselves need not be a single complex data structure. 
     Complex data structure types, also referred to as “composite data types” and “data types,” are assemblies of simple data types. Simple data types, also referred to as “primitive” and “elementary” data types, cannot be broken down into smaller component data types. In general, simple data types are the basic data types that are predefined in a language for authoring machine-readable instructions. Simple data types include, e.g., character, numeric, string, and Boolean data types. Simple types do not have element content and do not carry attributes. 
     In addition to simple data types, complex data structure types can also include other complex data structures in the assembly. In general, complex data structure types are defined by a user to fit the operational context of a particular set of machine-readable instructions. Example complex data structure types include data objects, records, arrays, tables, and the like. Complex data structure types can be defined by a user who assembles a set of elements, fields, and/or attributes to form a reusable data structure. Each of these has a type and, as discussed above, hierarchical and recursive complex data structure types that are themselves assembled from complex data structure types can be formed. 
     A data structure identifier, or a “key,” is information that identifies a complex data structure for data processing activities performed in accordance with a set of machine-readable instructions. The identification is generally unambiguous, i.e., each identifier or key generally refers to a single complex data structure to the exclusion of all other data structures. 
     A data structure identifier can include, e.g., a name or a value that identifies the object within an identification scheme, a scheme identifier that identifies a frame of reference in which it is possible to identify a data structure, and an agency identifier that identifies the entity that defines the identification scheme and issues names for data structures within the identification scheme. 
     Different applications, different modules, different data processing systems, different data processing system landscapes, and different public identification scheme entities (such as Dun &amp; Bradstreet, which issues DUNS numbers, and GS1, which issues GTIN&#39;s) can use different identification schemes, in which even the same single data structure is referred to using different identifiers. 
     Moreover, even a single application, module, data processing system, data processing system landscape, and/or public identification scheme entity can use multiple complex data structures of the same semantic type to refer to the same real-world item. Semantic type is a descriptive attribute of information that identifies the behavior (i.e., the semantics) for that information. The semantic type of information can identify the usage and rules for that information to set of a data processing instructions. Two or more objects (or other complex data structures) of the same semantic type can be used to refer to the same single real world entity in one or more sets of data processing activities. For example, a data processing module can include a “product object” instance that includes attributes and values that characterize an instance of a real-world item as a product. The same data processing module can include a “material object” that has the same attributes and values and characterizes the same real-world item, but as a material. Moreover, a second data processing module can include a “design object” that has the same attributes and values and characterizes the same real-world item, but as a design. Even though such objects may refer to the same single real-world entity and share the same semantic type, the various objects may be referred to using different identifiers. 
     When information regarding a data structure or structures is exchanged, a process called key mapping can be used to translate the different identifiers. In general, key mapping involves accessing a key mapping database where keys used by a first set of processing activities are associated with keys used by a second set of processing activities. When information regarding one or more complex data structures is exchanged, one of the sets of processing activities can access the key mapping database to translate the key from the source processing activities to the key in the second processing activities. 
     SUMMARY 
     Systems and techniques for the creation and use of a complex data structure identifier thesaurus are described. 
     In one aspect, an article comprises one or more machine-readable media storing instructions operable to cause one or more machines to perform operations. The operations include receiving, from a data processing system, a collection of mapping information identifying a first object and a first collection of two or more keys used to identify the first object, determining whether a first key in the first collection is found in a first mapping group of a mapping data store, determining whether second key in the first collection is found in a second mapping group of the mapping data store, and merging the first mapping group and the second mapping group to reflect that objects from the first mapping group and the second mapping group are related. 
     This and other aspects can include one or more of the following features. Each mapping group can include references to two or more related objects. A first of the related objects can be associated with a first collection of one or more keys and a second of the related objects can be associated with a second collection one or more keys. Also, none of the keys in the first collection need be found in the second collection and none of the keys in the second collection need be found in the first collection. The first mapping group and the second mapping group can be merged by forming a merged mapping group. 
     The first mapping group and the second mapping group can also be merge by eliminating a reference to a first object found in one of the first mapping group and the second mapping group, and associating keys that were associated with the eliminated first object with an object in the merged mapping group. The merging of the first mapping group and the second mapping group can also include storing, outside of any mapping group, at least one of a reference to the first object or a reference an object from one of the first mapping group and the second mapping group. 
     The operations can also include receiving, from a second data processing system, a second collection of mapping information that identifies a second object and a second collection of two or more keys used to identify the second object, determining that the second object is related to the object reference stored outside of any mapping group, and creating a new mapping group that includes the second object and the object reference that was stored outside of any mapping group. The operations can also include adding a key from the first collection to a collection of keys in the mapping data store and/or mapping keys using the mapping data store. Keys can be mapped by mapping keys associated with multiple objects in the same mapping group to each other and/or by mapping keys associated with a single object. 
     In another aspect, an article includes one or more machine-readable media storing instructions operable to cause one or more machines to perform operations. The operations can include receiving, from a data processing system, a collection of mapping information that identifies a first object and a first collection of two or more keys used to identify the first object, determining that none of the keys in the first collection are found in any mapping group of a mapping data store and that the first object is not related to any object found in any mapping group of the mapping data store, determining that a related object that is associated with a second collection of keys exists outside of any mapping group of the mapping data store, wherein the related object is related to the first object in that the related object includes same attributes as the first object and wherein none of the keys in the first collection are found in the second collection and none of the keys in the second collection are found in the first collection, and creating a new mapping group to include the related object and the first object. 
     This and other aspects can include one or more of the following features. The new mapping group can be created by adding the related object in association with the keys in the second collection to the new mapping group. The operations can also include receiving a second collection of mapping information that identifies the first object and a third collection of two or more keys and associating the first key with the first object in the new mapping group. This can be done when a first key in the third collection can be different from any of the keys in the first collection. The operations can also include receiving a second collection of mapping information that identifies an object and a third collection of two or more keys and eliminating the new mapping group. This can be done when one of the keys in the third collection can be found in the first collection and another of the keys in the third collection can be found in the second collection. 
     The new mapping group can be eliminated by storing a reference to at least one of the object, the first object, and the related object outside any mapping group in the data store. The operations can also include receiving a second collection of mapping information that identifies an object and a third collection of two or more keys and merging the new mapping group and the second mapping group. This can be done when one of the keys in the third collection can be found in the first collection and another of the keys in the third collection can be found in a second mapping group of the mapping data store. Keys can also be mapped using the mapping data store. For example, keys associated with multiple objects in the same mapping group can be mapped to each other. The mapping data store can include keys stored as core component type identifiers. 
     In another aspect, a memory for storing data for access by operations performed by one or more data processing systems can include mapping data store. The mapping data store can include a mapping group including references to two or more related objects in different data processing systems using different identification schemes and a reference to an object outside of any mapping group. A first of the related objects can be associated with a first collection of one or more keys and a second of the related objects being associated with a second collection one or more keys. None of the keys in the first collection need be found in the second collection and none of the keys in the second collection need be found in the first collection. The object outside of any mapping group can be associated with a third collection one or more keys. None of the keys in the third collection need be found in the first collection or the second collection and none of the keys in the first collection or the second collection need be found in the third collection. The object outside of any mapping group need not be related to any object in any mapping group. Related objects can be related in that they include same attributes. 
     The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic representation of an example complex data structure, namely a data object. 
         FIGS. 2 and 3  are schematic diagrams of example data processing system landscapes. 
         FIGS. 4A and 4B  is a schematic representation of how related data objects can be identified using different identifiers. 
         FIG. 5  is a schematic representation of a mapping data store for an object thesaurus. 
         FIG. 6  is a flowchart of a process for the creation and use of a data store of an object thesaurus. 
         FIG. 7  is a flowchart of a process for the creation of a data store of an object thesaurus. 
         FIGS. 8-13  schematically illustrate various examples of the modification of the mapping data store of  FIG. 5  based on mapping information. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG. 1  is a schematic representation of an example complex data structure, namely a data object  100 . Data object  100  includes an object class name  105 , a collection of attributes  110 , and a collection of operations  115 . A data object such as data object  100  is a complex data structure that generally assembles information to represent a concrete or abstract real-world entity. An object can be of a certain object class, with individual objects being instances of that class. The entities represented by an object can include, e.g., a set of data processing instructions (such as a program), a data structure (such as a table), individual entries in a data structure (such as a record in a table), a data processing system, a customer, a product, a time, or a location. A data object is generally free of internal references and information stored in a data object can be changed without concomitant changes to the data processing instructions that handle the data object. In some implementations, the information in a data object can be stored in a contiguous block of computer memory of a specific size at a specific location, although this is not necessarily the case. 
     Object class name  105  is the name of the class of data object  100 . For example, data object  100  is of the “SalesOrder” class and represents a sales order entity. Attribute collection  110  includes attributes that are properties of data object  100  and have associated values that characterize the entity represented by data object  100 . In particular, the attributes in collection  110  are HeaderID, CustomerID, SalespersonID, Date, Tax, and SalesGroupID. These attributes have values characterizing the sales order represented by data object  100 . 
     Operation collection  115  includes various data processing activities that can be performed on data object  100 . The operations in collection  115  can, e.g., return a value or change a value of an attribute in collection  110 , in another data object, or the like. The operations in collection  115  can also cause the creation and deletion of objects. 
       FIG. 2  is a schematic representation of a distributed data processing system landscape  200 . A distributed data processing system landscape can include a collection of data processing devices, software, and/or systems (hereinafter “data processing systems”) that operate autonomously yet coordinate their operations across data communication links in a network and on individual data processing devices. By operating autonomously, the data processing systems can operate in parallel, handling local workloads of data processing activities. The data communication links allow information regarding the activities, including the results of performance of the activities, to be exchanged between data processing systems. To these ends, many distributed data processing systems include distributed databases and system-wide rules for the exchange of data. 
     System landscape  200  thus is a collection of data processing systems that exchange information for the performance of one or more data processing activities in accordance with the logic of one or more sets of machine readable instructions. System landscape  200  includes one or more servers  205  that are in communication with a collection of clients  210 ,  215 ,  220  over a collection of data links  225 . 
     Server  205  is a data processing system that provides services to clients  210 ,  215 ,  120 . The services can include, e.g., the provision of data, the provision of instructions for processing data, and/or the results of data processing activities. The services can be provided in response to requests from clients  210 ,  215 ,  220 . 
     The services can be provided by server  205  in accordance with the logic of one or more applications  230 ,  235 . An application is a program or group of programs that perform one or more sets of data processing activities. An application can perform data processing activities directly for a user or for another application. Examples of applications include word processors, database programs, Web browsers, development tools, drawing, paint, image editing programs, and communication programs. In the context of enterprise software that is operable to integrate and manage the operations of a company or other enterprise, applications can be allocated to managing product lifecycles, managing customer relationships, managing supply chains, managing master data, managing financial activities, and the like. 
     Clients  210 ,  215 ,  220  are data processing systems that receive services from server  205 . Clients  210 ,  215 ,  220  can be responsible for other data processing activities, such as managing interaction with human users at their respective locations. For example, client  220  can perform data processing activities in accordance with the logic of an application  240 , and client  215  can perform data processing activities in accordance with the logic of an application  245 . In these course of these and other data processing activities, clients  110 ,  115 ,  120  can generate requests for such services and convey the requests to server  205  over one or more of data links  225 . 
     Data links  225  can form a data communication network such as a LAN, a WAN, or the Internet. System landscape  200  can also include additional data links, including direct links between clients  210 ,  215 ,  220  and data links to systems and devices outside landscape  200 , such as a communications gateway (not shown). Additional data processing activities, performed in accordance with the logic of additional applications, can be performed at the systems and devices outside landscape  200 . 
     The roles of “server” and “client” can be played by the same individual data processing system in system landscape  200 . For example, the data processing system denoted as server  205  may receive certain services from one of clients  210 ,  215 ,  220 . Thus, a data processing system may be a “server” in the context of a first set of services but a “client” in the context of a second set of services. 
       FIG. 3  is a schematic representation of another implementation of a system landscape, namely, a system landscape  300 . System landscape  300  is a three tiered hierarchy of data processing systems and includes application servers  305 ,  310 ,  315 , one or more database servers  320 , and presentation systems  325 ,  330 ,  335 . Application servers  305 ,  310 ,  315  and database server  320  are in data communication with each other and with presentation systems  325 ,  330 ,  335  over a collection of data links  340 . 
     Application servers  305 ,  310 ,  315  are data processing systems that provide services to presentation systems  325 ,  330 ,  335  and/or database server  310 . Each application server  305 ,  310 ,  315  can provide services in accordance with the logic of one or more applications. Moreover, individual application servers can also provide services in accordance with the logic of multiple applications, and services in accordance with the logic of a single application can be provided by multiple application servers. In the illustrated implementation, application server  305  provides services in accordance with the logic of applications  345 ,  350 . Application server  310  provides services in accordance with the logic of application  355 . Application server  315  provides services in accordance with the logic of application  360 . 
     Database server  320  is a data processing system that provides storage, organization, retrieval, and presentation of instructions and data services to application servers  305 ,  310 ,  315  and/or presentation systems  325 ,  330 ,  335 . 
     Presentation systems  325 ,  330 ,  335  are data processing systems that receive services from application servers  305 ,  310 ,  315  and database server  320  and perform other data processing activities. For example, presentation systems  325 ,  330 ,  335  can manage interaction with human users at their respective locations, such as the display of information on a graphical user interface. 
     In the illustrated implementation, presentation system  325  performs data processing activities in accordance with the logic of an application  365 , and presentation system  335  performs data processing activities in accordance with the logic of an application  370 . In the course of these and other data processing activities, presentation systems  325 ,  330 ,  335  can generate requests for services and convey the requests to application servers  305 ,  310 ,  215  and database server  320  over one or more of data links  340 . 
       FIG. 4A  is a schematic representation of how data object  100  ( FIG. 1 ) can be identified during different data processing activities using different identifiers. In particular, data processing activities performed in accordance with the logic of applications  405 ,  410 ,  415 ,  420  all identify data object  100  during various identifications, represented by arrows  425 ,  430 ,  435 ,  440 . However, the activities of applications  405 ,  410 ,  415 ,  420  use different identifiers to uniquely identify data object  100 . In particular, identification  425  uses keys k 1  and k 2 . Identification  430  uses keys k 3  and k 4 . Identification  435  uses keys k 5  and k 6 . Identification  440  uses keys k 7  and k 8 . 
     The existence of multiple unique keys k 1 , k 2 , k 3 , k 4 , k 5 , k 6 , k 7 , k 8  for the same data object can arise for any of a number of different reasons. For example, some of keys k 1 , k 2 , k 3 , k 4 , k 5 , k 6 , k 7 , k 8  can be universally unique ID&#39;s (UUID&#39;s) that are used internally by data processing activities. Moreover, different UUID&#39;s can be assigned by different agents for different identification schemes. Further, some of keys k 1 , k 2 , k 3 , k 4 , k 5 , k 6 , k 7 , k 8  can be identifiers that are tailored for use by humans. For example, a human user may use a value of an attribute of a data object to uniquely identify the object. For example, a human user may use a key such as “BMW” or “Toyota” to uniquely identify a customer object. Moreover, different human users and different public identification scheme entities may use different identifiers to uniquely identify a data object. 
       FIG. 4B  is a schematic representation of how multiple data objects  445 ,  450 ,  455  that describe the same real-world entity can be identified using different identifiers. In particular, data processing activities performed in accordance with the logic of application  405  can identify customer object  445  using keys k 1  and k 2 . Data processing activities performed in accordance with the logic of application  405  can identify business partner object  450  using keys k 3  and k 4 . Data processing activities performed in accordance with the logic of application  410  can identify salesperson object  455  using keys k 5  and k 6 . A single real-world entity is described by data objects  445 ,  450 ,  455  and data objects  445 ,  450 ,  455  can be of the same semantic type. 
       FIG. 5  is a schematic representation of a mapping data store  500  of an object thesaurus. Mapping data store  500  is a collection of key mapping information that associates the various keys used to identify one or more data objects in one or more data processing systems. For example, the different data processing systems can operate using different identifiers that are issued for different identification schemes by different entities. Moreover, mapping data store  500  can act as a centralized repository for key mapping information from the different data processing systems. For example, the data processing systems whose mapping information is stored at mapping data store  500  need not maintain separate key mapping information. Thus, in some implementations, mapping data store  500  can be a reusable component that provides a consistent view of mapping information to other components. In particular, details regarding data processing instructions associated with mapping data store  500  such as data replication and mapping group merges (as discussed further below) can be hidden from calling components. 
     Mapping data store  500  can be a structured data collection, such as a table, a record, a data object, a list, or the like. The key mapping information in mapping data store  500  can also be subdivided. For example, key mapping information in mapping data store  500  can be divided and the resulting divisions stored in different data structures. Mapping data store  500  can be stored at a variety of locations in a data processing system landscape. For example, mapping data store  500  can be stored at one or more of server  205  and clients  210 ,  215 ,  220  in system landscape  200  ( FIG. 2 ). As another example, mapping data store  500  can be stored at one or more of database server  320 , application servers  305 ,  310 ,  315 , and presentations systems  325 ,  330 ,  335  in system landscape  300  ( FIG. 4 ). Mapping data store  500  can also be stored remotely from system landscapes  200 ,  300  and yet be accessed from system landscapes  200 ,  300 . In some implementations, the storage of a single mapping data store  500  can be distributed across different systems in a system landscape. Mapping data store  500  can be structured into a file, packed, compressed, or otherwise prepared for storage. Mapping data store  500  can also include metadata or executable instructions that are relevant to accessing key mapping information. Examples of metadata include default keys, leading keys, and internal keys. Such metadata can be used internally, i.e., for data processing activities associated with mapping data store  500 , and need not be provided to user interfaces. 
     Mapping data store  500  includes mapping groups  505 ,  510 . A mapping group is a collection of references to related objects. The way in which the objects are related can be defined, e.g., by a user or by a set of data processing activity that accesses mapping data store  500 . For example, a user can define a mapping group to include references to the same objects that are involved in different sets of data processing activities that use different identification schemes. The objects in such a mapping group can be identical in that they have the same attributes and values, but are subject to different operations in different data processing systems. As another example, a component set of data processing activities can group similar objects in a mapping group. The objects can be similar in that there is a logical relationship between the objects. Such a logical relationship can be specified, e.g., by the component set of data processing activities in accordance with the logic of those data processing activities. One example of such a logical relationship is that the object describe the same real-world entity. 
     Mapping group  505  includes references to objects  515 ,  520 ,  525 . Mapping group  510  includes references to objects  530 ,  535 . Each of objects  515 ,  520 ,  525 ,  530 ,  535  is associated with one or more keys that are used to uniquely identify objects  515 ,  520 ,  525 ,  530 ,  535  for the relevant data processing activities. For example, object  515  can be identified using keys  540 ,  545  during data processing activities performed in accordance with a first set of instructions. Object  520  can be identified using keys  550 ,  555  during data processing activities performed in accordance with a second set of instructions. Object  525  can be identified using keys  560 ,  565  during data processing activities performed in accordance with a third set of instructions. Object  530  can be identified using keys  570 ,  575  during data processing activities performed in accordance with the first set of instructions. Object  535  can be identified using keys  580 ,  585 ,  590  during data processing activities performed in accordance with a fourth set of instructions. As shown, the number of keys per object is arbitrary. Moreover, the number of objects in excess of one in each mapping group is arbitrary. 
     In one implementation, mapping data store  500  is implemented as a storage of a collection of core component type (CCT) identifiers. A core component type identifier can identify a particular business object along with the context in which that identification is valid. For example, in addition to the identifier of the particular object, a CCT identifier can identify one or more of an identification scheme that assigned the identifier, the version of the identification scheme, an agent that administers that identification scheme, the identification scheme of such an agent, and the agent that administers the identification scheme of such an agent. 
       FIG. 6  is a flowchart of a process  600  for the creation and use of a mapping data store of an object thesaurus. Process  600  can be performed by one or more data processing systems that exchange information with one or more data processing systems. For example, one or more data processing systems can perform data processing activities for the creation of the data store for an object thesaurus, and one or more data processing systems can perform data processing activities for the use of the object thesaurus. 
     The system(s) performing process  600  can assemble key mapping information from three or more data processing systems into a single mapping data store at  605 . Such an assembly of key mapping information is more complicated than assembling mapping information from two or fewer data processing systems. For example, as discussed further below, the number and type of mappings is more difficult to define with larger numbers of data processing systems. As another example, increased numbers and different categories of mergers and deletions may be required. 
     The system(s) performing process  600  can also map the keys using the mapping data store at  610 . For example, keys can be mapped between two or more data processing systems using different identification schemes, or keys can be mapped between synchronized systems. The mappings can be performed, e.g., in response to requests received from the data processing systems themselves. 
       FIG. 7  is a flowchart of a process  700  for the creation of a data store for an object thesaurus. Process  700  can be performed independently or in conjunction with other data processing activities. For example, process  700  can be performed at  605  in process  600  ( FIG. 6 ). 
     The system(s) performing process  700  can receive information that identifies a data object and one or more keys for identifying the data object in another data processing system at  705 . The information that identifies a data object can itself be a key for identifying the data object. In some implementations, the information can be received directly from the data processing system that uses those keys. For example, the information can be received in a message that includes the keys as CCT identifiers. 
     The system(s) performing process  700  can determine if the received information appears in one or more existing mapping groups in the data store at  710 . The received information appears in an existing mapping group when the object or the keys identified in the information appear in an existing mapping group. The determination can be made by comparing the received information to the contents of the data store. For example, received keys can be compared to existing keys. 
     If the system(s) performing process  700  determines that the received information does appear in an existing mapping group, then the system(s) can, as appropriate, modify the data store at  715 . If the received information is already found in the data store and the data store already accurately reflects the received information, no modifications are necessarily performed. However, if modifications are appropriate, they can include adding some or all of the information to the data store, deleting information from the data store, and/or changing the associations between mapping groups, objects, and keys in the data store. For example, new references to new objects can be added, new keys can be added to existing objects, new mapping groups can be created, and/or existing mapping groups can be merged. Illustrative modifications are discussed further below. 
     If the system(s) performing process  700  determines that the received information does not appear in an existing mapping group, then the system(s) can determine if there is an object outside of existing mapping groups that has matching keys at  720 . The determination can be made by comparing the received information to the contents of the data store that are outside of mapping groups. 
     If the system(s) performing process  700  determines that there are not any related objects outside of existing mapping groups with different keys, then the system(s) can add the information to a data store outside of any mapping group at  725 . The addition can occur in a number of ways. For example, if a related object is found, but with at least some identical keys, any keys from the received information that do not appear in the related object outside of the existing mapping group can be added to the related object outside of the existing mapping group. As another example, if no related object is found, the received object and its keys can be added to the data store outside of any mapping group. In one implementation, this is done by inserting key, object, and group information into the relevant tables of the database. 
     If the system(s) performing process  700  determines that there are related objects outside of existing mapping groups that have different keys, then the system(s) can create and populate a new mapping group at  730 . The new mapping group can be populated with the received information, as well as the related object and its associated keys that were found in the data store but outside of existing mapping groups. Once the new mapping group is populated, the related object outside of existing mapping groups can be deleted from the data store. 
     After the activities of any of  715 ,  725 , or  730 , the system(s) performing process  700  can return to receive additional information that identifies a data object and one or more keys for identifying the data object in another data processing system at  705 . Through such repetitions, a data store can be assembled and updated to reflect the current state of two or more data processing systems. Further, the data store can be made available during such repetitions for mapping the keys between systems. 
       FIGS. 8-13  schematically illustrate various examples of the modification of mapping data store  500  based on mapping information. The illustrated additions, modifications, and similar processes can be performed by one or more data processing systems at  715  in process  700  ( FIG. 7 ). 
       FIG. 8  is a schematic representation of information  800  that can be received by a system performing process  700  at  705  ( FIG. 7 ). Information  800  identifies an object  805  and keys  810 ,  815  for identifying object  805  in a data processing system. 
     Keys  810 ,  815  can be identified as identical to keys identified in one or more existing mapping groups in a data store. For example, key  810  can be identified as identical to key  575  in mapping group  510  in mapping data store  500 , and key  815  can be identified as identical to key  585  in mapping group  510  in mapping data store  500 . 
       FIG. 9  is a schematic representation of mapping store  500  after modification in light of information  800  ( FIG. 8 ). In particular, mapping group  510  and object  535  have been eliminated to reflect that objects  530 ,  535  are mapped to each other. For example, the elimination of mapping group  510  and object  535  can reflect that objects  530 ,  535  are related objects or even the same object. Also, object  530  has been associated with keys  580 ,  585 ,  590  and is now stored in mapping data store  500  outside of a mapping group. Although object  530  and keys  570 ,  575 ,  580 ,  585 ,  590  are not applicable to mapping between objects in different data processing systems since object  530  exists in a single system, the information embodied in object  530  and keys  570 ,  575 ,  580 ,  585 ,  590  is still useful. For example, object  530  and keys  570 ,  575 ,  580 ,  585 ,  590  can be used for key mapping within a single system or between synchronized systems. As another example, object  530  and keys  570 ,  575 ,  580 ,  585 ,  590  stand ready for the creation and population of a new mapping group when objects and keys from different data processing systems are received. 
       FIG. 10  is a schematic representation of information  1000  that can be received by a system performing process  700  at  705  ( FIG. 7 ). Information  1000  identifies an object  1005  and keys  1010 ,  1015 ,  1020  for identifying object  1005  in a data processing system. 
     Keys  1010 ,  1015 ,  1020  can be identified as identical to keys identified in one or more existing mapping groups in a data store. For example, key  1010  can be identified as identical to key  560  in mapping group  505  in mapping data store  500 , key  1015  can be identified as identical to key  570  in mapping group  510  in mapping data store  500 , and key  1020  can be identified as identical to key  575  in mapping group  510  in mapping data store  500 . 
       FIG. 11  is a schematic representation of mapping store  500  after modification in light of information  1000  ( FIG. 10 ). In particular, mapping group  510  and object  530  have been eliminated and object  535  has been added to mapping group  505  to reflect that objects  525 ,  530  were not only related but also in the same data processing system. The addition of object  535  to mapping group  505  is also based on a prior identification of object  535  as related to object  530 . Also, object  525  has been associated with keys  560 ,  565 ,  570 ,  575 . 
       FIG. 12  is a schematic representation of information  1200  that can be received by a system performing process  700  at  705  ( FIG. 7 ). Information  1200  identifies an object  1205  and keys  1210 ,  1215 ,  1220 ,  1225  for identifying object  1205  in a data processing system. 
     Keys  1210 ,  1215 ,  1220  can be identified as identical to keys identified in an existing mapping group in a data store. For example, key  1210  can be identified as identical to key  550  in mapping group  505  in mapping data store  500 , key  1215  can be identified as identical to key  560  in mapping group  505  in mapping data store  500 , and key  1220  can be identified as identical to key  570  in mapping group  510  in mapping data store  500 . Key  1225  does not appear in mapping data store  500  before information  1200  is received. 
       FIG. 13  is a schematic representation of mapping data store  500  after modification in light of information  1200  and after addition of some of information  1200  ( FIG. 12 ). In particular, mapping group  510  and objects  525 ,  530  have been eliminated and object  535  has been added to mapping group  505  to reflect that objects  520 ,  525 ,  530  were not only related but also in the same data processing system. The addition of object  535  to mapping group  505  is also based on a prior identification of object  535  as related to object  530 . Also, object  520  has been associated with keys  560 ,  565 ,  570 ,  575 , and newly added key  1305 . Key  1305  has been newly added on the basis of its prior absence from mapping data store  500 . 
     Keys can be mapped using the mapping information stored in a mapping data store in a variety of different scenarios. For example, keys can be mapped when master data is distributed between harmonized data processing systems. Harmonized systems are systems which share at least one common identifier for data objects involved in data processing activities in those systems. Master data is information that is stored on a relatively long-term basis in one or more data processing systems and is often relevant to multiple processes in those systems. In the illustrative mapping data store  500  described above, keys to master data objects in such harmonized data processing systems will be associated with multiple objects in the same mapping group provided that the systems are synchronized as to those objects. 
     Keys can also be mapped when transactional data objects are distributed between harmonized data processing systems. Transactional data is information that records events occurring between individuals, groups, and organizations. Transactional data is generally created more frequently, and can be modified more often, than master data. In the illustrative mapping data store  500  described above, keys to transactional data objects in such harmonized data processing systems will be associated with multiple objects in the same mapping group provided that the systems are synchronized as to those objects. 
     Keys can also be mapped during synchronous access from one data processing system to another harmonized data processing system. Synchronous access can include a first data processing system reading of data directly from and writing data directly to a second data processing system. In the illustrative mapping data store  500  described above, keys for synchronous access in such harmonized data processing systems will be associated with multiple objects in the same mapping group provided that the systems are synchronized as to those objects. 
     Keys can also be mapped during translation of external identifiers. External identifiers are identifiers used by another data processing system landscape. For example, external identifiers can be included in messages and other information received from remote systems. In the illustrative mapping data store  500  described above, keys for the translation of external identifiers will be associated with the same single object, provided that the multiple identifiers of that single object have previously been identified to the mapping data store  500  as identifying the same object. 
     Keys can also be mapped during translation of incompatible sets of machine-readable instructions in the same data processing system landscape. Example incompatible sets of machine-readable instructions include unharmonized applications in the same data processing system landscape. In the illustrative mapping data store  500  described above, keys for translation between incompatible sets of machine-readable instructions will be associated with the same single object, provided that the multiple identifiers of that single object have previously been identified to the mapping data store  500  as identifying the same object. 
     A complex data structure thesaurus, such as data store  500 , can be also used in contexts outside of key mapping. For example, a complex data structure thesaurus can be used for object-based navigation. Object-based navigation is a navigation style based upon the characteristics at the object level, i.e., the contents of the objects and the relationship among the objects. With object-based navigation, users can specify a set of objects and their relationship. The system creates queries from the users&#39; input and determines links dynamically based on matching between this query and indices. 
     As another example, a complex data structure thesaurus can be used to identify data processing systems that use certain objects and/or identifiers. Such “where-used” checks can be used, e.g., for a corporate wide reporting of purchasing costs of a single product to identify if centralized “buying in bulk” can be used to lower the cost of that product. 
     As another example, a complex data structure thesaurus can be used in global searches and central searches with downstream identification translation. Such searches located objects by the attributes of a central object and then determine the identifier of the local representation. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. Accordingly, other implementations are within the scope of the following claims.

Technology Category: 4