Consolidation and customization of graph-based models

Techniques of consolidation and customization of graph-based models are disclosed. A first graph-based representation of a first model can comprise a first set of nodes corresponding to data items of the first model, and a second graph-based representation of a second model can comprise a second set of nodes corresponding to data items of the second model. Matching nodes between the first set of nodes and the second set of nodes can be identified. Matching topological features between the first set of nodes and the second set of nodes can be identified. Matching dependency characteristics between the first set of nodes and the second set of nodes can be identified. A third graph-based representation of a consolidated model can be created based on the matching nodes, the matching topological features, and the matching dependency characteristics.

TECHNICAL FIELD

The present application relates generally to the technical field of data processing, and, in various embodiments, to methods and systems of consolidation and customization of graph-based models created by different actors.

BACKGROUND

Maintaining knowledge and knowledge sharing between employees and partners is a crucial task for companies to stay competitive. One of the means for knowledge sharing is the use of models. However, current solutions lack an efficient way to consolidate and customize models.

DETAILED DESCRIPTION

Example methods and systems of consolidation and customization of graph-based models are disclosed. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present embodiments can be practiced without these specific details.

Models can be visualized using a graph-based representation. Graphs can comprise nodes (e.g., proper model entities) and edges (e.g., weighted and directed connections between nodes, expressing, for example, dependencies or influences between nodes). A graph can enable a feasible interpretation of dependencies between data items represented as nodes. They can be used for describing and analysis of complex problems, including, but not limited to, root-cause analysis, key performance indicator (KPI) analysis, what-if simulation, and production networks. The models and their graph-based representations can be produced by different means and actors, including, but not limited to, expert estimations, statistical analysis, fact-based reasoning, structural analysis, and user interaction. Furthermore, one single model sometimes represents only one specific viewpoint of an expert or one area of interest. As result, there can exist different models for the same use case, or models covering parts of a single use case. For solving a certain analytical problem, a user might not be able to work simultaneously with different models, since it is not clear what version of model should be used for analysis. In these situations, the work of different actors can be merged into one valid consolidated model, covering other relevant models. This generally valid consolidated model can be used to create new problem-specific or use case specific models that are appropriate for the current situation. In the course of time, the situation in an enterprise can change, which can lead to an adjustment of the specific models, as well as influence and evolve the consolidated model.

The systems and methods of the present disclosure enable the continuous consolidation and customization of models to keep them up-to date. This disclosure discloses a method and a system architecture to consolidate different models (e.g., with a graph-based visualization) and to derive new models specific for analysis tasks (e.g., user-specific, area-specific, etc.). Specific models can be created for a certain analytical task. Consolidated models can be created based on different specific and consolidated (if already available) models and represent the consolidated knowledge of several experts that increases their validity. At their turn, new specific models can be derived from consolidated models, so that the re-use of models and adjusting of them to the specific analysis needs are supported.

In some embodiments, a first graph-based representation of a first model created by a first user can be accessed. The first graph-based representation can comprise a first set of nodes and connections between the nodes, each node in the first set of nodes corresponding to a data item of the first model. A second graph-based representation of a second model created by a second user can also be accessed. The second graph-based representation can comprise a second set of nodes and connections between the nodes, each node in the second set of nodes corresponding to a data item of the second model. Matching nodes between the first set of nodes and the second set of nodes can be identified. Matching topological features between the first set of nodes and the second set of nodes can be identified. Matching dependency characteristics between the first set of nodes and the second set of nodes can be identified. A third graph-based representation of a consolidated model can be created based on the matching nodes, the matching topological features, and the matching dependency characteristics.

In some embodiments, a conflict between the first graph-based representation of the first model and the second graph-based representation of the second model can be identified, the third graph-based representation of the consolidated model can be caused to be displayed to a resolving user, and the resolving user can be enabled to resolve the conflict by modifying the third graph-based representation of the consolidated model. In some embodiments, the conflict is between at least one topological feature of the first set of nodes and at least one topological feature of the second set of nodes. In some embodiments, the conflict is between at least one dependency characteristic of the first set of nodes and at least one dependency characteristic of the second set of nodes.

In some embodiments, a deriving user can be enabled to derive a fourth graph-based representation of a derivative model from the third graph-based representation of the consolidated model. In some embodiments, enabling the deriving user to derive the fourth graph-based representation comprises enabling the deriving user to add a new node, modify at least one topological feature, and modify at least one dependency characteristic. In some embodiments, the fourth graph-based representation of the derivative model can be merged with the third graph-based representation of the consolidated model to form a fifth graph-based representation of an updated consolidated model.

In some embodiments, identifying matching nodes comprises performing a semantic matching technique. In some embodiments, identifying matching nodes comprises performing a schema matching technique.

In some embodiments, the data items of the first model and the data items of the second model comprise concrete data objects. In some embodiments, the data items of the first model and the data items of the second model comprise abstract semantic concepts.

The methods or embodiments disclosed herein may be implemented as a computer system having one or more modules (e.g., hardware modules or software modules). Such modules may be executed by one or more processors of the computer system. In some embodiments, a non-transitory machine-readable storage device can store a set of instructions that, when executed by at least one processor, causes the at least one processor to perform the operations and method steps discussed within the present disclosure.

FIG. 1is a network diagram illustrating a client-server system100, in accordance with an example embodiment. A platform (e.g., machines and software), in the example form of an enterprise application platform112, provides server-side functionality, via a network114(e.g., the Internet) to one or more clients.FIG. 1illustrates, for example, a client machine116with programmatic client118(e.g., a browser), a small device client machine122with a small device web client120(e.g., a browser without a script engine), and a client/server machine117with a programmatic client119.

Turning specifically to the example enterprise application platform112, web servers124and Application Program Interface (API) servers125can be coupled to, and provide web and programmatic interfaces to, application servers126. The application servers126can be, in turn, coupled to one or more database servers128that facilitate access to one or more databases130. The cross-functional services132can include relational database modules to provide support services for access to the database(s)130, which includes a user interface library136. The web servers124, API servers125, application servers126, and database servers128can host cross-functional services132. The application servers126can further host domain applications134.

The cross-functional services132provide services to users and processes that utilize the enterprise application platform112. For instance, the cross-functional services132can provide portal services (e.g., web services), database services and connectivity to the domain applications134for users that operate the client machine116, the client/server machine117and the small device client machine122. In addition, the cross-functional services132can provide an environment for delivering enhancements to existing applications and for integrating third-party and legacy applications with existing cross-functional services132and domain applications134. Further, while the system100shown inFIG. 1employs a client-server architecture, the embodiments of the present disclosure are of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system.

The enterprise application platform112can implement partition level operation with concurrent activities. For example, the enterprise application platform112can implement a partition level lock, a schema lock mechanism, manage activity logs for concurrent activity, generate and maintain statistics at the partition level, and efficiently build global indexes. The enterprise application platform112is described in greater detail below in conjunction withFIG. 2.

FIG. 2is a block diagram illustrating enterprise applications and services in an enterprise application platform112, in accordance with an example embodiment. The enterprise application platform112can include cross-functional services132and domain applications134. The cross-functional services132can include portal modules140, relational database modules142, connector and messaging modules144, API modules146, and development modules148.

The portal modules140can enable a single point of access to other cross-functional services132and domain applications134for the client machine116, the small device client machine122, and the client/server machine117. The portal modules140can be utilized to process, author and maintain web pages that present content (e.g., user interface elements and navigational controls) to the user. In addition, the portal modules140can enable user roles, a construct that associates a role with a specialized environment that is utilized by a user to execute tasks, utilize services and exchange information with other users and within a defined scope. For example, the role can determine the content that is available to the user and the activities that the user can perform. The portal modules140include a generation module, a communication module, a receiving module and a regenerating module. In addition the portal modules140can comply with web services standards and/or utilize a variety of Internet technologies including Java, J2EE, SAP's Advanced Business Application Programming Language (ABAP) and Web Dynpro, XML, JCA, JAAS, X.509, LDAP, WSDL, WSRR, SOAP, UDDI and Microsoft .NET.

The relational database modules142can provide support services for access to the database(s)130, which includes a user interface library136. The relational database modules142can provide support for object relational mapping, database independence and distributed computing. The relational database modules142can be utilized to add, delete, update and manage database elements. In addition, the relational database modules142can comply with database standards and/or utilize a variety of database technologies including SQL, SQLDBC, Oracle, MyS QL, Unicode, JDBC, or the like.

The connector and messaging modules144can enable communication across different types of messaging systems that are utilized by the cross-functional services132and the domain applications134by providing a common messaging application processing interface. The connector and messaging modules144can enable asynchronous communication on the enterprise application platform112.

The API modules146can enable the development of service-based applications by exposing an interface to existing and new applications as services. Repositories can be included in the platform as a central place to find available services when building applications.

The development modules148can provide a development environment for the addition, integration, updating and extension of software components on the enterprise application platform112without impacting existing cross-functional services132and domain applications134.

Turning to the domain applications134, the customer relationship management application150can enable access to and can facilitate collecting and storing of relevant personalized information from multiple data sources and business processes. Enterprise personnel that are tasked with developing a buyer into a long-term customer can utilize the customer relationship management applications150to provide assistance to the buyer throughout a customer engagement cycle.

Enterprise personnel can utilize the financial applications152and business processes to track and control financial transactions within the enterprise application platform112. The financial applications152can facilitate the execution of operational, analytical and collaborative tasks that are associated with financial management. Specifically, the financial applications152can enable the performance of tasks related to financial accountability, planning, forecasting, and managing the cost of finance.

The human resource applications154can be utilized by enterprise personnel and business processes to manage, deploy, and track enterprise personnel. Specifically, the human resource applications154can enable the analysis of human resource issues and facilitate human resource decisions based on real time information.

The product life cycle management applications156can enable the management of a product throughout the life cycle of the product. For example, the product life cycle management applications156can enable collaborative engineering, custom product development, project management, asset management and quality management among business partners.

The supply chain management applications158can enable monitoring of performances that are observed in supply chains. The supply chain management applications158can facilitate adherence to production plans and on-time delivery of products and services.

The third-party applications160, as well as legacy applications162, can be integrated with domain applications134and utilize cross-functional services132on the enterprise application platform112.

FIG. 3illustrates a system architecture300used in consolidation and customization of models, in accordance with some example embodiments. The system architecture300can comprise a model management system310. The model management system310can be configured to enable one or more different users (e.g., User 1, User 2, . . . , User N) to request, direct, or otherwise cause, the performance of functions related to models, such as those functions discussed below with respect toFIGS. 4-8. The term “users” can refer to any one of people, organizations, companies, or other entities. The users can access the model management system310using machines, such as any one of the client machines116,117, and122inFIG. 1.

The users can access the model management system310via a user interface (UI)320integrated or otherwise associated with the model management system310. The UI320can be configured to enable users to create, retrieve, edit, consolidate, validate, view, and analyze models.

User data can be stored in a user data storage340. The user data storage340can comprise one or more databases or other storage mediums. The user data can comprise data relating to and/or affecting each user's use of the model management system310. Examples of user data include, but are not limited to, user identification information, user login information (e.g., username and password), and user preference information.

Model data can be stored in a model data storage350. The model data storage350can comprise one or more databases or other storage mediums. The model data can comprise specific models and consolidated models. The model data can also comprise graph-based representations of the models.

Different methods of model management (e.g., the methods or operations discussed below with respect toFIGS. 4-8) can be stored in a method repository360. The method repository360can comprise one or more databases or other storage mediums.

The model management system310can support the management of different kind of models, as well as the execution of different methods of model management (e.g., consolidation). The model management system310can use the user data storage340, the model data storage350, and the method repository360for persisting and managing specific and consolidated models, as well as for keeping libraries of methods and operations for implementing the features of the present disclosure.

In some embodiments, one or more third party tools330can be used for creation of models. The model management system310can receive models from the third party tool(s)330, and the models can then be integrated as specific models into the model data storage350.

In some embodiments, the model management system310can be incorporated into the enterprise application platform112inFIG. 1(e.g., on application server(s)126). In some embodiments, user data storage340, model data storage350, and method repository360can be incorporated into database(s)130inFIG. 1. However, it is contemplated that other configurations are also within the scope of the present disclosure.

FIG. 4is a block diagram illustrating components of the model management system310, in accordance with some example embodiments. In some embodiments, the model management system310can comprise any combination of one or more of a model creation module410, a node matching module420, a topology matching module430, a dependency characteristics matching module440, a consolidated model creation module450, and a validation module460. These modules410,420,430,440,450, and460can reside on a machine having a memory and at least one processor (not shown). In some embodiments, these modules410,420,430,440,450, and460can be incorporated into the enterprise application platform112inFIG. 1(e.g., on application server(s)126). However, it is contemplated that other configurations are also within the scope of the present disclosure.

In some embodiments, the model creation module410is configured to create models. The model creation module410can also create graph-based representations of the models. Each graph-based representation can comprise a set of nodes and connections between the nodes. Each node can correspond to a data item of the model. It is contemplated that a variety of different methods can be used to create a model. Such methods for model creation can include, but are not limited to, statistical modeling (e.g., using regression models resulting in decision trees), process and production site structure analysis that creates the models based on the structure of the enterprise or site, data structure analysis (e.g., where the models are produced based on the analysis of tables and dependencies between them), and expert estimations (e.g., where human experts define the models based on their experience). Other methods of model creation are also within the scope of the present disclosure. Subsequent to their creation, the models and their corresponding graph-based representations can be accessed.

In some embodiments, the node matching module420is configured to identify matching nodes between the nodes of two or more different models. The nodes of the different models can be matched based on the characteristics of the nodes (e.g., position, naming, identification, queries, description, values, etc.) to determine if they represent the same concept or data item and thus can be merged. It is contemplated that a variety of different node matching algorithms can be used to identify the matching nodes. Such node matching algorithms include, but are not limited to, semantic matching (e.g., a general approach to match the semantics of two different nodes or schemas), generic schema matching (e.g., matching of general hierarchies of nominal values), and XML schema matching (e.g., comparing of XML data elements using their names, semantic meaning, structure etc.). Other node matching algorithms are also within the scope of the present disclosure.

In some embodiments, the topology matching module430is configured to identify matching topological features between two or more different models. The topology matching module430can analyze the level, layers, and hierarchical structure of the matched nodes. The topology matching module430can resolve dependencies by their matching and create structural information. For this phase, graph drawing layout algorithms can be applied. Examples of such graph drawing layout algorithms include, but are not limited to, LinLog (described in http://www.informatik.tu-cottbus.de/˜an/GD/linlog.html, which is incorporated by reference in its entirety) and Sugiyama's algorithm (described in http://jgaa.info/accepted/2005/EiglspergerSiebenhallerKaufmann2005.9.3.pdf, which is incorporated by reference in its entirety), and graph clustering techniques (e.g., techniques described in “Graph clustering based on structural/attribute similarities”, Proc. VLDB Endow., VLDB Endowment, 2009, 2, 718-729, http://dLacm.org/citation.cfm?id=1687627.1687709). Other methods of matching topological features are also within the scope of the present disclosure. In response to a determination by the topology matching module430that dependencies cannot be solved, the nodes (or a sub-model) can be recognized and treated by the topology matching module430as a separate branch in the eventual consolidated model.

In some embodiments, the dependency characteristics matching module440is configured to identify matching dependency characteristics between two or more different models. It is contemplated that a variety of different algorithms can be used to match dependency characteristics. Different methods for comparing the nominal (e.g., semantic closeness) and numerical values (e.g., weighted averages) can be applied. Depending on the selected method, the automatic merging of dependency characteristics can be made. Different approaches to merging the data like weighted average for numerical data can be used. The weighting coefficients can be chosen depending on actuality of the model, confidence of the expert, and other factors. If the dependencies are not numerical numbers but text, then the weighting based on the semantic closeness can be applied. Other methods of matching dependency characteristics are also within the scope of the present disclosure.

In some embodiments, the consolidated model creation module450is configured to create a consolidated model based on the matching nodes, the matching topological features, and the matching dependency characteristics. With the knowledge about matched nodes, detected dependencies, and harmonized or combined dependency characteristics, a consolidated model can created. Criteria of consolidation, which can used by the consolidation algorithms, can be defined by the user. This step can be achieved using an initial consolidated model integrating every further model. In case of conflicts or if nodes cannot be matched, the resolving of conflicts can be done by a user.

In some embodiments, the validation module460is configured to validate the consolidated model. The consolidated model or some parts of it can be presented to a user to resolve pending conflicts. The user can be supported in this task using a graph-based representation. In some embodiments, validation of a model can comprise storing the model for later access and use by a user.

FIG. 5illustrates a sample workflow500of consolidation and customization of models, in accordance with some example embodiments. A variety of data items507can be provided by one or more sources505. The data items507can be abstract semantic concepts or concrete data objects (e.g., tables in a database). AlthoughFIG. 5illustrates only one database as source505, in some embodiments, the workflow considers multiple different data sources505as input.

At Stage 1 of the workflow500, a first user (e.g., User 1) can select data items507and build his or her own use case specific model510using the selected data items507. This model510comprises nodes, which are the data items form Source(s)505, and relations between them, represented by the arrows between the nodes. This selection of data items and building of a model can be performed using a GUI320or a third party tool330, and the model510can be displayed using a graph-based representation. A second user (e.g., User 2) can also select data items507(some could be the same as those selected by User 1, and some could be different from those selected by User 1) from the source(s)505, and the second user can create another use case specific model520(e.g., for a different business unit than the business unit for model510). This model520can be visualized in a graph-based representation. In this manner, multiple users can create multiple models.

For creating the models (e.g., models510and520), manual creation by a user can be sidestepped. For example, statistical modeling (e.g., using regression models) or structural analysis that creates the models based on the structure of the factory (or in the database) can be applied instead of manual creation by a user.

At Stage 2, the created models510and520can be consolidated to form a consolidated model530. In order to consolidate the created models510and520into consolidated model530, the previously discussed operations of node matching (e.g., as previously discussed with respect to node matching module420), topology matching (e.g., as previously discussed with respect to topology matching module430), dependency characteristic matching (e.g., as previously discussed with respect to dependency characteristics matching module440), and consolidated model creation (e.g., as previously discussed with respect to consolidate model creation module450) can be performed. These operations can be performed automatically by running the corresponding methods in method execution components. The results of these methods can be validated by a user.

At Stage 3, a third user (e.g., User 3) can derive a specific model540from the consolidated model530(e.g., as a use case specific derivate model) according to his analysis needs (e.g., logistics data). The specific model540can be displayed in a graph form and can optionally be connected to a data source of real data (e.g., a regular database, an enterprise data warehouse, a resource description framework store, or an XML file). In course of analysis, the third user can enrich the model540with new data items507by creating new nodes or dependencies, updating characteristics, etc. These data items507were previously not covered by the consolidated model530. The third user can also updates some characteristics of a dependency according to the requirements of his use case. His changes to the model can lead to a new graph representation.

At stage 4, the changes of the third user in the derived specific model540can be incorporated back into the consolidated model530by consolidating specific model540and consolidated model530into consolidated model550. This consolidation can update the consolidated model530with new information (e.g., local characteristics, use-case specific settings) to a new graph representation shown as consolidated model550.

FIG. 6is a flowchart illustrating a method600, in accordance with some example embodiments. Method600can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In one implementation, the method600is performed by the model management system310ofFIGS. 3-4, or any combination of one or more of its modules, as described above.

At operation610, a first graph-based representation of a first model created by a first user can be accessed. The first graph-based representation can comprise a first set of nodes and connections between the nodes, each node in the first set of nodes corresponding to a data item of the first model. At operation620, a second graph-based representation of a second model created by a second user can be accessed. The second graph-based representation can comprise a second set of nodes and connections between the nodes, each node in the second set of nodes corresponding to a data item of the second model. At operation630, matching nodes between the first set of nodes and the second set of nodes can be identified, as previously discussed with respect to the node matching module420. At operation640, matching topological features between the first set of nodes and the second set of nodes can be identified, as previously discussed with respect to the topology matching module430. At operation650, matching dependency characteristics between the first set of nodes and the second set of nodes can be identified, as previously discussed with respect to the dependency characteristics matching module440. At operation660, a third graph-based representation of a consolidated model can be created based on the matching nodes, the matching topological features, and the matching dependency characteristics, as previously discussed with respect to the consolidated model creation module450. At operation670, the consolidated model can be validated, as previously discussed with respect to the validation module460.

FIG. 7is a flowchart illustrating a method700, in accordance with some example embodiments. Method700can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In one implementation, the method700is performed by the model management system310ofFIGS. 3-4, or any combination of one or more of its modules, as described above. In some embodiments, the operations of method700can be incorporated into method600.

At operation710, a conflict between a first graph-based representation of a first model and a second graph-based representation of a second model can be identified. In some embodiments, the conflict comprises a lack of matching nodes between a first set of nodes of the first model a second set of nodes of the second model. In some embodiments, the conflict is between at least one topological feature of a first set of nodes of the first model and at least one topological feature of a second set of nodes of the second model. In some embodiments, the conflict is between at least one dependency characteristic of a first set of nodes of the first model and at least one dependency characteristic of a second set of nodes of the second model. At operation720, a third graph-based representation of a consolidated model (e.g., a consolidation of the first model and the second model) can be caused to be displayed to a resolving user. At operation730, the resolving user can be enabled to resolve the conflict by modifying the third graph-based representation of the consolidated model.

FIG. 8is a flowchart illustrating a method800, in accordance with some example embodiments. Method800can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In one implementation, the method800is performed by the model management system310ofFIGS. 3-4, or any combination of one or more of its modules, as described above. In some embodiments, the operations of method800can be incorporated into methods600and700.

At operation810, a deriving user can be enabled to derive a fourth graph-based representation of a derivative model from a third graph-based representation of a consolidated model. In some embodiments, enabling the deriving user to derive the fourth graph-based representation comprises enabling the deriving user to add a new node, modify at least one topological feature, and modify at least one dependency characteristic. At operation820, the fourth graph-based representation of the derivative model can be merged with the third graph-based representation of the consolidated model to form a fifth graph-based representation of an updated consolidated model.

The features of the present disclosure enable the use of a user interface or third party tools for creating models from multiple data sources, as well as using data storage for persisting and managing of specific and consolidated models and for keeping the libraries of methods realizing the operations disclosed herein.

In some embodiments, created models can be consolidated by performing the following steps: a) matching nodes on the basis of their characteristics and merging them if they possess matching concept/data items; b) allowing a user to define criteria of consolidation; c) matching topology of the matched nodes in the model by analyzing their level, layers, and hierarchical structure; d) matching different characteristics of the dependencies; e) creating a consolidated model based on matched nodes, detected dependencies, and combined dependency characteristics; and f) repeating the consolidation process until pending conflicts are resolved by a user using graphical representation of the consolidated model.

The features of the present disclosure also enable a consolidated model to be customized by deriving a specific model from the consolidated model according to user needs, and allowing a user to add nodes and/or dependencies, update characteristics, and perform other modifications to the specific model. The derivative specific model can be integrated back into the consolidated model.

The features disclosed herein provide a fundamental approach to combining multiple specific models into one generic model and vice versa. From a business perspective, the features can be used to create an enterprise-wide model for performance measurement and KPI analysis reflecting the local expertise of local responsible persons (e.g., production managers in a factory of a distributed enterprise). Using such a generic model allows the creation of a valid but specific model, which applies to, for example, new production sites. The combination of distributed models into one generic/consolidated model allows for reflecting the local features, such as dependency characteristics (e.g., weight or influence).

Example Mobile Device

FIG. 9is a block diagram illustrating a mobile device900, according to an example embodiment. The mobile device900can include a processor902. The processor902can be any of a variety of different types of commercially available processors suitable for mobile devices900(for example, an XScale architecture microprocessor, a Microprocessor without Interlocked Pipeline Stages (MIPS) architecture processor, or another type of processor). A memory904, such as a random access memory (RAM), a Flash memory, or other type of memory, is typically accessible to the processor902. The memory904can be adapted to store an operating system (OS)906, as well as application programs908, such as a mobile location enabled application that can provide LBSs to a user. The processor902can be coupled, either directly or via appropriate intermediary hardware, to a display910and to one or more input/output (I/O) devices912, such as a keypad, a touch panel sensor, a microphone, and the like. Similarly, in some embodiments, the processor902can be coupled to a transceiver914that interfaces with an antenna916. The transceiver914can be configured to both transmit and receive cellular network signals, wireless data signals, or other types of signals via the antenna916, depending on the nature of the mobile device900. Further, in some configurations, a GPS receiver918can also make use of the antenna916to receive GPS signals.

Modules, Components and Logic

Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules can be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications can be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules can be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module can then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules can also initiate communications with input or output devices and can operate on a resource (e.g., a collection of information).

The various operations of example methods described herein can be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein can, in some example embodiments, comprise processor-implemented modules.

Similarly, the methods described herein can be at least partially processor-implemented. For example, at least some of the operations of a method can be performed by one or more processors or processor-implemented modules. The performance of certain of the operations can be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors can be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors can be distributed across a number of locations.

The one or more processors can also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations can be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the network114ofFIG. 1) and via one or more appropriate interfaces (e.g., APIs).

The example computer system1000includes a processor1002(e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory1004and a static memory1006, which communicate with each other via a bus1008. The computer system1000can further include a video display unit1010(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system1000also includes an alphanumeric input device1012(e.g., a keyboard), a user interface (UI) navigation (or cursor control) device1014(e.g., a mouse), a disk drive unit1016, a signal generation device1018(e.g., a speaker) and a network interface device1020.

The disk drive unit1016includes a machine-readable medium1022on which is stored one or more sets of data structures and instructions1024(e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions1024can also reside, completely or at least partially, within the main memory1004and/or within the processor1002during execution thereof by the computer system1000, the main memory1004and the processor1002also constituting machine-readable media. The instructions1024can also reside, completely or at least partially, within the static memory1006.