Abstract: A method and system are provided to calculate a future behavioral data and identify a relative causal impact of external factors affecting the data. Behavioral data and data for one or more external factors are harvested for a first time period. New behavioral data is harvested for a second time period. New data for the second time period is harvested. Based on a second training algorithm, a forecast time series value of a future behavioral data for a third time period that is after the second time period is calculated. A relative causal impact between each external factor and the predicted time series value of the behavioral data, for the third time period, is identified.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include identifying a number of data parameters to extract from a blockchain based on a request for analytic data, creating one or more queries based on the data parameters, executing the one or more queries and retrieving the data parameters from the blockchain, identifying one or more permissions of a user account associated with the request for analytic data, and populating an interface with analytic figures based on the data parameters retrieved from the blockchain.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include identifying a number of data parameters to extract from a blockchain based on a request for analytic data, creating one or more queries based on the data parameters, executing the one or more queries and retrieving the data parameters from the blockchain, identifying one or more permissions of a user account associated with the request for analytic data, and populating an interface with analytic figures based on the data parameters retrieved from the blockchain.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include identifying a number of data parameters to extract from a blockchain based on a request for analytic data, creating one or more queries based on the data parameters, executing the one or more queries and retrieving the data parameters from the blockchain, identifying one or more permissions of a user account associated with the request for analytic data, and populating an interface with analytic figures based on the data parameters retrieved from the blockchain.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include identifying a number of data parameters to extract from a blockchain based on a request for analytic data, creating one or more queries based on the data parameters, executing the one or more queries and retrieving the data parameters from the blockchain, identifying one or more permissions of a user account associated with the request for analytic data, and populating an interface with analytic figures based on the data parameters retrieved from the blockchain.

Abstract: Process management techniques using a representation state transfer architecture include, for example, a method of managing a given process in a data-centric manner in a client/server environment, comprising at least one client and at least one server. In an exemplary method, the server obtains from the client at least one request associated with the given process. In response to the request obtained from the client, the server generates an entity, wherein the generated entity is represented in terms of a data model and a lifecycle model associated therewith, wherein the lifecycle model comprises states and state transitions with each transition caused by one or more activities in the given process, and wherein the given process is represented as a collection of communicating entities. The server sends a response to the at least one request to the client, wherein the response is based at least in part on the generated entity.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include identifying a number of data parameters to extract from a blockchain based on a request for analytic data, creating one or more queries based on the data parameters, executing the one or more queries and retrieving the data parameters from the blockchain, identifying one or more permissions of a user account associated with the request for analytic data, and populating an interface with analytic figures based on the data parameters retrieved from the blockchain.

Abstract: A method and system are provided to calculate a future behavioral data and identify a relative causal impact of external factors affecting the data. Behavioral data and data for one or more external factors are harvested for a first time period. New behavioral data is harvested for a second time period. New data for the second time period is harvested. Based on a second training algorithm, a forecast time series value of a future behavioral data for a third time period that is after the second time period is calculated. A relative causal impact between each external factor and the predicted time series value of the behavioral data, for the third time period, is identified.

Abstract: Techniques for generating and executing a performance-aware enterprise component (PAEC) are provided.

Abstract: Process management techniques using a representation state transfer architecture include, for example, a method of managing a given process in a data-centric manner in a client/server environment, comprising at least one client and at least one server. In an exemplary method, the server obtains from the client at least one request associated with the given process. In response to the request obtained from the client, the server generates an entity, wherein the generated entity is represented in terms of a data model and a lifecycle model associated therewith, wherein the lifecycle model comprises states and state transitions with each transition caused by one or more activities in the given process, and wherein the given process is represented as a collection of communicating entities. The server sends a response to the at least one request to the client, wherein the response is based at least in part on the generated entity.

Abstract: Execution of flexible workflows using artifacts is described. A workflow execution engine is configured to instantiate a process execution (PE) artifact. The PE artifact includes one or more transitions. The workflow execution engine is further configured to execute the one or more transitions and determine if any of the one or more transitions are new or modified. The workflow execution engine is additionally configured to load and execute new or modified transitions, without reinstantiating the PE artifact, responsive to determining that at least one new or modified transitions exist.

Abstract: Execution of flexible workflows using artifacts is described. A workflow execution engine is configured to instantiate a process execution (PE) artifact. The PE artifact includes one or more transitions. The workflow execution engine is further configured to execute the one or more transitions and determine if any of the one or more transitions are new or modified. The workflow execution engine is additionally configured to load and execute new or modified transitions, without reinstantiating the PE artifact, responsive to determining that at least one new or modified transitions exist.

Abstract: Process management techniques using a representation state transfer architecture that manages a given process in a data-centric manner are disclosed. By way of one example, a method of managing a given process in a data-centric manner in a client/server environment, comprising at least one client and at least one server, comprises the following steps. The server obtains from the client at least one request associated with the given process. In response to the request obtained from the client, the server generates an entity, wherein the generated entity is represented in terms of a data model and a lifecycle model associated therewith, wherein the lifecycle model comprises states and state transitions with each transition caused by one or more activities in the given process, and wherein the given process is represented as a collection of communicating entities. The server sends a response to the at least one request to the client, wherein the response is based at least in part on the generated entity.

Abstract: Techniques for generating and executing a performance-aware enterprise component (PAEC) are provided.

Abstract: The invention provides a system adapted to enable contextual collaboration within a computer network. The invention provides a collaboration manager that supports manipulation of collaboration spaces. The collaboration spaces contain one or more collaboration elements that have potentially different collaboration modalities. The context manager maintains resources and resource interrelationships within contexts. The resources can include collaboration elements, processes, tasks, business objects, users, and/or roles. A view generator selects a set of the resources to be displayed to a user of the computer network. The invention also includes an interface adapted to allow applications to specify commands to manipulate collaboration spaces, commands to maintain resources and resource interrelationships within contexts, and queries for context-sensitive views.

Abstract: Techniques for modeling a composite application are provided. The techniques include identifying one or more entities that are processed in a user enterprise, identifying one or more actions to be performed during a lifecycle of the one or more entities, and modeling a composite application based on the one or more actions to be performed during a lifecycle of the one or more entities.

Abstract: An enterprise system includes a plurality of artifacts (e.g., persons, objects, databases, autonomous elements, intelligent agents, information systems, etc), and a smart distance (e.g., a distance which may optimize the performance of the enterprise system) between the plurality of artifacts.

Abstract: An enterprise system includes a plurality of artifacts (e.g., persons, objects, databases, autonomous elements, intelligent agents, information systems, etc), and a smart distance (e.g., a distance which may optimize the performance of the enterprise system) between the plurality of artifacts.

Abstract: An enterprise system includes a plurality of artifacts (e.g., persons, objects, databases, autonomous elements, intelligent agents, information systems, etc), and a smart distance (e.g., a distance which may optimize the performance of the enterprise system) between the plurality of artifacts.

Abstract: A Web browser is augmented with collaborative features to support community aware browsing sessions. The augmentation provided includes a data mining plug-in for the browser to establish collaborative context based on Uniform Resource Locator (URL) meta data keywords, or mined topics, and a collaboration co-browser (explorer bar) which displays a collaboration space, called CollabSpace, corresponding to the document that is displayed in the main browser. With this augmented browser, a user can perform various collaboration functions from the collaboration explorer bar, including viewing the online status of members of the CollabSpace, communicate with members of the CollabSpace via electronic mail (e-mail), instant messaging or discussion threads, and register as a member of the CollabSpace. CollabSpaces can be established and associated with one or more Web documents, meta data keywords or topics.