Abstract: A computer-implemented method, system, and article of manufacture for extracting and processing event data from heterogeneous computer applications. The method includes providing a computer system having software modules for performing the steps of: receiving data related to a first event; identifying a data type of the first event based on data type definitions; selecting a set of extraction rules for extracting an attribute of an event having the identified data type; extracting the first attribute from the first event data based on the set of extraction rules; and mapping the first attribute to an event attribute of a unified structure.

Abstract: A method, system and computer program product for determining health of a case. The method includes the steps of: obtaining at least one correlated trace from (i) task descriptions or (ii) data related to the task descriptions or a process instance; calculating at least one current metric using (i) the task descriptions, (ii) the data, (iii) the correlated trace or (iv) a first model; calculating at least one prognostic metric using a second model; and creating at least one combination metric from the current metric and the prognostic metric; where at least one of the steps is carried out using a computer device.

Abstract: A computer-implemented method, system, and article of manufacture for determining a set of correlated data among heterogeneous computer applications. The method includes providing a computer system having software modules, receiving statistics on data relating to a first event and a second event, generating a confidence score for an attribute set, where the attribute set includes an attribute from the first event data and an attribute from the second event data, and selecting the attribute set as a set of correlated data if the confidence score is within a threshold value.

Abstract: Receiving from each of a plurality of low level monitor models an outbound event comprising information associated with a respective data source. The information received in each of the outbound events can be collected to a global monitoring context in which the information is automatically aggregated. Via a global cube associated with the global monitoring context, the information can be presented in a report. At least one new outbound event can be received. The new outbound event can include updated information associated with at least one of the data sources. Responsive to receiving the new outbound event, the information presented the report can be updated in real time to reflect the updated information.

Abstract: Receiving from each of a plurality of low level monitor models an outbound event comprising information associated with a respective data source. The information received in each of the outbound events can be collected to a global monitoring context in which the information is automatically aggregated. Via a global cube associated with the global monitoring context, the information can be presented in a report. At least one new outbound event can be received. The new outbound event can include updated information associated with at least one of the data sources. Responsive to receiving the new outbound event, the information presented the report can be updated in real time to reflect the updated information.

Abstract: Receiving from each of a plurality of low level monitor models an outbound event comprising information associated with a respective data source. The information received in each of the outbound events can be collected to a global monitoring context in which the information is automatically aggregated. Via a global cube associated with the global monitoring context, the information can be presented in a report. At least one new outbound event can be received. The new outbound event can include updated information associated with at least one of the data sources. Responsive to receiving the new outbound event, the information presented the report can be updated in real time to reflect the updated information.

Abstract: A computer-implemented method, system, and article of manufacture for determining a set of correlated data among heterogeneous computer applications. The method includes providing a computer system having software modules, receiving statistics on data relating to a first event and a second event, generating a confidence score for an attribute set, where the attribute set includes an attribute from the first event data and an attribute from the second event data, and selecting the attribute set as a set of correlated data if the confidence score is within a threshold value.

Abstract: A computer-implemented method, system, and article of manufacture for extracting and processing event data from heterogeneous computer applications. The method includes providing a computer system having software modules for performing the steps of: receiving data related to a first event; identifying a data type of the first event based on data type definitions; selecting a set of extraction rules for extracting an attribute of an event having the identified data type; extracting the first attribute from the first event data based on the set of extraction rules; and mapping the first attribute to an event attribute of a unified structure.

Abstract: Techniques are disclosed for capturing, storing, querying and analyzing provenance data for automatic discovery of enterprise process information. For example, a computer-implemented method for managing a process associated with an enterprise comprises the following steps. Data associated with an actual end-to-end execution of an enterprise process is collected. Provenance data is generated based on at least a portion of the collected data, wherein the provenance data is indicative of a lineage of one or more data items. A provenance graph that provides a visual representation of the generated provenance data is generated, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. The generated provenance graph is stored in a repository for use in analyzing the enterprise process.

Abstract: Techniques are disclosed for capturing and visualizing data lineage in content management systems. For example, a method comprises the following steps. A plurality of data sets is received. Each of the data sets is associated with a party and comprises a plurality of information. A set of lineage data about one or more of the data sets is received. The lineage data comprises information about the history of a particular data set. A user interface is presented that conveys a representation of one or more of the plurality of received data sets and at least a portion of the lineage data about the history of one or more of the data sets. A command is received at the user interface to merge or unmerge two data sets in the plurality of data sets. Two or more data sets in the plurality of data sets are merged or unmerged based on the received command.

Abstract: Techniques are disclosed for capturing, storing, querying and analyzing provenance data for automatic discovery of enterprise process information. For example, a computer-implemented method for managing a process associated with an enterprise comprises the following steps. Data associated with an actual end-to-end execution of an enterprise process is collected. Provenance data is generated based on at least a portion of the collected data, wherein the provenance data is indicative of a lineage of one or more data items. A provenance graph that provides a visual representation of the generated provenance data is generated, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. The generated provenance graph is stored in a repository for use in analyzing the enterprise process.

Abstract: Techniques are disclosed for influencing behavior of enterprise operations during process enactment using provenance data. For example, a computer-implemented method of influencing a behavior of an enterprise process comprises the following steps. Provenance data is generated, wherein the provenance data is based on collected data associated with at least a partial actual execution of the enterprise process and is indicative of a lineage of one or more data items. A provenance graph is generated that provides a visual representation of the generated provenance data, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. At least a portion of the generated provenance data from the graph is analyzed to generate an execution pattern corresponding to the at least partial actual execution of the enterprise process. The execution pattern is compared to one or more previously stored patterns.

Abstract: Techniques for role-based service operation status reporting to clients are provided. In one aspect, a method for reporting a status of a service operation to a client is provided. The method includes the following steps. A sequence of business process steps involved in performing the service operation is identified. One or more abstractions of the business process steps are made, each abstraction containing a sequence of a fewer number of steps than the business process, wherein the number of steps in each of the abstractions correlates with a level of detail about the service operation. The status of the service operation is reported to the client based on a given one of the abstractions having the level of detail best suited to a role of the client.

Abstract: Techniques are disclosed for influencing behavior of enterprise operations during process enactment using provenance data. For example, a computer-implemented method of influencing a behavior of an enterprise process comprises the following steps. Provenance data is generated, wherein the provenance data is based on collected data associated with at least a partial actual execution of the enterprise process and is indicative of a lineage of one or more data items. A provenance graph is generated that provides a visual representation of the generated provenance data, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. At least a portion of the generated provenance data from the graph is analyzed to generate an execution pattern corresponding to the at least partial actual execution of the enterprise process. The execution pattern is compared to one or more previously stored patterns.

Abstract: Techniques are disclosed for extracting information through analysis of provenance data. For example, a computer-implemented method of extracting information regarding an execution of an enterprise process comprises the following steps. Provenance data is generated, wherein the provenance data is based on collected data associated with an actual end-to-end execution of the enterprise process and is indicative of a lineage of one or more data items. A provenance graph is generated that provides a visual representation of the generated provenance data, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. At least a portion of the generated provenance data from the graph is analyzed so as to extract information about the execution of the enterprise process based on the analysis.

Abstract: Techniques are disclosed for capturing, storing, querying and analyzing provenance data for automatic discovery of enterprise process information. For example, a computer-implemented method for managing a process associated with an enterprise comprises the following steps. Data associated with an actual end-to-end execution of an enterprise process is collected. Provenance data is generated based on at least a portion of the collected data, wherein the provenance data is indicative of a lineage of one or more data items. A provenance graph that provides a visual representation of the generated provenance data is generated, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. The generated provenance graph is stored in a repository for use in analyzing the enterprise process.

Abstract: Techniques are disclosed for validating compliance with enterprise operations based on provenance data. For example, a computer-implemented method for validating that an enterprise process is in compliance with a rule comprises the following steps. Provenance data is generated, wherein the provenance data is based on collected data associated with an actual end-to-end execution of the enterprise process and is indicative of a lineage of one or more data items. A provenance graph is generated that provides a visual representation of the generated provenance data, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. A correlation is generated between one or more entities in the rule and one or more record types in the provenance data. One or more control points are generated in accordance with the generated correlation.