Patents by Inventor Huong Thu Morris
Huong Thu Morris has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 9053437Abstract: 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.Type: GrantFiled: November 6, 2008Date of Patent: June 9, 2015Assignee: International Business Machines CorporationInventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Douglas C. Lovell, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Patent number: 8595042Abstract: 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.Type: GrantFiled: March 20, 2012Date of Patent: November 26, 2013Assignee: International Business Machines CorporationInventors: Sharon C. Adler, Francisco P. Curbera, Yurdaer N Doganata, Chung-Sheng Li, Axel Martens, Kevin P. McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Patent number: 8229775Abstract: 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.Type: GrantFiled: November 6, 2008Date of Patent: July 24, 2012Assignee: International Business Machines CorporationInventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Publication number: 20120179510Abstract: 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.Type: ApplicationFiled: March 20, 2012Publication date: July 12, 2012Applicant: International Business Machines CorporationInventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Patent number: 8209204Abstract: 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.Type: GrantFiled: November 6, 2008Date of Patent: June 26, 2012Assignee: International Business Machines CorporationInventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Patent number: 7792817Abstract: A relationship and navigation data repository can interface with a central data model and contains templates that define relationships among data from a number of distributed heterogeneous data sources. An integration engine is coupled to the relationship and navigation data repository, and can receive a query command and determine which of the heterogeneous data sources and which of the templates the query applies to, and then calculate the desired query result, responsive to the command, based on the relevant data sources and templates. The distributed heterogeneous data sources are managed by a system and method that involves obtaining pre-existing definitional data, instantiating in-memory nodes for the data, initializing a relationship attribute and an entity attribute for each of the nodes, and then forming an updated navigation tree structure therefrom.Type: GrantFiled: April 19, 2005Date of Patent: September 7, 2010Assignee: International Business Machines CorporationInventors: Eric Yu-sen Shan, Huong Thu Morris
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Publication number: 20100114630Abstract: 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.Type: ApplicationFiled: November 6, 2008Publication date: May 6, 2010Inventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Publication number: 20100114629Abstract: 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.Type: ApplicationFiled: November 6, 2008Publication date: May 6, 2010Inventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Douglas C. Lovell, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Publication number: 20100114628Abstract: 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.Type: ApplicationFiled: November 6, 2008Publication date: May 6, 2010Inventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Axel Martens, Kevin Patrick McAuliffe, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski
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Publication number: 20100114627Abstract: 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.Type: ApplicationFiled: November 6, 2008Publication date: May 6, 2010Inventors: Sharon C. Adler, Francisco Phelan Curbera, Yurdaer Nezihi Doganata, Chung-Sheng Li, Axel Martens, Kevin Patrick McAuliffee, Huong Thu Morris, Nirmal K. Mukhi, Aleksander A. Slominski