A method, system and article of manufacture are disclosed for configuring software application components. The method comprises the steps of developing a set of policy application rules, assembling unconfigured software components into one or more software applications, and applying said application rules to the unconfigured software components to configure said software components. In the preferred embodiment, the applying step includes the steps of passing the unconfigured software components to a policy rule engine, and using said policy rule engine to apply said application rules to the unconfigured software components to produce the configured components. In addition, the method may be done to resolve ambiguities in the software components. In particular, the application rules may be designed to resolve ambiguities in the application of these rules to the unconfigured software components. Also, each application rule preferably includes a condition, an application template, and a policy.
Processing Of Provenance Data For Automatic Discovery Of Enterprise Process Information
Sharon C. Adler - East Greenwich RI, US Francisco P. Curbera - Hastings on Hudson NY, US Yurdaer N Doganata - Chestnut Ridge NY, US Chung-Sheng Li - Scarsdale NY, US Axel Martens - White Plains NY, US Kevin P. McAuliffe - Yorktown Heights NY, US Huong Thu Morris - Ridgefield CT, US Nirmal K. Mukhi - Ramsey NJ, US Aleksander A. Slominski - Bronx NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06Q 10/00
US Classification:
705 711
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.
Influencing Behavior Of Enterprise Operations During Process Enactment Using Provenance Data
Sharon C. Adler - East Greenwich RI, US Francisco Phelan Curbera - Hastings on Hudson NY, US Yurdaer Nezihi Doganata - Chestnut Ridge NY, US Chung-Sheng Li - Scarsdale NY, US Axel Martens - White Plains NY, US Kevin Patrick McAuliffe - Yorktown Heights NY, US Huong Thu Morris - Ridgefield CT, US Nirmal K. Mukhi - Ramsey NJ, US Aleksander A. Slominski - Bronx NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/60
US Classification:
705 711
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.
Pattern-Based Policy Application Mechanism For Sca
David A. Booz - Rhinebeck NY, US Francisco P. Curbera - Hastings on Hudson NY, US Shinichi Hirose - Tokyo, JP Nirmal K. Mukhi - Ramsey NJ, US Yuichi Nakamura - Tokyo, JP Fumiko Satoh - Tokyo, JP
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 21/00 G06F 3/00
US Classification:
705 59, 719318, 719331
Abstract:
A pattern-based policy method for service component architecture (SCA) defines a policy pattern for SCA. The policy pattern includes a plurality of roles and one or more constraints between said plurality of roles. Each of said plurality of roles defines a plurality of intents or policy sets or combination thereof. One or more roles assigned to said one or more SCA components are identified and one or more intents or policy sets or combination thereof associated with said one or more roles are automatically applied to said one or more SCA components. Said one or more intents or policy sets or combination thereof applied to said one or more SCA components are validated based on said one or more constraints.
Processing Of Provenance Data For Automatic Discovery Of Enterprise Process Information
Sharon C. Adler - East Greenwich RI, US Francisco Phelan Curbera - Hastings on Hudson NY, US Yurdaer Nezihi Doganata - Chestnut Ridge NY, US Chung-Sheng Li - Scarsdale NY, US Axel Martens - White Plains NY, US Kevin Patrick McAuliffee - Yorktown Heights NY, US Huong Thu Morris - Ridgefield CT, US Nirmal K. Mukhi - Ramsey NJ, US Aleksander A. Slominski - Bronx NY, US
International Classification:
G06Q 10/00 G06F 17/30 G06T 11/20 G06F 7/06
US Classification:
705 7, 345440
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.
Extracting Enterprise Information Through Analysis Of Provenance Data
Sharon C. Adler - East Greenwich RI, US Francisco Phelan Curbera - Hastings on Hudson NY, US Yurdaer Nezihi Doganata - Chestnut Ridge NY, US Chung-Sheng Li - Scarsdale NY, US Douglas C. Lovell - Fishkill NY, US Axel Martens - White Plains NY, US Kevin Patrick McAuliffe - Yorktown Heights NY, US Huong Thu Morris - Ridgefield CT, US Nirmal K. Mukhi - Ramsey NJ, US Aleksander A. Slominski - Bronx NY, US
International Classification:
G06Q 10/00
US Classification:
705 7
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.
Capturing And Visualizing Data Lineage In Content Management System
Francisco P. Curbera - Hastings on Hudson NY, US Yurdaer N. Doganata - Chestnut Ridge NY, US Axel Martens - White Plains NY, US Huong T. Morris - Los Gatos CA, US Nirmal K. Mukhi - Lake Grove NY, US Aleksander A. Slominski - Bronx NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 3/048
US Classification:
715771
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.
Francisco P. Curbera - Hawthorne NY, US Yurdaer N. Doganata - Hawthorne NY, US Rania Y. Khalaf - Cambridge MA, US Geetika T. Lakshmanan - Cambridge MA, US Axel Martens - Hawthorne NY, US Kevin P. McAuliffe - Hawthorne NY, US Nirmal K. Mukhi - Hawthorne NY, US Aleksander A. Slominski - Hawthorne NY, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06Q 10/06
US Classification:
705 736
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.
Transfr
Chief Technology Officer
Ibm
Development Leader, Watson Education Platform
Ibm
Development Manager, Ibm Watson Education
Ibm Jan 2015 - Jun 2016
Solutions Portfolio Manager
Ibm Nov 2000 - Dec 2014
Software Engineer
Education:
Indiana University Bloomington 1996 - 1999
Master of Science, Masters, Computer Science
Birla Institute of Technology and Science, Pilani 1991 - 1996
Bachelors, Bachelor of Science, Economics, Computer Science
Skills:
Distributed Systems Java Enterprise Architecture Cloud Computing C Software Development Linux Soa Computer Science Algorithms