Abstract. While a considerable amount of research on enterprise ontologies exists, work showing h... more Abstract. While a considerable amount of research on enterprise ontologies exists, work showing how to use ontologies for enterprise architecture (EA) analysis is scarce. We present our ongoing work on creating analyzable enterprise models using EA-based ontological representation. Our contributions are twofold: first, we show how an existing EA modeling language can be leveraged to create EA ontology and second, we show how EA analyses can be realized using this ontology. Initial results of basic EA change impact analysis suggest that ontology representation facilitates EA analysis prototyping due to right mix of representation and reasoning functionalities.
Reducing energy consumption of mobile systems in order to prolong their operating time has been a... more Reducing energy consumption of mobile systems in order to prolong their operating time has been an interesting research topic over the past several years. Such systems are typically battery powered. Hence, their uptime depends on the energy consumptions of the used components. By applying novel strategies that allow systems to dynamically adapt at runtime can be effectively used to reduce energy consumption. The focus of this paper is based on a case study that uses an energy management component that can dynamically choose the “best” sorting algorithm duing a multi-party mobile communication. The results indicate that Insertionsort is the most optimal sorting algorithm when in comes to saving energy.
Taking and executing correct decisions is critical in enterprise systems which are characterized ... more Taking and executing correct decisions is critical in enterprise systems which are characterized by rapid changes along interconnected dimensions. Enterprise architecture (EA) frameworks offer holistic treatment of enterprise systems but constitute only one part of the solution to problems arising due to organizational changes. The other, less explored part is the ability to explicate and analyze the intentions behind major decisions. We investigate a step-by-step approach where intentional modeling is treated as a problem solving technique. In our approach, an intentional model devoid of goals is obtained from the existing EA model via mapping. It is expanded by representing the problems due to organizational changes as goals and soft goals and alternative solutions to them. The final intentional model is transformed back to an actionable EA model via the same mapping. In the case study, we re-imagine the evolution of our model-driven software development unit as an enterprise wher...
2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, 2015
Knowledge is stored in an enterprise in various forms ranging from unstructured operational data,... more Knowledge is stored in an enterprise in various forms ranging from unstructured operational data, legal documents to structured information like programs, as well as relational data stored in databases to semi-structured information stored in xml files. All these information if viewed from a holistic standpoint can help an enterprise to understand and reflect upon itself and thereby make knowledgeable decisions whenever required. In order to satisfy this objective of holistic knowledge representation and decision making, we begin with mining unstructured information present in an enterprise. In particular, in this paper, we intend to mine a document intensive business processes and extract information as a knowledge repository that captures the various stakeholders along with their intentions and the tasks they perform. The goal is to automate the validation of such business processes by eliminating any manual verification, which is time consuming and error prone. We believe this is the first step towards realizing our broader objective of collective modeling of enterprise knowledge that will involve mining of information available in unstructured, structured, relational as well as semi-structured form present in an enterprise.
Enterprises are complex heterogeneous entities consisting of multiple stakeholders with each perf... more Enterprises are complex heterogeneous entities consisting of multiple stakeholders with each performing a particular role to meet the desired overall objective. With increased dynamics that enterprises are witnessing, it is becoming progressively difficult to maintain synchrony within the enterprise for it to function effectively. Current practice is to rely on human expertise which is time-, cost-, and effort-wise expensive and also lacks in certainty. Use of machine-manipulable models that can aid in pro-active decision-making could be an alternative. In this paper, we describe such a prescriptive decision making facility that makes use of different modeling techniques and illustrate the same with an industrial case study.
2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations, 2014
ABSTRACT Today's Enterprises exist in highly dynamic environment. Simulation could be use... more ABSTRACT Today's Enterprises exist in highly dynamic environment. Simulation could be used to reveal complex dynamic behavior of enterprise, especially for playing out dynamic what-if scenarios, in determining enterprise's response to a change. Instead of relying on guidelines for simulating prescriptive models of enterprise as in other approaches including our own in which we simulated intentional models of enterprise, we propose a comprehensive metamodel of system dynamics and provide relation-based mapping to intentional metamodel. Ongoing explorations suggest that while several challenges of simulating enterprise aspects for what-if analyses remain unaddressed, in the least we take a step toward making simulation of intentional models more structured.
Abstract. While a considerable amount of research on enterprise ontologies exists, work showing h... more Abstract. While a considerable amount of research on enterprise ontologies exists, work showing how to use ontologies for enterprise architecture (EA) analysis is scarce. We present our ongoing work on creating analyzable enterprise models using EA-based ontological representation. Our contributions are twofold: first, we show how an existing EA modeling language can be leveraged to create EA ontology and second, we show how EA analyses can be realized using this ontology. Initial results of basic EA change impact analysis suggest that ontology representation facilitates EA analysis prototyping due to right mix of representation and reasoning functionalities.
Reducing energy consumption of mobile systems in order to prolong their operating time has been a... more Reducing energy consumption of mobile systems in order to prolong their operating time has been an interesting research topic over the past several years. Such systems are typically battery powered. Hence, their uptime depends on the energy consumptions of the used components. By applying novel strategies that allow systems to dynamically adapt at runtime can be effectively used to reduce energy consumption. The focus of this paper is based on a case study that uses an energy management component that can dynamically choose the “best” sorting algorithm duing a multi-party mobile communication. The results indicate that Insertionsort is the most optimal sorting algorithm when in comes to saving energy.
Taking and executing correct decisions is critical in enterprise systems which are characterized ... more Taking and executing correct decisions is critical in enterprise systems which are characterized by rapid changes along interconnected dimensions. Enterprise architecture (EA) frameworks offer holistic treatment of enterprise systems but constitute only one part of the solution to problems arising due to organizational changes. The other, less explored part is the ability to explicate and analyze the intentions behind major decisions. We investigate a step-by-step approach where intentional modeling is treated as a problem solving technique. In our approach, an intentional model devoid of goals is obtained from the existing EA model via mapping. It is expanded by representing the problems due to organizational changes as goals and soft goals and alternative solutions to them. The final intentional model is transformed back to an actionable EA model via the same mapping. In the case study, we re-imagine the evolution of our model-driven software development unit as an enterprise wher...
2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, 2015
Knowledge is stored in an enterprise in various forms ranging from unstructured operational data,... more Knowledge is stored in an enterprise in various forms ranging from unstructured operational data, legal documents to structured information like programs, as well as relational data stored in databases to semi-structured information stored in xml files. All these information if viewed from a holistic standpoint can help an enterprise to understand and reflect upon itself and thereby make knowledgeable decisions whenever required. In order to satisfy this objective of holistic knowledge representation and decision making, we begin with mining unstructured information present in an enterprise. In particular, in this paper, we intend to mine a document intensive business processes and extract information as a knowledge repository that captures the various stakeholders along with their intentions and the tasks they perform. The goal is to automate the validation of such business processes by eliminating any manual verification, which is time consuming and error prone. We believe this is the first step towards realizing our broader objective of collective modeling of enterprise knowledge that will involve mining of information available in unstructured, structured, relational as well as semi-structured form present in an enterprise.
Enterprises are complex heterogeneous entities consisting of multiple stakeholders with each perf... more Enterprises are complex heterogeneous entities consisting of multiple stakeholders with each performing a particular role to meet the desired overall objective. With increased dynamics that enterprises are witnessing, it is becoming progressively difficult to maintain synchrony within the enterprise for it to function effectively. Current practice is to rely on human expertise which is time-, cost-, and effort-wise expensive and also lacks in certainty. Use of machine-manipulable models that can aid in pro-active decision-making could be an alternative. In this paper, we describe such a prescriptive decision making facility that makes use of different modeling techniques and illustrate the same with an industrial case study.
2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations, 2014
ABSTRACT Today's Enterprises exist in highly dynamic environment. Simulation could be use... more ABSTRACT Today's Enterprises exist in highly dynamic environment. Simulation could be used to reveal complex dynamic behavior of enterprise, especially for playing out dynamic what-if scenarios, in determining enterprise's response to a change. Instead of relying on guidelines for simulating prescriptive models of enterprise as in other approaches including our own in which we simulated intentional models of enterprise, we propose a comprehensive metamodel of system dynamics and provide relation-based mapping to intentional metamodel. Ongoing explorations suggest that while several challenges of simulating enterprise aspects for what-if analyses remain unaddressed, in the least we take a step toward making simulation of intentional models more structured.
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Papers by Suman Roychoudhury