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Knowledge Based Consolidation of UML Diagrams for Creation of Virtual Enterprise

Author(s): Debasis Chanda | Dwijesh Dutta Majumder | Swapan Bhattacharya

Journal: Intelligent Information Management
ISSN 2160-5912

Volume: 02;
Issue: 03;
Start page: 159;
Date: 2010;
Original page

Keywords: Knowledge Base | Predicate Calculus | Service Oriented Architecture | UML | Fuzzy Data Mining | Cluster Analysis

In this paper we address the problem related to determination of the most suitable candidates for an M&A (Merger & Acquisition) scenario of Banks/Financial Institutions. During the pre-merger period of an M&A, a number of candidates may be available to undergo the Merger/Acquisition, but all of them may not be suitable. The normal practice is to carry out a due diligence exercise to identify the candidates that should lead to optimum increase in shareholder value and customer satisfaction, post-merger. The due diligence ought to be able to determine those candidates that are unsuitable for merger, those candidates that are relatively suitable, and those that are most suitable. Towards achieving the above objective, we propose a Fuzzy Data Mining Framework wherein Fuzzy Cluster Analysis concept is used for advisability of merger of two banks and other Financial Institutions. Subsequently, we propose orchestration/composition of business processes of two banks into consolidated business process during Merger & Acquisition (M&A) scenario. Our paper discusses modeling of individual business process with UML, and the consolidation of the individual business process models by means of our proposed Knowledge Based approach.
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