Authors:
Kritika Anand
;
Sayandeep Mitra
and
Pavan Kumar Chittimalli
Affiliation:
TCS Innovation Labs, Pune and India
Keyword(s):
Business Rules, First Order Logic, SBVR, SMT Solvers, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Application Integration Technologies
;
Applications
;
Applications and Software Development
;
Artificial Intelligence
;
Component-Based Software Engineering
;
Formal Methods
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Model-Driven Software Development
;
Requirements Engineering
;
Simulation and Modeling
;
Software Engineering
;
Software Engineering Methods and Techniques
;
Symbolic Systems
Abstract:
Presently, business organizations are regulating their activities with the aid of Business Rules (BR’s). A single rule set of an organization contains large and diverse categories of BR’s , thereby making it difficult for Business Analysts and end users to analyze and extract relevant BR’s. Rule Search with natural language terms fail due to their inability to capture logical semantics present in BR’s. In this paper, we present a novel approach to give correct and complete sets of SBVR (Semantics of Business Vocabulary and Business Rules) based BR’s based on a specified query. We integrate conventional Information Retrieval Approach of text based searches over the rule base and corresponding meta-data with a SMT (Satisfiability Modulo Theory) based approach capturing the higher first order logic of the rules. The major applications of this approach are change impact analysis when rules are added, deleted or modified from a rule set, identifying the candidate set of rules affected due
to change in the rule set and during match and gap analysis where we compare two sets of BR’s identifying similarity and difference in business functionality between them. We show the implementation of our tool along with its performance on industry level datasets.
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