The set of BR supporting data quality grows over time and the amount of manual processing becomes huge and time consuming. In the current context, the BR is manually read, disambiguated and converted to DEMS Agent BR format for further operations. However, the complexity of the description of the BR due to ambiguities of NL can induce a deviation in the operationalization of the DEF. DEF is supported by the DEMS platform shown in Fig.1. The DEF describes the methodology, processes and roles required to generate business value while improving business processes using data quality and BR. The paper ends in Section V with conclusions and future work. Section III describes the whole process of the methodology along with a running example. The reader is introduced to the basic principles behind the DEF, SBVR, MDA and model transformations. The remaining paper is structured into the following sections: Section II briefly introduces the underpinning technologies used in our approach.
One motivation of this framework is to provide support for the Data Excellence Management System (DEMS) platform driven by the Data Excellence Framework (DEF). Since SBVR is the NL starting point for the Model Driven Architecture (MDA) process, the MDA model transformations are used to transform SBVR models towards executable models. The resulting SBVR BR is mapped to SBVR models through semantic formulations. The presented methodology follows a pipeline architecture defined in two steps: Firstly, the NL BR is linguistically analyzed using the Micro-Systemic Linguistic Analysis (MSLA) methodology and automatically transformed to SBVR BR to overcome the syntactic inconsistencies and semantic ambiguities involved in the NL representation. Based on a fusion of linguistics, logic, and computer science, SBVR provides a way to capture specifications in NL and represent them in formal logic so they can be machine-processed. Our idea is motivated by the birth of the Semantics of Business Vocabulary and Business Rules (SBVR) standard which is a Semantic Metamodel (SMM), for specifying semantic models of business using NL.
Its contribution is to provide a complete framework which takes as input the BR written in natural language (NL) (such as English or French) and provides a set of linguistic analyses and automated transformations into executable models for IT people.
Thus, this paper aims at bridging the gap between business and IT people in order to minimize the loss of semantics and overcome the huge software failures due to miscommunication.
The expression of requirements in any human language is unavoidably ambiguous and most of the time incomplete. However, transferring the business semantics from business people to IT people can introduce inconsistencies. Thus they currently rely on technical experts (IT) to model their business requirements expressed as a set of business rules (BR). However, designing, refining and maintaining an IS requires some technical or programming skills that stakeholders do not have in general. Information Systems (IS) play an important role in business because they implement the business process.