International Journal of Computer Applications (0975 – 8887) Volume 70– No.16, May 2013 20 An Agent Oriented Approach of Requirement Engineering in Developing a Data Ware Houses for Banking System Sandeep Mathur Girish Sharma A K Soni ABSTRACT Most of the data ware house project fails to meet the business requirements and business goals because of the improper requirement engineering phase. The chaos all through the development of requirements evolves due to disparity between users and developers resulting in project devastations and terminations. Building a data warehouse is a very challenging task. Data warehouse quality depends on the quality of its requirement engineering models. Agent orientation is emerging as a unique paradigm for constructing Data ware house. Agent oriented systems are expected to be more powerful, more flexible, and more robust than conventional software systems. In this paper the detail discussion of agent oriented methodology used in early as well as late requirement elicitation. The proposed approach is illustrated through an case study of the general banking system for which Data Ware house is to be built to support decisional goals. Keywords—Data ware house (DWH), Requirement elicitation, agents, Goal decision information model (GDI), AGDI. 1. INTRODUCTION Critical business decisions depend upon the availability of proper strategic information in the enterprise [1], [5], [7]. Data Warehouse (DW) systems are used by decision makers to analyze. DW acknowledged as one of the most complex information system modules and its design and maintenance is characterized by several complexity factors. The solution of the aforesaid problem is data ware house. The data warehouse is primarily used for the decisional purposes and supports on-line analytical processing. The data in data warehouse is historical in nature available in very huge amount. Because of these basic requirements of a data warehouse system, the development of a data warehouse system is also different from the development of a conventional operational system. Therefore the data warehouse design process has not been supported by a formal requirements analysis method though there are some approaches for requirements gathering. Thus requirements engineering for the data warehouse aims to identifying the information needs of the decision-makers. In recent years, requirements engineering for DW has acquired importance. [7], [13], [17]. A relationship of the Data Warehouse to the organizational context is established at the requirements level. The requirements Engineering task has been divided into two phases: early requirements engineering phase and late requirements engineering phase [4], [23]. The early phase of requirements engineering activities include to consider how the intended system would meet organizational goals, why the system is needed, The emphasis here is on understanding the “whys” that underlies system requirements, rather than on the precise and detailed specification of “what” the system should do. The late requirement analysis describes the developing system within its operational environment along with its function and properties. Now this phase specifies what the system will do and how it will be done. 2. Overview of Goal decision Information model for DWH Requirement Engineering Goal– decision model covers both the analysis of initial requirements and the specification of these requirements in terms of a conceptual schema. Goal-oriented requirements analysis starts with a list of stakeholders and their high-level goals, which are refined and interrelated to produce a goal model in which only major stakeholders or decision making users are involved in requirement elicitation process [12] . Fig 1: GDI model Goal-oriented schema design is further divided into two Stages [17]: the modeling of the application domain which describes the necessary understanding of a part of the real world, and facilitates the communication of domain knowledge between developers, end-users and other stakeholders and a conceptual schema, that represents the semantics of the actual data in the proposed database; its design focuses on issues that are specific to the conceptual content and organization of the data.