Eliciting Data Warehouse Contents for Policy Enforcement Rules

Eliciting Data Warehouse Contents for Policy Enforcement Rules

Deepika Prakash (Delhi Technological University, Delhi, India) and Daya Gupta (Delhi Technological University, Delhi, India)
Copyright: © 2014 |Pages: 29
DOI: 10.4018/ijismd.2014040103
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Abstract

Data Warehouse requirements engineering has been extensively looked at from the ENDS perspective of the Business Motivation Model, in terms of goals the system to-be wants to achieve. The authors propose that the MEANS perspective of this Model can drive the requirements engineering process. MEANS are organized into business policies and ‘policy enforcement rules'. Starting from policies expressed in a higher order logic, the authors propose an approach to formulate policy enforcement rules. That subset of the set of formulated policy enforcement rules which is most appropriate for the business is to be selected. For this, the information relevant to the rules is to be kept in the Data Warehouse. The authors technique picks up the components of the policy enforcement rule to elicit the information that has a bearing on its selection. The elicited information is represented as an ER diagram. The authors rely on existing methodologies to convert an ER form into star schemas. The authors use the medical domain to illustrate our methodology.
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Introduction

Data Warehouse Requirements Engineering, DWRE, is concerned with arriving at the information contents of the Data warehouse, DW, To-Be. The scope of DWRE is divided in two parts, organizational and technical. The organizational part deals with the role of the DW in the larger context of the business whereas the technical part looks at the requirements of the technical solution to be put in place. Taken holistically, DWRE spans across “business information” to be kept in the Data Warehouse and determining its facts and dimensions.

Consider the three life cycles of DW development described in (Prakash 2008), namely, data base driven, ER driven, and goal driven. The main task in the data base driven (Golfarelli, 1999) and ER driven (Hüsemann, 2000) life cycles is to restructure data bases and ER diagrams respectively to determine the required facts and dimensions. The Goal oriented approaches (Prakash, 2008; Boehnlein, 1999; Boehnlein, 2000; Bonifati, 2001; Giorgini, 2005; Prakash, 2003) explore system/organizational goals and determine star schemas. (Prakash, 2012) has introduced the notion of a target. Targets participate in two hierarchies, the relevance and fulfilment hierarchies. It has been shown that these hierarchies lead to determination of the information to be kept in the Data Warehouse To-Be. In (Prakash, 2012) the process of arriving at star schemas has been split into two parts (i) an ‘early information’ part where the information relevant to decision making is discovered and (ii) a ‘late’ part where the discovered information is structured as facts and dimensions. As explained in (Prakash, 2012) ‘early’ information is that which is in: an abstract, relatively fuzzy form devoid of any structure. Yet, all requirements for example, of history and aggregation are identified here.

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