Designing data marts for data warehouses

Abstract

Data warehouses are databases devoted to analytical processing. They are used to support decision-making activities in most modern business settings, when complex data sets have to be studied and analyzed. The technology for analytical processing assumes that data are presented in the form of simple data marts, consisting of a well-identified collection of facts and data analysis dimensions (star schema). Despite the wide diffusion of data warehouse technology and concepts, we still miss methods that help and guide the designer in identifying and extracting such data marts out of an enterprisewide information system, covering the upstream, requirement-driven stages of the design process. Many existing methods and tools support the activities related to the efficient implementation of data marts on top of specialized technology (such as the ROLAP or MOLAP data servers). This paper presents a method to support the identification and design of data marts. The method is based on three basic steps. A first top-down step makes it possible to elicit and consolidate user requirements and expectations. This is accomplished by exploiting a goal-oriented process based on the Goal/Question/Metric paradigm developed at the University of Maryland. Ideal data marts are derived from user requirements. The second bottom-up step extracts candidate data marts

Data Warehousing: A Practical Managerial Approach

“…Some good questions you may want to ask decision makers are which reports or types of information are 1) fairly routine, 2) time-consuming and, 3) (Kimball 2002). …”

Decisions and Decision Requirements for Data Warehouse Systems

“…Goal oriented data warehouse development approaches of [6,7] and [16] reach data warehouse contents directly from goals without an explicit decisional stage. On the other hand, [15] recognizes the need to do further analysis from the decisional point of view.…”

“…Today, there is a body of opinion that uses goal oriented techniques [10,11,13,15,16] for determining data warehouse structure. One goal-oriented approach [10,11,13] is based on the notion of the GoalDecision-Information diagram.…”

A Comprehensive Framework on Multidimensional Modeling

“…Nevertheless, a requirement analysis phase is crucial to meet the user needs and expectations [3,5,22,15,11,1]. Otherwise, the user may find himself frustrated since he / she would not be able to analyze data of his / her interest, entailing the failure of the whole system.…”