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 The editorial processing for this paper was managed by Axel van Lamsweerde.

Data warehouse views typically store large aggregate tables based on a subset of dimension attributes of the main data warehouse fact table. Aggregate views can be stored as 2 n subviews of a data cube with n attributes. Methods have been proposed for selecting only some of the data cube views to materialize in order to speed up query response time, accommodate storage space constraint and reduce warehouse maintenance cost. This paper proposes a method for selecting and materializing views, which selects and horizontally fragments a view, recomputes the size of the stored partitioned view while deciding further views to select. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Data warehouse; Views; Fragmentation; Performance benet 1. Introduction Decision support systems (DSS) used by business executives require analyzing snapshots of departmental databases over several periods of time. Departmental databases of the same organization (e.g., a bank) may be stored on dier...

Data Warehouse views in relational OLAP (online analytical processing) are aggregation or summary tables which hold millions of tuples describing activities that occurred on various departmental source databases over long periods of time. An n-dimensional data cube is a multidimensional data model used to generate n different perspectives of the measure aggregates (e.g., SUM, MAX) of interest in a warehouse, and has 2 subviews.

Storing vast number of aggregate tables (materialized views) of the base data collected from its various independent data sources is one way warehousing systems provide fast access to data requested by complex warehouse queries. A data warehouse collects, stores and integrates large amounts of data from various function oriented databases over a long period of time which is used for online analytical processing (OLAP). In addition to storing views which project mostly on primary key attributes (e.g., customerid), materializing some of their indexes help reduce query response time at the expense of increasing maintenance cost for stored tables and diminishing storage space. Thus, in order to achieve near optimal query response time, maintenance cost and storage space utilization, schemes that enable careful selection of views and indexes are required. For an even better system performance, extending these selection schemes to accommodate views that are grouped on dimension at...