OLAP Patterns: A Pattern-Based Approach to Multidimensional Data Analysis

Users of a business intelligence (BI) system employ an approach referred to as online analytical processing (OLAP) to view multidimensional data from different perspectives. Query languages, e.g., SQL or MDX, allow for flexible querying of multidimensional data but query formulation is often time-consuming and cognitively challenging for many users. Alternatives to using a query language, e.g., graphical OLAP clients, parameterized reports, or dashboards, are often not a full-blown alternative to using a query language. Experience in cooperative research projects with industry led to the following observations regarding the use of OLAP queries in practice. First, within the same organization, similar OLAP queries are repeatedly composed from scratch in order to satisfy similar information needs. Second, across different organizations and even domains, OLAP queries with similar structures are repeatedly composed from scratch. Finally, vague requirements regarding frequently composed OLAP queries in the early stages of a project potentially lead to rushed development in later stages, which can be alleviated by following best practices for OLAP query composition. In engineering, knowledge about best-practice solutions to frequently arising challenges is often documented and represented using patterns. In that spirit, an OLAP pattern describes a generic solution for composing a query that allows a BI user to satisfy a certain type of information need given fragments of a conceptual model. This paper introduces a formal definition of OLAP patterns as well as an expressive, flexible, and generally applicable definition language.

Keywords: Business intelligence, Conceptual modeling, Solution pattern, Online analytical processing, Data warehouse