Online analytical processing (OLAP) allows domain experts to gain insights into a subject of analysis. Domain experts are often casual users who interact with OLAP systems using standardized reports covering most of the domain experts? information needs. Analytical questions not answered by standardized reports must be posed as ad hoc queries. Casual users, however, are typically not familiar with OLAP data models and query languages, preferring to formulate questions in business terms. Experience from industrial research projects shows that ad hoc queries frequently follow certain patterns which can be leveraged to provide assistance to domain experts. For example, queries in many domains focus on the relationships between a set of interest and a set of comparison. We propose a pattern framework which allows for the definition and usage of recurring patterns for data analysis. We illustrate the idea of OLAP patterns using the example of data analysis for precision dairy farming, where we successfully employed the pattern approach to allow veterinarians to flexibly pose interesting queries.
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