Christoph Georg Schütz, Simon Schausberger, Ilko Kovacic, Michael Schrefl,
"Semantic OLAP Patterns: Elements of Reusable Business Analytics"
: Proceedings of the Confederated International Conferences On-the-Move 2017 (OTM 2017), October 23-27, 2017, Rhodes, Greece, Serie Lecture Notes in Computer Science (LNCS), Vol. 10574, Springer International Publishing, Seite(n) 318-336, 10-2017, ISBN: 978-3-319-69458-0
Original Titel:
Semantic OLAP Patterns: Elements of Reusable Business Analytics
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the Confederated International Conferences On-the-Move 2017 (OTM 2017), October 23-27, 2017, Rhodes, Greece
Original Kurzfassung:
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. This paper proposes a pattern definition framework which allows for a machine-readable representation of recurring, domain-Independent patterns in OLAP. Semantic web technologies serve for the Definition of OLAP patterns as well as the data models and business terms used to instantiate the patterns. Ad hoc query composition then amounts to selecting an appropriate pattern and instantiating that pattern by reference to semantic predicates that encode business terms. Pattern instances eventually translate into a target language, e.g., SQL.
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