Detecting summarizability in OLAP

Tapio Niemi, M. Niinimäki, Peter Thanisch, Jyrki Nummenmaa

    Research output: Contribution to journalReview Articlepeer-review

    9 Citations (Scopus)

    Abstract

    The industry trend towards self-service business intelligence is impeded by the absence, in commercially-available information systems, of automated identification of potential issues with summarization operations. Research on statistical databases and on data warehouses have both produced widely-accepted categorisations of measure attributes, the former based on general summarizability properties and the latter based on measures' additivity properties. We demonstrate that neither of these categorisations is an appropriate basis for precise identification of measure types since they are incomplete, ambiguous and insufficiently refined. Using a new categorisation of dimension types and multidimensional structures, we derive a measure categorisation which is a synthesis and a refinement of the two aforementioned categorisations. We give formal definitions for our summarizability types, based on the relational model of data, and then construct rules for correct summarization by using these definitions. We also give a method to detect whether a given MDX OLAP query conforms to those rules.

    Original languageEnglish
    Pages (from-to)1-20
    Number of pages20
    JournalData and Knowledge Engineering
    Volume89
    DOIs
    Publication statusPublished - 2014
    Publication typeA2 Review article in a scientific journal

    Keywords

    • Additivity
    • Business intelligence
    • Database design
    • Modelling and management
    • OLAP
    • Summarizability

    Publication forum classification

    • Publication forum level 2

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