Discovering OLAP Dimensions in Semi-Structured Data

Zitieren

Dateien zu dieser Ressource

Prüfsumme: MD5:9ab457ab0e79f671a3f9487dca6d2809

MANSMANN, Svetlana, Nafees Ur REHMAN, Andreas WEILER, Marc H. SCHOLL, 2014. Discovering OLAP Dimensions in Semi-Structured Data. In: Information Systems. 44, pp. 120-133. ISSN 0306-4379. eISSN 0306-4379

@article{Mansmann2014Disco-25828, title={Discovering OLAP Dimensions in Semi-Structured Data}, year={2014}, doi={10.1016/j.is.2013.09.002}, volume={44}, issn={0306-4379}, journal={Information Systems}, pages={120--133}, author={Mansmann, Svetlana and Rehman, Nafees Ur and Weiler, Andreas and Scholl, Marc H.} }

Information Systems ; 44 (2014). - S. 120-133 2014-01-13T13:48:42Z deposit-license Scholl, Marc H. Weiler, Andreas Weiler, Andreas 2014-01-13T13:48:42Z eng 2014 OLAP cubes enable aggregation-centric analysis of transactional data by shaping data records into measurable facts with dimensional characteristics. A multidimensional view is obtained from the available data fields and explicit relationships between them. This classical modeling approach is not feasible for scenarios dealing with semi-structured or poorly structured data. We propose to the data warehouse design methodology with a content-driven discovery of measures and dimensions in the original dataset. Our approach is based on introducing a data enrichment layer responsible for detecting new structural elements in the data using data mining and other techniques. Discovered elements can be of type measure, dimension, or hierarchy level and may represent static or even dynamic properties of the data. This paper focuses on the challenge of generating, maintaining, and querying discovered elements in OLAP cubes.<br /><br /><br /><br />We demonstrate the power of our approach by providing OLAP to the public stream of user-generated content on the Twitter platform. We have been able to enrich the original set with dynamic characteristics, such as user activity, popularity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional models from relational or semi-structured data. Scholl, Marc H. Discovering OLAP Dimensions in Semi-Structured Data Rehman, Nafees Ur Mansmann, Svetlana Mansmann, Svetlana Rehman, Nafees Ur

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

Mansmann_258286.pdf 207

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto