Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu


  • Matteo GolfarelliEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80662


Data mining on top of data warehouse systems; OLAM


The term Online Analytical Mining, coined in 1997 by J. Han [9], refers to solutions that integrate online analytical processing (OLAP) with data mining functionalities so that mining can be performed in different portions of databases or data warehouses and at different levels of abstraction at the user’s fingertips. In such a system, data mining techniques will beneficiate of a higher level of integration, consistency, and cleanness, and data warehouse users will be able to express more powerful queries directly from their user interface. Although no commercial tools make available a complete and integrated set of OLAM features, many data mining techniques have been extended to deal with specific data warehouse features, while new algorithms, that specifically address the OLAP user’s advanced requirements, have been developed.

Historical Background

OLAM originated from the coupling of OLAP and data mining systems....

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Abdelbaki W, Yahia SB, Messaoud RB. NAP-SC: a neural approach for prediction over sparse cubes. In: Proceedings of the 8th International Conference on Advanced Data Mining and Applications; 2012. p. 340–52.CrossRefGoogle Scholar
  2. 2.
    Aligon J, Golfarelli M, Marcel P, Rizzi S. Similarity measures for OLAP sessions. Int J Knowl Inf Syst. 2014;39(2):463–89.CrossRefGoogle Scholar
  3. 3.
    Biondi P, Golfarelli M, Rizzi S. myOLAP: an approach to express and evaluate OLAP preferences. IEEE Trans Knowl Data Eng. 2011;23(7):1050–64.CrossRefGoogle Scholar
  4. 4.
    Chiang JK, Chu C. Multidimensional multi-granularities data mining for discover association rule. Trans Mach Learn Artif Intell. 2014;2(3):73–89.CrossRefGoogle Scholar
  5. 5.
    Chihcheng K, Li MZ. Techniques for finding similarity knowledge in OLAP reports. Expert Syst Appl. 2011;38(4):3743–56.CrossRefGoogle Scholar
  6. 6.
    Cuzzocrea A. An OLAM-based framework for complex knowledge pattern discovery in distributed-and-heterogeneous-data-sources and cooperative information systems. In: Proceedings of the 9th International Conference on Data Warehousing and Knowledge Discovery; 2007. p. 181–98.Google Scholar
  7. 7.
    Golfarelli M, Rizzi S. Honey, I Shrunk the Cube. In: Proceedings of the 17th East European Conference on Advances in Databases and Information Systems; 2013. p. 176–89.CrossRefGoogle Scholar
  8. 8.
    Golfarelli M, Turricchia E. A characterization of hierarchical computable distance functions for data warehouse systems. Decis Support Syst. 2014;62(June):144–57.CrossRefGoogle Scholar
  9. 9.
    Han J. OLAP mining: an integration of OLAP with data mining, In: Proceedings of the 7th IFIP 2.6 Working Conference on Database Semantics; 1997. p. 3–20.CrossRefGoogle Scholar
  10. 10.
    Han J, Fu Y, Wang W, Chiang J, et al. DBMiner: a system for mining knowledge in large relational databases. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining; 1996. p. 250–55.Google Scholar
  11. 11.
    Han J, Kamber M, Pei J. Data mining: concepts and techniques. 3rd ed. Waltham: Morgan Kaufmann; 2011.Google Scholar
  12. 12.
    Jensen M, Holmgren T, Pedersen T. Discovering multidimensional structure in relational data. In: Proceedings of the 6th International Conference on Data Warehousing and Knowledge Discovery; 2004. p. 138–48.CrossRefGoogle Scholar
  13. 13.
    Li Y, Wu J, Xu Y, Yang W. Granule mining oriented data warehousing model for representations of multidimensional association rules. Int J Intell Inf Database Syst. 2008;2(1):125–45.Google Scholar
  14. 14.
    Mansmann S, Rehman N, Weiler A, Scholl M. Discovering OLAP dimensions in semi-structured data. Inf Syst. 2014;44(Aug):120–33.CrossRefGoogle Scholar
  15. 15.
    Palpanas T, Koudas N, Mendelzon A. Using datacube aggregates for approximate querying and deviation detection. IEEE Trans Knowl Data Eng. 2005;17(11):1465–77.CrossRefGoogle Scholar
  16. 16.
    Psaila G, Lanzi P. Hierarchy-based mining of association rules in data warehouses. In: Proceedings of the 2000 ACM Symposium on Applied Computing; 2000. p. 307–12.Google Scholar
  17. 17.
    Usman M, Asghar S. An architecture for integrated online analytical mining. J Emerg Technol Web Intell. 2011;3(2):74–99.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.DISI – University of BolognaBolognaItaly

Section editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Stefano Rizzi
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.DISIUniv. of BolognaBolognaItaly