Skip to main content
SpringerLink
Log in

Discovering Contextual Association Rules in Relational Databases

  • Conference paper
New Trends in Databases and Information Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 241))

  • 1401 Accesses

Abstract

Contextual association rules represent co-occurrences between contexts and properties of data, where the context is a set of environmental or user personal features employed to customize an application. Due to their particular structure, these rules can be very tricky to mine, and if the process is not carried out with care, an unmanageable set of not significant rules may be extracted. In this paper we survey two existing algorithms for relational databases and present a novel algorithm that merges the two proposals overcoming their limitations.

This research has been partially funded by the European Commission, Programme IDEAS ERC, Project 227977-SMScom and by the Italian project Industria 2015, Program no. MI01 00091 SENSORI.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Appice, A., Berardi, M., Ceci, M., Malerba, D.: Mining and filtering multi-level spatial association rules with ARES. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 342–353. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on context-aware systems. Int. Journal of Ad Hoc and Ubiquitous Computing 2(4), 263–277 (2007)

    Article  Google Scholar 

  3. Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P., Marchetti, M.: Context-aware user and service profiling by means of generalized association rules. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009, Part II. LNCS, vol. 5712, pp. 50–57. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Bolchini, C., Curino, C., Quintarelli, E., Schreiber, F.A., Tanca, L.: A data-oriented survey of context models. SIGMOD Record 36(4), 19–26 (2007)

    Article  Google Scholar 

  5. Bolchini, C., Quintarelli, E., Tanca, L.: CARVE: Context-aware automatic view definition over relational databases. Inf. Syst. 38(1), 45–67 (2013)

    Article  Google Scholar 

  6. Cremonesi, P., Garza, P., Quintarelli, E., Turrin, R.: Top-N recommendations on unpopular items with contextual knowledge. In: Proc. of CARS. CEUR-WS.org (2011)

    Google Scholar 

  7. Goethals, B., Laurent, D., Le Page, W.: Discovery and application of functional dependencies in conjunctive query mining. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DAWAK 2010. LNCS, vol. 6263, pp. 142–156. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Goethals, B., Le Page, W., Mannila, H.: Mining association rules of simple conjunctive queries. In: Proc. of SDM, pp. 96–107. SIAM, Philadelphia (2008)

    Google Scholar 

  9. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  10. Leung, C.K.-S., Lakshmanan, L.V.S., Ng, R.T.: Exploiting succinct constraints using FP-trees. SIGKDD Explorations 4(1), 40–49 (2002)

    Article  Google Scholar 

  11. Miele, A., Quintarelli, E., Rabosio, E., Tanca, L.: A data-mining approach to preference-based data ranking founded on contextual information. Inf. Syst. 38(4), 524–544 (2013)

    Article  Google Scholar 

  12. Miele, A., Quintarelli, E., Tanca, L.: A methodology for preference-based personalization of contextual data. In: Proc. of EDBT, pp. 287–298. ACM Press, New York (2009)

    Chapter  Google Scholar 

  13. Pei, J., Han, J., Lakshmanan, L.V.S.: Pushing convertible constraints in frequent itemset mining. Data Min. Knowl. Discov. 8(3), 227–252 (2004)

    Article  MathSciNet  Google Scholar 

  14. Stefanidis, K., Pitoura, E., Vassiliadis, P.: Managing contextual preferences. Inf. Syst. 36(8), 1158–1180 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Elisa Quintarelli & Emanuele Rabosio

Authors
  1. Elisa Quintarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emanuele Rabosio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elisa Quintarelli .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica Bioingegneria, Robotica e, Università di Genova, Genova, Italy

    Barbara Catania

  2. Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy

    Tania Cerquitelli

  3. Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy

    Silvia Chiusano

  4. Dipartimento di Informatica, Bioingegneria, Robotica e, Università di Genova, Genova, Italy

    Giovanna Guerrini

  5. Cloudera, Inc., California,, California, USA

    Mirko Kämpf

  6. Faculty of Informatics, Technische Universität München, Garching, Germany

    Alfons Kemper

  7. Dept. of Analytical Information Systems, Saint Petersburg University, Saint Petersburg, Russia

    Boris Novikov

  8. Dipartimento di Ingegneria e Scienza, dell’Informazione, ItalyUniversità di Trento, Povo, TN,, Italy

    Themis Palpanas

  9. Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Praha, Praha, Czech Republic

    Jaroslav Pokorný

  10. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Athena Vakali

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Quintarelli, E., Rabosio, E. (2014). Discovering Contextual Association Rules in Relational Databases. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-01863-8_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01862-1

  • Online ISBN: 978-3-319-01863-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation