Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database Systems for Advanced Applications

DASFAA 2010: Database Systems for Advanced Applications pp 45–56Cite as

SGDB – Simple Graph Database Optimized for Activation Spreading Computation

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6193))

Abstract

In this paper, we present SGDB, a graph database with a storage model optimized for computation of Spreading Activation (SA) queries. The primary goal of the system is to minimize the execution time of spreading activation algorithm over large graph structures stored on a persistent media; without pre-loading the whole graph into the memory. We propose a storage model aiming to minimize number of accesses to the storage media during execution of SA and we propose a graph query type for the activation spreading operation. Finally, we present the implementation and its performance characteristics in scope of our pilot application that uses the activation spreading over the Wikipedia link graph.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amann, M., Scholl, B.: Gram: A graph data model and query language. In: Proceedings of the European Conference on Hypertext Technology (ECHT), pp. 201–211. ACM, New York (1992)

    Google Scholar 

  2. Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1–39 (2008)

    Article  Google Scholar 

  3. Berthold, M.R., Brandes, U., Kötter, T., Mader, M., Nagel, U., Thiel, K.: Pure spreading activation is pointless. In: CIKM 2009: Proceeding of the 18th ACM conference on Information and knowledge management, pp. 1915–1918. ACM, New York (2009)

    Chapter  Google Scholar 

  4. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD 2008: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp. 1247–1250. ACM, New York (2008)

    Chapter  Google Scholar 

  5. Ciglan, M., Rivière, E., Nørvåg, K.: Learning to find interesting connections in wikipedia. In: Proceeding of APWeb 2010 (2010)

    Google Scholar 

  6. Crestani, F.: Application of spreading activation techniques in information retrieval. Artif. Intell. Rev. 11(6), 453–482 (1997)

    Article  Google Scholar 

  7. Erling, O., Mikhailov, I.: RDF support in the virtuoso DBMS. In: Conference on Social Semantic Web. LNI, vol. 113, pp. 59–68. GI (2007)

    Google Scholar 

  8. Gyssens, M., Paredaens, J., Gucht, D.V.: A graph-oriented object model for database end-user. In: Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, pp. 24–33. ACM Press, New York (1990)

    Chapter  Google Scholar 

  9. Hidders, J.: A graph-based update language for object-oriented data models. Ph.D. dissertation. Technische Universiteit Eindhoven (2001)

    Google Scholar 

  10. Kang, U., Tsourakakis, C.E., Faloutsos, C.: PEGASUS: A peta-scale graph mining system implementation and observations. In: Ninth IEEE International Conference on Data Mining, ICDM 2009, December 2009, pp. 229–238 (2009)

    Google Scholar 

  11. Kiryakov, A., Ognyanov, D., Manov, D.: OWLIM - a pragmatic semantic repository for OWL. In: Proc. Workshop Scalable Semantic Web Knowledge Base Systems

    Google Scholar 

  12. Levene, M., Poulovassilis, A.: The hypernode model and its associated query language. In: Proceedings of the 5th Jerusalem Conference on Information technology, pp. 520–530. IEEE Computer Society Press, Los Alamitos (1990)

    Chapter  Google Scholar 

  13. Mainguenaud, M.: Simatic XT: A data model to deal with multi-scaled networks. Comput. Environ. Urban Syst. 16, 281–288 (1992)

    Article  Google Scholar 

  14. Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: PODC 2009: Proceedings of the 28th ACM symposium on Principles of distributed computing, p. 6. ACM, New York (2009)

    Chapter  Google Scholar 

  15. Mehler, A.: Text linkage in the wiki medium: A comparative study. In: Proceedings of the EACL 2006 Workshop on New Text: Wikis and Blogs and Other Dynamic Text Sources, pp. 1–8 (2006)

    Google Scholar 

  16. Paredaens, J., Peelman, P., Tanca, L.: G-Log: A graph-based query language. IEEE Trans. Knowl. Data Eng. 7, 436–453 (1995)

    Article  Google Scholar 

  17. Rohloff, K., Dean, M., Emmons, I., Ryder, D., Sumner, J.: An evaluation of triple-store technologies for large data stores. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2007, Part II. LNCS, vol. 4806, pp. 1105–1114. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Troussov, A., Sogrin, M., Judge, J., Botvich, D.: Mining socio-semantic networks using spreading activation technique. In: International Workshop on Knowledge Acquisition from the Social Web, KASW 2008 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dep. of Computer and Information Science, NTNU, Trondheim, Norway

    Marek Ciglan & Kjetil Nørvåg

Authors
  1. Marek Ciglan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kjetil Nørvåg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo, 606-8501, Kyoto, Japan

    Masatoshi Yoshikawa

  2. Information School, Renmin University of China, 100872, Beijing, China

    Xiaofeng Meng

  3. Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, 671-2280, Hyogo, Japan

    Takayuki Yumoto

  4. Graduate School of Informatics, Kyoto University, Yoshidahonmachi, Sakyo, 606-8501, Kyoto, Japan

    Qiang Ma

  5. Institute of HCI and Media Integration, Tsinghua University, 100084, Bejing, China

    Lifeng Sun

  6. Department of Information Science, Ochanomizu University, 2-1-1, Otsuka, Bunkyo-ku, 112-8610, Tokyo, Japan

    Chiemi Watanabe

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ciglan, M., Nørvåg, K. (2010). SGDB – Simple Graph Database Optimized for Activation Spreading Computation. In: Yoshikawa, M., Meng, X., Yumoto, T., Ma, Q., Sun, L., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 6193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14589-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14589-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14588-9

  • Online ISBN: 978-3-642-14589-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation