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Cluster Computing

, Volume 13, Issue 3, pp 257–276 | Cite as

Distributing and searching concept hierarchies: an adaptive DHT-based system

  • Athanasia Asiki
  • Dimitrios Tsoumakos
  • Nectarios Koziris
Article

Abstract

Concept hierarchies greatly help in the organization and reuse of information and are widely used in a variety of information systems applications. In this paper, we describe a method for efficiently storing and querying data organized into concept hierarchies and dispersed over a DHT. In our method, peers individually decide on the level of indexing according to the granularity of the incoming queries. Roll-up and drill-down operations are performed on a per-node basis in order to minimize the required bandwidth for answering queries on variable aggregation levels. We motivate our approach by applying it on a large-scale Grid system: Specifically, we apply our fully decentralized scheme that creates, queries and updates large volumes of hierarchical data on-line and replace the traditional centralized and strictly indexed information systems. Our extensive experimental results support this argument on many diverse configurations: Our system proves very efficient in skewed workloads, both over single and multiple hierarchy levels at the same time. It adapts to sudden changes in popularity and effectively stores and updates large amounts of data at very low cost.

Keywords

Distributed hash table Concept hierarchies Adaptive indexing Grid information system 

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References

  1. 1.
  2. 2.
    Ganglia Monitoring System. http://ganglia.info/
  3. 3.
    GT Information Services: Monitoring and Discovery System (MDS). http://www.globus.org/toolkit/mds/
  4. 4.
    Hawkeye: A Monitoring and Management Tool for Distributed Systems. http://www.cs.wisc.edu/condor/hawkeye/
  5. 5.
    R-GMA: Relational Grid Monitoring Architecture. http://www.r-gma.org/
  6. 6.
    The Globus Toolkit. http://www.globus.org/
  7. 7.
    Aberer, K., Cudre-Mauroux, P., Hauswirth, M.: The chatty web: emergent semantics through gossiping. In: WWW Conference (2003) Google Scholar
  8. 8.
    Aberer, K., Cudre-Mauroux, P., Hauswirth, M., Pelt, T.V.: Gridvine: building internet-scale semantic overlay networks. In: International Semantic Web Conference (2004) Google Scholar
  9. 9.
    OLAP Council, APB- 1 OLAP Benchmark. http://www.olapcouncil.org/research/resrchly.htm
  10. 10.
    Ester, M., Kohlhammer, J., Kriegel, P.: The dc-tree: a fully dynamic index structure for data warehouses. In: ICDE (2000) Google Scholar
  11. 11.
    Byrom, B. et al.: Apel: an implementation of grid accounting using r-gma. In: UK e-Science All Hands Conference (2005) Google Scholar
  12. 12.
  13. 13.
    Huebsch, R., Hellerstein, J.M., Lanham, N.L., Boon, T., Shenker, S., Stoica, I.: Querying the internet with PIER. In: VLDB (2003) Google Scholar
  14. 14.
    Kantere, V., Tsoumakos, D., Sellis, T., Roussopoulos, N.: GrouPeer: dynamic clustering of P2P databases. Inf. Syst. 34(1), 62–86 (2009) CrossRefGoogle Scholar
  15. 15.
    Koloniari, G., Pitoura, E.: Content-based routing of path queries in peer-to-peer systems. In: EDBT (2004) Google Scholar
  16. 16.
    Lakshmanan, L., Pei, J., Zhao, Y.: QC-trees: an efficient summary structure for semantic OLAP. In: SIGMOD (2003) Google Scholar
  17. 17.
    Ng, W.S., Ooi, B.C., Tan, K.L., Zhou, A.: PeerDB: a P2P-based system for distributed data sharing. In: ICDE (2003) Google Scholar
  18. 18.
    Sismanis, Y., Deligiannakis, A., Kotidis, Y., Roussopoulos, N.: Hierarchical dwarfs for the rollup cube. In: DOLAP (2003) Google Scholar
  19. 19.
    Tang, C., Xu, Z., Dwarkadas, S.: Peer-to-peer information retrieval using self-organizing semantic overlay networks. In: SIGCOMM (2003) Google Scholar
  20. 20.
    Tatarinov, I., Halevy, A.: Efficient query reformulation in peer-data management systems. In: SIGMOD (2004) Google Scholar
  21. 21.
    Wang, W., Lu, H., Feng, J., Yu, J.X.: Condensed cube: an effective approach to reducing data cube size. In: ICDE (2002) Google Scholar
  22. 22.
    Zhang, X., Freschl, J., Schopf, J.: Scalability analysis of three monitoring and information systems: MDS2, R-GMA, and Hawkeye. J. Parallel Distrib. Comput. 67(8), 883–902 (2007) zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Athanasia Asiki
    • 1
  • Dimitrios Tsoumakos
    • 1
  • Nectarios Koziris
    • 1
  1. 1.Computing Systems LaboratorySchool of Electrical and Computer Engineering, National Technical University of AthensAthensGreece

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