Encyclopedia of GIS

2008 Edition
| Editors: Shashi Shekhar, Hui Xiong

Top-k Retrieval Techniques in Distributed Sensor Systems

  • Song Lin
  • Demetrios Zeinalipour-Yazti
  • Dimitrios Gunopulos
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-35973-1_1395

Synonyms

Top-k query processing; Spatio-temporal similarity search

Definition

Fast developments in wireless technologies and microelectronics made it feasible to develop economically viable embedded sensor systems for monitoring and understanding the physical world [5]. Traditional monitoring approaches, like passive sensing devices, transmit their readings to a centralized processing unit for storage and analysis. Wireless Sensor Devices (WSDs)on the other hand, are tiny computers on a chip that is often no bigger than a coin or credit card. These devices, equipped with a low frequency processor (≈ 4–58 MHz) and a wireless radio, can sense parameters such as, light, sound, temperature, humidity, pressure, noise levels, movement, and many others at extremely high resolutions. The applications of sensor networks range from environment monitoring (such as atmosphere and habitant monitoring, seismic and structural monitoring) to...

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

Recommended Reading

  1. 1.
    Cao, P., Wang, Z.: Efficient top-k query calculation in distributed networks. In: PODC 04: Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing, pp. 206–215, St. John's, Newfoundland, Canada, 25–28 July 2004Google Scholar
  2. 2.
    Fagin, R.: Combining fuzzy information from multiple systems (extended abstract). In: PODS 96: Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, pp. 216–226, Montreal, Canada, 3–5 June 1996Google Scholar
  3. 3.
    Hadjieleftheriou, M., Kollios, G., Bakalov, P., Tsotras, V.J.: Complex spatio‐temporal pattern queries. In: VLDB 05: Proceedings of the 31st international conference on Very large data bases, pp. 877–888, Trondheim, Norway, 30 Aug–2 Sept 2005Google Scholar
  4. 4.
    Kollios, G., Gunopulos, D., Tsotras, V.J.: On indexing mobile objects. In: PODS 99: Proceedings of the eighteenth ACM SIGMODSIGACT- SIGART symposium on Principles of database systems, pp. 261–272, Philadelphia, Pennsylvania, 31 May–2 June 1999CrossRefGoogle Scholar
  5. 5.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: a tiny aggregation service for ad-hoc sensor networks. ACM SIGOPS Operating Systems Review 36(SI), 131–146 (2002)Google Scholar
  6. 6.
    Marian, A., Bruno, N., Gravano, L.: Evaluating top-k queries over web‐accessible databases. ACM Transactions on Database Systems 29(2), 319–362 (2004)CrossRefGoogle Scholar
  7. 7.
    Michel, S., Triantafillou, P., Weikum, G.: Klee: a framework for distributed top-k query algorithms. In VLDB 05: Proceedings of the 31st international conference on Very large data bases, pp. 637–648, Trondheim, Norway, 30 Aug–2 Sept 2005Google Scholar
  8. 8.
    Nieto, M.: Public video surveillance: Is it an effective crime prevention tool? Technical report, California Research Bureau Report, June 1997Google Scholar
  9. 9.
    Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: SIGMOD 00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 331–342, Dallas, Texas, 16–18 May 2000Google Scholar
  10. 10.
    Tao, Y., Sun, J., Papadias, D.: Analysis of predictive spatio‐temporal queries. ACM Trans Database Syst 28(4), 295–336 (2003)CrossRefGoogle Scholar
  11. 11.
    Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multi‐dimensional time-series with support for multiple distance measures. In KDD 03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 216–225, Washington, D.C., USA, 24–27 Aug 2003Google Scholar
  12. 12.
    Zeinalipour-Yazti, D., Lin, S., Gunopulos, D.: Distributed spatio‐temporal similarity search. In: CIKM 06: Proceedings of the 15th ACM international conference on Information and knowledge management, pp. 14–23, Arlington, VA, USA, 6–11 Nov 2006Google Scholar
  13. 13.
    Zeinalipour-Yazti, D., Vagena, Z., Gunopulos, D., Kalogeraki, V., Tsotras, V., Vlachos, M., Koudas, N., Srivastava, D.: The threshold join algorithm for top-k queries in distributed sensor networks. In: DMSN 05: Proceedings of the 2nd international workshop on Data management for sensor networks, pp. 61–66, Trondheim, Norway, 29 Aug 2005Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Song Lin
    • 1
  • Demetrios Zeinalipour-Yazti
    • 2
  • Dimitrios Gunopulos
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.School of Pure and Applied SciencesOpen University of CyprusNicosiaCyprus