New Data Types and Operations to Support Geo-streams

  • Yan Huang
  • Chengyang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5266)


The volume of real-time streaming data produced by geo-referenced sensors and sensor networks is staggeringly large and growing rapidly. Queries on these geo-streams often require tracking spatio-temporal extent (e.g. evolving region) continuously in real time. The notion of real-time monitoring and notification requires support from a database capable of tracking and querying dynamic and transient spatio-temporal events as well as static spatial objects and sending out real-time notifications. In this paper, we leverage the work in data type based spatio-temporal databases and propose new data types called STREAM and their abstract semantics to support geo-stream applications. New operations on STREAM data types are defined and illustrated by embedding them into SQL.


Data Type Continuous Query Abstract Semantic Spatial Predicate Move Object Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
  2. 2.
    Abadi, D.J., Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.: Aurora: a new model and architecture for data stream management. The VLDB Journal 12(2), 120–139 (2003)CrossRefGoogle Scholar
  3. 3.
    Ali, M.H., Aref, W.G., Bose, R., Elmagarmid, A.K., Helal, A., Kamel, I., Mokbel, M.F.: Nile-pdt: a phenomenon detection and tracking framework for data stream management systems. In: VLDB 2005: Proceedings of the 31st international conference on Very large data bases. VLDB Endowment, pp. 1295–1298 (2005)Google Scholar
  4. 4.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S.R., Reiss, F., Shah, M.A.: Telegraphcq: continuous dataflow processing. In: SIGMOD 2003: Proceedings of the 2003 ACM SIGMOD international conference on Management of data, p. 668. ACM Press, New York (2003)CrossRefGoogle Scholar
  5. 5.
    Chen, J., DeWitt, D.J., Tian, F., Wang, Y.: Niagaracq: a scalable continuous query system for internet databases. SIGMOD Rec. 29(2) (2000)Google Scholar
  6. 6.
    Considine, J., Li, F., Kollios, G., Byers, J.: Approximate aggregation techniques for sensor databases. In: Proceedings of the 20th International Conference on Data Engineering (2004)Google Scholar
  7. 7.
    Deshpande, A., Guestrin, C., Madden, S.R., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In: Proceedings of VLDB, pp. 588–599 (2004)Google Scholar
  8. 8.
    Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. In: SIGMOD 2000: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 319–330. ACM Press, New York (2000)CrossRefGoogle Scholar
  9. 9.
    Grumbach, S., Rigaux, P., Segoufin, L.: The dedale system for complex spatial queries. In: SIGMOD 1998: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp. 213–224. ACM Press, New York (1998)CrossRefGoogle Scholar
  10. 10.
    Guibas, L.: Kinetic data structures: A state of the art report. In: The 3rd Workshop on Algorithmic Foundations of Robotics (1998)Google Scholar
  11. 11.
    Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. 25(1), 1–42 (2000)CrossRefGoogle Scholar
  12. 12.
    Güting, R.H., de Almeida, V.T., Ansorge, D., Behr, T., Ding, Z., Höse, T., Hoffmann, F., Spiekermann, M., Telle, U.: Secondo: An extensible dbms platform for research prototyping and teaching. In: ICDE 2005: Proceedings of the 21st International Conference on Data Engineering, pp. 1115–1116. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  13. 13.
    Guting, R.H., Schneider, M.: Moving Objects Databases . Morgan Kaufmann, San Francisco (2005)Google Scholar
  14. 14.
    Jain, N., Amini, L., Andrade, H., King, R., Park, Y., Selo, P., Venkatramani, C.: Design, implementation, and evaluation of the linear road bnchmark on the stream processing core. In: SIGMOD 2006: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 431–442. ACM Press, New York (2006)CrossRefGoogle Scholar
  15. 15.
    Mokbel, M.F., Aref, W.G.: Sole: scalable on-line execution of continuous queries on spatio-temporal data streams. VLDB Journal (accepted for publication, 2008)Google Scholar
  16. 16.
    Mokbel, M.F., Xiong, X., Aref, W.G.: Sina: scalable incremental processing of continuous queries in spatio-temporal databases. In: SIGMOD 2004: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 623–634 (2004)Google Scholar
  17. 17.
    Relly, L., Röhm, U.: Plug and play: Interoperability in concert. In: Včkovski, A., Brassel, K.E., Schek, H.-J. (eds.) INTEROP 1999. LNCS, vol. 1580, pp. 277–291. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  18. 18.
    Systems, S.: StreamBase Server,
  19. 19.
    Tucker, P.A., Maier, D., Sheard, T., Fegaras, L.: Exploiting punctuation semantics in continuous data streams. IEEE Transactions on Knowledge and Data Engineering 15(3), 555–568 (2003)CrossRefGoogle Scholar
  20. 20.
    Yiu, M.L., Mamoulis, N., Bakiras, S.: Retrieval of spatial join pattern instances from sensor networks. In: SSDBM, p. 25 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yan Huang
    • 1
  • Chengyang Zhang
    • 1
  1. 1.University of North Texas 

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