Using Temporal Semantics for Live Media Stream Queries

  • Bin Liu
  • Amarnath Gupta
  • Ramesh Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4254)


Querying live media streams is a challenging problem that becomes an essential requirement in a growing number of applications. We address the problem of evaluating continuous queries on media streams produced by media sources such as webcams and microphones. The temporal attributes and the order of stream tuples play essential roles in live stream generation and query execution. Furthermore, the temporal constraints and query semantics of related streams provide additional query optimization opportunities. We investigate the modeling issues and introduce the query processing techniques of a live media stream management system (MedSMan), including media capturing, automatic feature generating, declaration and query languages, temporal stream operators and querying algorithms. A prototype is implemented and we present experimental results to show the performance of our prototype using various real-time media experiments.


Query Execution Query Plan Continuous Query Media Stream Audio Clip 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, L., Pu, C., Tang, W.: Continual queries for internet scale event-driven information delivery. IEEE Transactions on Knowledge and Data Engineering 11(4), 610–628 (1999)CrossRefGoogle Scholar
  2. 2.
    Chen, J., DeWitt, D.J., Tian, F., Wang, Y.: Niagaracq: a scalable continuous query system for internet databases. In: International Conference on Management of Data, Dallas, Texas, pp. 379–390 (2000)Google Scholar
  3. 3.
    Abadi, D., Carney, D., Cetintemel, U., et al.: Aurora: A new model and architecture for data stream management. VLDB Journal 12(2), 120–139 (2003)CrossRefGoogle Scholar
  4. 4.
    Madden, S., Shah, M., Hellerstein, J.M., Raman, V.: Continously adaptive continous queries over streams. In: ACM SIGMOD International Conference on Management of Data, Madison, Wisconsin (2002)Google Scholar
  5. 5.
    Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Tan, K.-L., Franklin, M.J., Lui, J.C.-S. (eds.) MDM 2001. LNCS, vol. 1987, pp. 3–14. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Symposium on Principles of Database Systems, Madison, Wisconsin, pp. 1–16 (2002)Google Scholar
  7. 7.
    Liu, B., Gupta, A., Jain, R.: A live multimedia stream querying system. In: Proceedings of the 2nd international workshop on Computer Vision Meets Databases, pp. 35–42 (2005)Google Scholar
  8. 8.
    Golab, L., Ozsu, M.T.: Issues in data stream management. ACM SIGMOD Record 32(2), 5–14 (2003)CrossRefGoogle Scholar
  9. 9.
    Seshadri, P., Livny, M., Ramakrishnan, R.: Seq: A model for sequence databases. ICDE 232–239 (1995)Google Scholar
  10. 10.
    Seshadri, P., Livny, M., Ramakrishnan, R.: Sequence query processing. ACM SIGMOD 430–441 (1994)Google Scholar
  11. 11.
    Enderle, J., Hampel, M., Seidl, T.: Joining interval data in relational databases. In: Proc. ACM SIGMOD (2004)Google Scholar
  12. 12.
    Soo, M.D., Snodgrass, R.T., Jensen, C.S.: Efficient evaluation of the valid-time natural join. ICDE 282–292 (1994)Google Scholar
  13. 13.
    Lu, H., Ooi, B.C., Tan, K.L.: On spatially partitioned temporal join. VLDB 546–557 (1994)Google Scholar
  14. 14.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-Tree: An efficient and robust access method for points and rectangles. In: SIGMOD Conference, pp. 322–331 (1990)Google Scholar
  15. 15.
    Elmasri, R., Wuu, G.T.J., Kim, Y.J.: The time index: An access structure for temporal data. VLDB 1–12 (1990)Google Scholar
  16. 16.
    Liu, B., Gupta, A., Jain, R.: Medsman: a streaming data management system over live multimedia. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 171–180 (2005)Google Scholar
  17. 17.
    Tansel, A.U., Clifford, J., Gadia, S.K., Segev, A., Snodgrass, R.T.: Temporal Databases: Theory, Design, and Implementation. Benjamin/Cummings (1993)Google Scholar
  18. 18.
    Ramachandran, U., Lillethun, D., Liu, B., Nakazawa, J., Hilley, D., Horrigan, S., Cooper, B.: Mediabroker++: A platform for applications in a dynamic pervasive environment. International Conference on Distributed Computing Systems (submitted, 2005)Google Scholar
  19. 19.
    Arasu, A., Babu, S., Widom, J.: The cql continuous query language: Semantic foundations and query execution. Technical report, Stanford University (2003)Google Scholar
  20. 20.
    Kang, J., Naughton, J.F., Viglas, S.D.: Evaluating window joins over unbounded streams. In: Proc. of the 2003 Intl. Conf. on Data Engineering (2003)Google Scholar
  21. 21.
    Gunadhi, H., Segev, A.: Query processing algorithms for temporal intersection joins. ICDE 336–344 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bin Liu
    • 1
  • Amarnath Gupta
    • 2
  • Ramesh Jain
    • 3
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.San Diego Supercomputer CenterUniversity of California San DiegoLa JollaUSA
  3. 3.Department of Computer ScienceUniversity of California IrvineIrvineUSA

Personalised recommendations