Higher-Accuracy for Identifying Frequent Items over Real-Time Packet Streams

  • Ling Wang
  • Yang Koo Lee
  • Keun Ho Ryu
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)


In this paper, we classified the synopses data structure into two major types, the Equal Synopses and Unequal Synopses. Usually, a Top-k query is always processed over equal synopses, but Top-k query is very difficult to implement over unequal synopses because of resulting inaccurate approximate answers. Therefore, we present a Dynamic Synopsis which is developed by DSW (Dynamic Sub-Window) algorithm to support the processing of Top-k aggregate queries over unequal synopses and guarantee the accuracy of the approximation results. Our experiment results show that using Dynamic Synopses have significant performance benefits of improving the accuracy of approximation answers on real time traffic analyses over packet streaming networks.


sliding window Top-k frequent items dynamic synopses 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Golab, L., Ozsu, M.T.: Issues in data stream management. ACM SIGMOD Record 32(2), 5–14 (2003)CrossRefGoogle Scholar
  2. 2.
    Cranor, C., Gao, Y., Johnson, T., Shkapenyunk, V., Spatscheck, O.: Gigascope: High performance network monitoring with an SQL interface. In: 2002 ACM SIGMOD international conference on Management of data, p. 623. ACM Press, New York (2002)CrossRefGoogle Scholar
  3. 3.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data streams. In: 21st ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 1–16. ACM Press, New York (2002)Google Scholar
  4. 4.
    Cohen, S.: User-defined aggregate functions: bridging theory and practice. In: 2006 ACM SIGMOD international conference on Management of data, pp. 49–60. ACM Press, Chicago (2006)CrossRefGoogle Scholar
  5. 5.
    Li, J., Maier, D., Tufte, K., Papadimos, V., Tucker, P.A.: No pane, no gain: efficient evaluation of sliding-window aggregates over data streams. ACM SIGMOD Rocord 34(1), 39–44 (2005)CrossRefGoogle Scholar
  6. 6.
    Krishnamurthy, S., Wu, C., Franklin, M.J.: On-the-fly sharing for streamed aggregation. In: 2006 ACM SIGMOD international conference on Management of data, pp. 623–634. ACM Press, Chicago (2006)CrossRefGoogle Scholar
  7. 7.
    Toman, D.: On Construction of Holistic Synopses under the Duplicate Semantics of Streaming Queries. In: 14th International Symposium on Temporal Representation and Reasoning (TIME 2007), pp. 150–162. IEEE Press, Alicante (2007)CrossRefGoogle Scholar
  8. 8.
    Kyriakos, M., Spiridon, B., Dimitris, P.: Continuous monitoring for top-k queries over sliding windows. In: 2006 ACM SIGMOD international conference on Management of data, pp. 635–646. ACM Press, New York (2006)Google Scholar
  9. 9.
    Wang, L., Lee, Y.K., Ryu, K.H.: Supporting Top-k Aggregate Queries over Unequal Synopsis on Internet Traffic Stream. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds.) APWeb 2008. LNCS, vol. 4976, pp. 590–600. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ling Wang
    • 1
  • Yang Koo Lee
    • 1
  • Keun Ho Ryu
    • 1
  1. 1.Database/Bioinformatics Laboratory, School of Electrical & Computer EngineeringChungbuk National UniversityChungbukKorea

Personalised recommendations