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Abstract

We propose two new data stream models: the reset model and the delta model, motivated by applications to databases, and to tracking the location of spatial points.

We present algorithms for several problems that fit within the stream constraint of polylogarithmic space and time. These include tracking the “extent” of the points and L p sampling.

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References

  1. DIMACS Workshop on Managing and Processing Data Streams, FCRC (2003), http://www.research.att.com/conf/mpds2003/

  2. http://www.interfleet.com/

  3. http://www.lbszone.com/ , http://gislounge.com/ll/lbs.shtml , http://www.lbsportal.com/

  4. http://www.whereismybus.com/

  5. http://dimacs.rutgers.edu/Workshops/WGDeliberate/FinalReport5-20-02.doc

  6. DIMACS Working Group on Streaming Data Analysis, http://dimacs.rutgers.edu/Workshops/StreamingII/

  7. Abounaga, A., Chaudhuri, S.: Self-tuning histograms: Building histograms without looking at the data. In: Proc. SIGMOD (1999)

    Google Scholar 

  8. Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. In: Proc. ACM STOC, pp. 20–29 (1996)

    Google Scholar 

  9. Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. ACM PODS, pp. 1–16 (2002)

    Google Scholar 

  10. Bar-Yossef, Z., Jayram, T.S., Kumar, R., Sivakumar, D., Trevisan, L.: Counting distinct elements in a data stream. In: Rolim, J.D.P., Vadhan, S.P. (eds.) RANDOM 2002. LNCS, vol. 2483, pp. 1–10. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Charikar, M., O’Callaghan, L., Panigrahy, R.: Better streaming algorithms for clustering problems. ACM STOC (2003)

    Google Scholar 

  12. Cormode, G., Datar, M., Indyk, P., Muthukrishnan, S.: Comparing data streams using Hamming norms (How to zero in). IEEE Trans. Knowledge and Data Engineering 15, 529–541 (2003)

    Article  Google Scholar 

  13. Cormode, G., Muthukrishnan, S.: Radial Histograms. DIMACS TR 2003-11.

    Google Scholar 

  14. Cormode, G., Muthukrishnan, S.: Estimating dominance norms of multiple data streams. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 148–160. Springer, Heidelberg (2003)

    Google Scholar 

  15. Datar, M., Muthukrishnan, S.: Estimating Rarity and Similarity over Data Stream Windows. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 323–334. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  16. Estan, C., Savage, S., Varghese, G.: Automatically inferring patterns of resource consumption in network traffic. SIGCOMM (2003)

    Google Scholar 

  17. Feigenbaum, J., Kannan, S., Ziang, J.: Computing diameter in the streaming and sliding window models. Manuscript (2002)

    Google Scholar 

  18. Flajolet, P., Martin, G.: Probabilistic counting algorithms for database applications. JCSS 31, 182–209 (1985)

    MATH  MathSciNet  Google Scholar 

  19. Gibbons, P., Matias, Y.: Synopsis data structures. In: Proc. SODA, pp. 909–910 (1999)

    Google Scholar 

  20. Gilbert, A.C., Kotidis, Y., Muthukrishnan, S., Strauss, M.: Surfing wavelets on streams: One pass summaries for approximate aggregate queris. VLDB Journal, 79–88 (2001)

    Google Scholar 

  21. Gilbert, A.C., Guha, S., Indyk, P., Indyk, P., Kotidis, Y., Muthukrishnan, S., Strauss, M.J.: Fast, small-space algorithms for approximate histogram maintenance. In: Proceedings 34th ACM STOC, pp. 389–398 (2002)

    Google Scholar 

  22. Greenwald, M., Khanna, S.: Space-efficient online computation of quantile summaries. In: Proc. ACM SIGMOD (2001)

    Google Scholar 

  23. Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams. IEEE FOCS, pp. 359–366 (2000)

    Google Scholar 

  24. Har-Peled, S., Mazumdar, S.: On Coresets for k-Means and k-Median Clustering. In: Proc. 36th ACM STOC, pp. 291–300 (2004)

    Google Scholar 

  25. Henzinger, M., Raghavan, P., Rajagopalan, S.: Computing on data stream. Technical Note 1998-011. Digital systems research center, Palo Alto (May 1998)

    Google Scholar 

  26. Hershberger, J., Suri, S.: Convex hulls and related problems on data streams. In: Proc. MPDS (2003)

    Google Scholar 

  27. Indyk, P.: Algorithms for dynamic geometric problems over data streams. In: Proc. Annual ACM Symposium on Theory of Computing (STOC), pp. 373–380 (2004)

    Google Scholar 

  28. Indyk, P.: Stable distributions, pseudorandom generators, embeddings and data stream computation. IEEE FOCS, pp. 189–197 (2000)

    Google Scholar 

  29. Indyk, P., Thorup, M.: Unpublished manuscript (2001)

    Google Scholar 

  30. Jana, R., Johnson, T., Muthukrishnan, S., Vitaletti, A.: Location based services in a wireless WAN using cellular digital packet data (CDPD). MobiDE 2001: 74–80

    Google Scholar 

  31. Korn, F., Muthukrishnan, S., Srivastava, D.: Reverse nearest neighbor aggregates over data streams. In: Proc. VLDB (2002)

    Google Scholar 

  32. Krishnamurthy, B., Sen, S., Zhang, Y., Chen, Y.: Sketch-based change detection: methods, evaluation and applications. In: Proc. Internet Measurement Conference (IMC) (2003)

    Google Scholar 

  33. Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: Proc. VLDB, pp. 346–357 (2002)

    Google Scholar 

  34. Madden, S., Franklin, M.: Fjording the stream: An architecture for queryies over streaming sensor data. In: Proc. ICDE (2002)

    Google Scholar 

  35. Muthukrishnan, S.: Data Streams: Algorithms and Applications. The Foundations and Trends in Theoretical Computer Science series, Now Publishers (2005)

    Google Scholar 

  36. Bates, J.: Talk at NAS meeting on Statistics and Massive Data, http://www7.nationalacademies.org/bms/Massive_Data_Workshop.html

  37. Querying and mining data streams: you only get one look. Tutorial at SIGMOD, VLDB 2002 etc. (2002), See http://www.bell-labs.com/user/minos/tutorial.html

  38. Varghese, G.: Detecting packet patterns at high speeds. Tutorial at SIGCOMM (2002)

    Google Scholar 

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Bo Chen Mike Paterson Guochuan Zhang

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© 2007 Springer-Verlag Berlin Heidelberg

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Hoffmann, M., Muthukrishnan, S., Raman, R. (2007). Streaming Algorithms for Data in Motion. In: Chen, B., Paterson, M., Zhang, G. (eds) Combinatorics, Algorithms, Probabilistic and Experimental Methodologies. ESCAPE 2007. Lecture Notes in Computer Science, vol 4614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74450-4_27

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  • DOI: https://doi.org/10.1007/978-3-540-74450-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74449-8

  • Online ISBN: 978-3-540-74450-4

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