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Sliding Window Algorithms

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  1. Aggarwal C (2007) Data streams: models and algorithms. Advances in database systems. http://www.springer.com/west/home/default?SGWID=4-40356-22-107949228-0

  2. Arasu A, Manku GS (2004) Approximate counts and quantiles over sliding windows. In: Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems (PODS’04). ACM, New York, pp 286–296. doi:10.1145/1055558.1055598

    Chapter  Google Scholar 

  3. Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems (PODS’02). ACM, New York, pp 1–16. doi:10.1145/543613.543615

    Chapter  Google Scholar 

  4. Babcock B, Datar M, Motwani R (2002) Sampling from a moving window over streaming data. In: Proceedings of the thirteenth annual ACM-SIAM symposium on discrete algorithms (SODA’02). Society for Industrial and Applied Mathematics, Philadelphia, pp 633–634. http://dl.acm.org/citation.cfm?id=545381.545465

  5. Babcock B, Datar M, Motwani R, O’Callaghan L (2003) Maintaining variance and k-medians over data stream windows. In: Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (PODS’03). ACM, New York, pp 234–243. doi:10.1145/773153.773176

    Chapter  Google Scholar 

  6. Braverman V, Ostrovsky R (2007) Smooth histograms for sliding windows. In: Proceedings of the 48th annual IEEE symposium on foundations of computer science (FOCS’07). IEEE Computer Society, Washington, DC, pp 283–293. doi:10.1109/FOCS.2007.63

    Chapter  Google Scholar 

  7. Braverman V, Ostrovsky R (2010) Effective computations on sliding windows. SIAM J Comput 39(6):2113–2131. doi:10.1137/090749281

    Article  MathSciNet  MATH  Google Scholar 

  8. Braverman V, Ostrovsky R (2010) Zero-one frequency laws. In: Proceedings of the 42nd ACM symposium on theory of computing (STOC’10). ACM, New York, pp 281–290. doi:10.1145/1806689.1806729

    Chapter  Google Scholar 

  9. Braverman V, Ostrovsky R, Zaniolo C (2009) Optimal sampling from sliding windows. In: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems (PODS’09). ACM, New York, pp 147–156. doi:10.1145/1559795.1559818

    Chapter  Google Scholar 

  10. Braverman V, Ostrovsky R, Zaniolo C (2012) Optimal sampling from sliding windows. J Comput Syst Sci 78(1):260–272. doi:10.1016/j.jcss.2011.04.004

    Article  MathSciNet  MATH  Google Scholar 

  11. Braverman V, Gelles R, Ostrovsky R (2013) How to catch l 2-heavy-hitters on sliding windows. In: Du DZ, Zhang G (eds) Computing and combinatorics. Lecture notes in computer science, vol 7936. Springer, Berlin/Heidelberg, pp 638–650. doi:10.1007/978-3-642-38768-5_56

    Chapter  Google Scholar 

  12. Braverman V, Katzman J, Seidell C, Vorsanger G (2014) An optimal algorithm for large frequency moments using \(\text{O}(n^{1-2/k})\) bits. In: Proceedings of the 18th international workshop on randomization and computation (RANDOM’2014)

    Google Scholar 

  13. Braverman V, Ostrovsky R, Roytman A (2014) Universal Streaming. ArXiv e-prints 1408.2604

    Google Scholar 

  14. Chen K (2009) On coresets for $k$-median and $k$-means clustering in metric and euclidean spaces and their applications. SIAM J Comput 39(3):923–947. doi:http://dx.doi.org/10.1137/070699007

    Google Scholar 

  15. Chi Y, Wang H, Yu PS, Muntz RR (2004) Moment: Maintaining closed frequent itemsets over a stream sliding window. In: ICDM, pp 59–66

    Google Scholar 

  16. Cormode G, Yi K (2012) Tracking distributed aggregates over time-based sliding windows. In: Proceedings of the 24th international conference on scientific and statistical database management (SSDBM’12). Springer, Berlin/Heidelberg, pp 416–430. doi:10.1007/978-3-642-31235-9_28

    Google Scholar 

  17. Crouch MS, McGregor A, Stubbs D (2013) Dynamic graphs in the sliding-window model. In: ESA, pp 337–348

    MATH  Google Scholar 

  18. Dang XH, Lee VC, Ng WK, Ong KL (2009) Incremental and adaptive clustering stream data over sliding window. In: Proceedings of the 20th international conference on database and expert systems applications (DEXA’09). Springer, Berlin/Heidelberg, pp 660–674. doi:10.1007/978-3-642-03573-9_55

    Chapter  Google Scholar 

  19. Datar M, Muthukrishnan MS (2002) Estimating rarity and similarity over data stream windows. In: Proceedings of the 10th annual European symposium on algorithms (ESA’02). Springer, London, pp 323–334. http://dl.acm.org/citation.cfm?id=647912.740833

  20. Datar M, Gionis A, Indyk P, Motwani R (2002) Maintaining stream statistics over sliding windows. SIAM J Comput 31(6):1794–1813

    Article  MathSciNet  MATH  Google Scholar 

  21. Datar M, Gionis A, Indyk P, Motwani R (2002) Maintaining stream statistics over sliding windows: (extended abstract). In: Proceedings of the thirteenth annual ACM-SIAM symposium on discrete algorithms (SODA’02). Society for Industrial and Applied Mathematics, Philadelphia, pp 635–644. http://dl.acm.org/citation.cfm?id=545381.545466

  22. Feigenbaum J, Kannan S, Zhang J (2004) Computing diameter in the streaming and sliding-window models. Algorithmica 41(1):25–41

    Article  MathSciNet  MATH  Google Scholar 

  23. Feigenbaum J, Kannan S, Zhang J (2005) Computing diameter in the streaming and sliding-window models. Algorithmica 41:25–41

    Article  MathSciNet  MATH  Google Scholar 

  24. Gibbons PB, Tirthapura S (2002) Distributed streams algorithms for sliding windows. In: Proceedings of the fourteenth annual ACM symposium on parallel algorithms and architectures (SPAA’02). ACM, New York, pp 63–72. doi:10.1145/564870.564880

    Google Scholar 

  25. Golab L, Özsu MT (2003) Processing sliding window multi-joins in continuous queries over data streams. In: Proceedings of the 29th international conference on Very large data bases (VLDB’03), vol 29. VLDB Endowment, pp 500–511. http://dl.acm.org/citation.cfm?id=1315451.1315495

  26. Golab L, DeHaan D, Demaine ED, Lopez-Ortiz A, Munro JI (2003) Identifying frequent items in sliding windows over on-line packet streams. In: Proceedings of the 3rd ACM SIGCOMM conference on internet measurement (IMC’03). ACM, New York, pp 173–178. doi:10.1145/948205.948227

    Google Scholar 

  27. Kane DM, Nelson J, Woodruff DP (2010) On the exact space complexity of sketching and streaming small norms. In: Proceedings of the 21st annual ACM-SIAM symposium on discrete algorithms (SODA’10)

    Google Scholar 

  28. Kane DM, Nelson J, Woodruff DP (2010) An optimal algorithm for the distinct elements problem. In: Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (PODS’10). ACM, New York, pp 41–52. doi:10.1145/1807085.1807094

    Chapter  Google Scholar 

  29. Lee LK, Ting HF (2006) Maintaining significant stream statistics over sliding windows. In: SODA ’06: Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm. ACM, New York, pp 724–732. doi:http://doi.acm.org/10.1145/1109557.1109636

    Google Scholar 

  30. Lee LK, Ting HF (2006) A simpler and more efficient deterministic scheme for finding frequent items over sliding windows. In: Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (PODS’06). ACM, New York, pp 290–297. doi:http://doi.acm.org/10.1145/1142351.1142393

    Google Scholar 

  31. Li J, Maier D, Tufte K, Papadimos V, Tucker PA (2005) No pane, no gain: efficient evaluation of sliding-window aggregates over data streams. SIGMOD Rec 34(1):39–44. doi:10.1145/1058150.1058158

    Article  Google Scholar 

  32. McGregor A (2014) Graph stream algorithms: A survey. SIGMOD Rec 43(1):9–20. doi:10.1145/2627692.2627694

    Article  Google Scholar 

  33. Ren J, Ma R, Ren J (2009) Density-based data streams clustering over sliding windows. In: Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery (FSKD’09), vol 5. IEEE Press, Piscataway, pp 248–252. http://dl.acm.org/citation.cfm?id=1801874.1801929

  34. Sohler C (2006) List of open problems in sublinear algorithms: problem 20. http://sublinear.info/20

  35. Tatbul N, Zdonik S (2006) Window-aware load shedding for aggregation queries over data streams. In: Proceedings of the 32nd international conference on very large data bases (VLDB’06). VLDB Endowment, pp 799–810. http://dl.acm.org/citation.cfm?id=1182635.1164196

  36. Zhang L, Li Z, Yu M, Wang Y, Jiang Y (2005) Random sampling algorithms for sliding windows over data streams. In: Proceedings of the 11th joint international computer conference, pp 572–575

    Google Scholar 

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Acknowledgements

This material is based upon work supported in part by the National Science Foundation under Grant No. 1447639.

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Authors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Vladimir Braverman

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  1. Vladimir Braverman
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Correspondence to Vladimir Braverman .

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Editors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA

    Ming-Yang Kao

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Braverman, V. (2016). Sliding Window Algorithms. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_797

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