How to catch L2-heavy-hitters on sliding windows

Finding heavy-elements (heavy-hitters) in streaming data is one of the central, and well-understood tasks. Despite the importance of this problem, when considering the sliding windows model of streaming (where elements eventually expire) the problem of finding L 2-heavy elements has remained completely open despite multiple papers and considerable success in finding L 1-heavy elements.

[1]  Erik D. Demaine,et al.  Identifying frequent items in sliding windows over on-line packet streams , 2003, IMC '03.

[2]  Edith Cohen,et al.  Maintaining time-decaying stream aggregates , 2003, J. Algorithms.

[3]  Charu C. Aggarwal,et al.  Data Streams - Models and Algorithms , 2014, Advances in Database Systems.

[4]  David P. Woodruff,et al.  Optimal approximations of the frequency moments of data streams , 2005, STOC '05.

[5]  Richard M. Karp,et al.  A simple algorithm for finding frequent elements in streams and bags , 2003, TODS.

[6]  Srikanta Tirthapura,et al.  Estimating simple functions on the union of data streams , 2001, SPAA '01.

[7]  Piotr Indyk,et al.  Maintaining stream statistics over sliding windows: (extended abstract) , 2002, SODA '02.

[8]  David P. Woodruff,et al.  An optimal algorithm for the distinct elements problem , 2010, PODS '10.

[9]  Andrei Z. Broder,et al.  On the resemblance and containment of documents , 1997, Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171).

[10]  Gurmeet Singh Manku,et al.  Approximate counts and quantiles over sliding windows , 2004, PODS.

[11]  George Varghese,et al.  New directions in traffic measurement and accounting: Focusing on the elephants, ignoring the mice , 2003, TOCS.

[12]  Divesh Srivastava,et al.  Finding Hierarchical Heavy Hitters in Data Streams , 2003, VLDB.

[13]  Moses Charikar,et al.  Finding frequent items in data streams , 2002, Theor. Comput. Sci..

[14]  Edith Cohen,et al.  Size-Estimation Framework with Applications to Transitive Closure and Reachability , 1997, J. Comput. Syst. Sci..

[15]  Noga Alon,et al.  The Space Complexity of Approximating the Frequency Moments , 1999 .

[16]  Alan M. Frieze,et al.  Min-Wise Independent Permutations , 2000, J. Comput. Syst. Sci..

[17]  Rajeev Motwani,et al.  Approximate Frequency Counts over Data Streams , 2012, VLDB.

[18]  Moses Charikar,et al.  Finding frequent items in data streams , 2004, Theor. Comput. Sci..

[19]  Michael E. Saks,et al.  Space lower bounds for distance approximation in the data stream model , 2002, STOC '02.

[20]  Yong Guan,et al.  Frequency Estimation over Sliding Windows , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[21]  Hing-Fung Ting,et al.  Finding Heavy Hitters over the Sliding Window of a Weighted Data Stream , 2008, LATIN.

[22]  S. Muthukrishnan,et al.  Data streams: algorithms and applications , 2005, SODA '03.

[23]  R. Ostrovsky,et al.  Smooth Histograms for Sliding Windows , 2007, FOCS 2007.

[24]  Geoffrey Zweig,et al.  Syntactic Clustering of the Web , 1997, Comput. Networks.

[25]  Lap-Kei Lee,et al.  A simpler and more efficient deterministic scheme for finding frequent items over sliding windows , 2006, PODS '06.

[26]  Philippe Flajolet,et al.  Probabilistic counting , 1983, 24th Annual Symposium on Foundations of Computer Science (sfcs 1983).

[27]  Aoying Zhou,et al.  Dynamically maintaining frequent items over a data stream , 2003, CIKM '03.

[28]  Erik D. Demaine,et al.  Frequency Estimation of Internet Packet Streams with Limited Space , 2002, ESA.

[29]  Sumit Ganguly,et al.  Simpler algorithm for estimating frequency moments of data streams , 2006, SODA '06.

[30]  Lap-Kei Lee,et al.  Finding frequent items over sliding windows with constant update time , 2010, Inf. Process. Lett..

[31]  Luca Trevisan,et al.  Counting Distinct Elements in a Data Stream , 2002, RANDOM.

[32]  Jia Wang,et al.  Analyzing peer-to-peer traffic across large networks , 2004, IEEE/ACM Trans. Netw..

[33]  Graham Cormode,et al.  An improved data stream summary: the count-min sketch and its applications , 2004, J. Algorithms.

[34]  Graham Cormode,et al.  What's hot and what's not: tracking most frequent items dynamically , 2003, TODS.

[35]  Ziv Bar-Yossef,et al.  Reductions in streaming algorithms, with an application to counting triangles in graphs , 2002, SODA '02.

[36]  David P. Woodruff,et al.  A General Method for Estimating Correlated Aggregates over a Data Stream , 2012, ICDE.

[37]  S. Muthukrishnan,et al.  Estimating Rarity and Similarity over Data Stream Windows , 2002, ESA.

[38]  Divyakant Agrawal,et al.  Efficient Computation of Frequent and Top-k Elements in Data Streams , 2005, ICDT.

[39]  Piotr Indyk,et al.  A small approximately min-wise independent family of hash functions , 1999, SODA '99.

[40]  Ravi Kumar,et al.  An information statistics approach to data stream and communication complexity , 2004, J. Comput. Syst. Sci..

[41]  Zhengding Lu,et al.  Approximate frequency counts in sliding window over data stream , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[42]  Divyakant Agrawal,et al.  Fast Algorithms for Heavy Distinct Hitters using Associative Memories , 2007, 27th International Conference on Distributed Computing Systems (ICDCS '07).