> Mining Data Streams-Estimating Frequency Moment Barna Saha October 26, 2017 Frequency Moment I … Counting distinct elements. Finding Persistent Items in Data Streams Haipeng Dai1 Muhammad Shahzad2 Alex X. Liu1 Yuankun Zhong1 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, CHINA 2Department of Computer Science, North Carolina State University, Raleigh, NC, USA haipengdai@nju.edu.cn, mshahza@ncsu.edu, alexliu@cse.msu.edu, kun@smail.nju.edu.cn Acknowledgements This dissertation is a result of help, encouragement and support that was given to me by a number of people I have been privileged to have come to know. Item frequencies Computing f(i) for all i is easy in O(n) space. We close the problem of understanding the space complexity of pth moment estimation in data streams for 0 p = 2 by giving the first optimal upper and lower bounds. L. Bhuvanagiri, S. Ganguly, D. Kesh, and C. Saha. mining data streams what arereal-world applications? 2 AMS Sketch Lets rst assume that we know m. Construct a random variable Xas follows: Choose a random element from the stream x= a i. If you nd mistakes, please inform me. Conventional knowl-edge discovery tools are facing two challenges, the overwhelming volume of the streaming data, and the concept drifts. /Length 799 Finding frequent elements 69 0 obj Estimating moments. In Proc of the 17th Annual ACM-SIAM Symposium on … Finally, the conclusions and future research are provided in Section 6. Sampling Data in a Stream – Filtering Streams – Counting Distinct Elements in a Stream – Estimating Moments – Counting Oneness in a Window – Decaying Window - Real time Analytics Platform(RTAP) Applications - Case Studies - Real Time Sentiment Analysis, Stock Market Predictions. iii. Created almost 50 years ago by Burton H. Bloom, at a time when computer science was still quite young, the original intent of this algorithm’s creator was to trade space (memory) and/or time (complexity) against what he called allowable errors. We present the ﬁrst O˜(1) space1 algorithm for the problem of estimating F p,q for p,q ∈ [0,2]. %PDF-1.5 data streams. x. from the stream. 1 Introduction The data stream model of computation is an abstraction for a variety of practical applications arising in network monitoring, sensor networks, RF-id processing, database systems, online web-mining, etc.. Estimating Frequency Moments of Data Streams using Random Linear Combinations Sumit Ganguly Indian Institute of Technology, Kanpur e-mail: sganguly@iitk.ac.in Abstract. In order to keep technical conditions to a minimum, we simply assume that g has con-tinuous derivatives of all … Estimating moments. 2 Outline • Stream management • Sampling and filtering streams • Counting in streams • Stream moments . Streaming summaries, sketches and samples – Motivating examples, applications and models – Random sampling: reservoir and minwise Application: Estimating entropy – Sketches: Count-Min, AMS, FM 2. Please do not cite this note as a reliable source. dev. 4 Assumptions: • Data comes in too fast to store all … Frequency Moment I Computing \moments" involves distribution of frequencies of di erent elements in the stream. 2011. Estimate avg./std. We demonstrate the variance-bias trade-off in estimating Shannon entropy and provide practical recommendations. /Filter /FlateDecode Section 5 presents the performance evaluations of the proposed approach by means of simulation. Surprisingly, despite the robust collection of data stream algorithms known to date, few if any apply to estimating graph aggregates on multigraph streams. Problems on Data Streams. Number of distinct elements in the last . We consider the problem of estimating hybrid frequency moments of two dimensional data streams. k. elements. This paper focuses on a very eﬃcient algorithm for estimating the entropy of data streams using a recently developed randomized algo-rithm called CompressedCounting(CC)byLi [23,21,24]. Abstract. Please do not cite this note as a reliable source. Feel free to use these slides verbatim, or to modify them to fit your own needs. Select elements with property . x��XKo7��W=I@��|��E]4h���-�!Y�l�^�������\rW�:�4��\���9�`�L�_'�h�X%�P�Vq�+���RY�m�rrzG��V.+���TŶ��t6&e=��x��(g�/�Ғ[���;V��6���FT�����?�Dn���p� endstream Estimating moments. Simpler algorithm for estimating frequency moments of data streams. Space-economical estimation of the pth frequency moments, defined as , for p> 0, are of interest in estimating all-pairs distances in a large data matrix [14], machine learning, and in data stream computation. Summary –Stream Mining Important tools for stream mining Sampling from Data Stream (Reservoir Sampling) Querying Over Sliding Windows (DGIM method for counting the number of 1s or sums in the window) Filtering a Data Stream (Bloom Filter) Counting Distinct Elements (Flajolet-Martin) Estimating Moments (AMS method; surprise number) Problems on Data Streams • Other types of queries one wants on answer on a data stream: – Filtering a data stream • Select elements with property x from the stream – Counting distinct elements • Number of distinct elements in the last k elements of the stream – Estimating moments In this model, data is viewed to be organized in a matrix form ( A i , j )1 i , j , n . Mining Data Streams Craig Douglas University of Wyoming. Mining Data Streams ... of the stream Estimating moments Estimate avg./std. January 10, 2011. Optimal Moment Estimation in Data Streams Date. dev. of last . how to compute the frequency moments using less than O(nlog m)space? /Filter /FlateDecode Correct! Mining Data Streams-Estimating Frequency Moment Barna Saha February 18, 2016. << Please share how this access benefits you. x��VKo�0��W� �&J��b���&����K��"i�a�~�l�nl݊5k���'��% 7���H�H$�$ׄh�ިh+0�(46K�]�M*��{T��� �B���|��ck���4p�Ƣ�&�U.���F{�p�� �b߁M���I'�)h$B��`H uř���.�2:�ɵ�=Bȿ�锦G�RJbc����XU���\z�g{;����( ſ��o�5K)��s��U On Estimating Frequency Moments of Data Streams Sumit Ganguly and1 Graham Cormode2 1 Indian Institute of Technology, Kanpur, sganguly@iitk.ac.in 2 AT&T Labs–Research, graham@research.att.com Abstract. dev. Space-economical estimation of the pth frequency moments, defined as Fp = P n i=1 |fi|p, for p> 0, are of interest in estimating all-pairs distances in a large data matrix [14], machine learning, and in data stream computation. Sorted by: Results 1 - 10 of 19. x. from the stream. 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