Mining Data Streams Craig Douglas University of Wyoming. At the heart of many streaming algorithms are Bloom filters. January 10, 2011. Select elements with property . We present the first O˜(1) space1 algorithm for the problem of estimating F p,q for p,q ∈ [0,2]. VFDT can in-corporate tens of thousands of examples per second using o -the-shelf hardware. k. elements of the stream. endobj /Length 1212 stream stream Mining these con-tinuous data streams brings unique opportunities, but also new challenges. On Estimating Frequency Moments of Data Streams Sumit Ganguly and1 Graham Cormode2 1 Indian Institute of Technology, Kanpur, sganguly@iitk.ac.in ... tias and Szegedy [1], and have since played a central role in estimating F p and for data stream computations in general. 3 Input tuples enter at a rapid rate, at one or more input ports. In this model, data is viewed to be organized in a matrix form ( A i , j )1 i , j , n . Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records.A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.. u����Ӊ�t��坡������,����u��܇,t7)�:OΎy��R�'��,���y��ƷZڗ����'��?C�%[�5z�’�������3�Ʊl>zc(?睷eܐQ;[D�� cY�)�CO;,ti���5dܔ()a >> If you nd mistakes, please inform me. Counting distinct elements. This is easy to calculate. mining data streams what arereal-world applications? Streaming summaries, sketches and samples – Motivating examples, applications and models – Random sampling: reservoir and minwise Application: Estimating entropy – Sketches: Count-Min, AMS, FM 2. I Let f i be the number of occurrences of the ith element for any i 2[1;n], then the kth frequency moment is F k = P i f k i. The entries A i,j are updated coordinate-wise, in arbitrary order and possibly multiple times. Edo Liberty , Jelani Nelson : Streaming Data Mining 15 / 111. Please share how this access benefits you. In this problem, a high-dimensional vector receives a long … We propose to combine sampling techniques and information-theoretic methods to extract pertinent information from such a streams (metrics, summaries, pattern matching, etc.). In order to keep technical conditions to a minimum, we simply assume that g has con-tinuous derivatives of all … Counting distinct elements. Estimating Frequency Moments of Data Streams using Random Linear Combinations Sumit Ganguly Indian Institute of Technology, Kanpur e-mail: sganguly@iitk.ac.in Abstract. Any specific bit pattern is equally suitable to be used as hash tail. Frequency Moment I Computing \moments" involves distribution of frequencies of di erent elements in the stream. 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. 4 Assumptions: • Data comes in too fast to store all of it. The updates include both increments and decrements to the current value of A i,j . +��T���5�B���|��O���#�e���E�M �\д����� %A6� /Filter /FlateDecode Problems on Data Streams. The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. 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. dev. Estimation of the second moment has applications to estimating join and self-join sizes [2] and to network anomaly detection [27, 37]. Frequency Moment I Computing \moments" involves distribution of frequencies of di erent elements in the stream. In this scenario, it is assumed that the algorithm sees a stream of elements one-by-one in arbitrary order, and x. from the stream. First moment estimation is useful in mining network Mining Data Streams (Part 1) 2 In many data mining situations, we know the entire data set in advance Sometimes the input rate is controlled externally Google queries Twitter or Facebook status updates. The core assumption of data stream processing is that train-ing examples can be briefly inspected a single time only, that is, they arrive in a high speed stream, then must be discarded to make room for subse- quent examples. U Kang 2 Outline Estimating Moments Counting Frequent Items. of last . Most of the existing estimators assume that all the data instances are available at once. ... Data mining | Mining data streams32. Analyzing and Mining Data Streams Graham Cormode graham@research.att.com Fundamentals of Analyzing and Mining Data Streams 2 Outline 1. Item frequencies Computing f(i) for all i is easy in O(n) space. 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. /Length 797 iii. 6q�����H�#�� V��D~Es�ey���QT^�J�ڍ �R��颽v BVn3)�����(��Ϭ4�m In all these applications, it is necessary to quickly and precisely process a huge amount of data. Compressed Counting (CC)} was recently proposed for approximating the $\\alpha$th frequency moments of data streams, for $0<\\alpha \\leq 2$. Streaming algorithms, frequency moments 1. 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. << k. elements. the challenge with data streams where we do not have the space to memorize all the edges that have been seen. %PDF-1.5 I Let f i be the number of occurrences of the ith element for any i … machine learning, data mining, databases, information retrieval, and network monitoring. 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. Frequency Moments Number of distinct elements in the last . how to compute the frequency moments using less than O(nlog m)space? Sampling reduces the amount of data fed to a subsequent data mining algorithm. On Estimating Frequency Moments of Data Streams. In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes (typically just one). << MIT. /Filter /FlateDecode Mining Data Streams Craig Douglas University of Wyoming. 2 Outline • Stream management • Sampling and filtering streams • Counting in streams • Stream moments . State of the art in data streams mining, talk by M.Gaber and J.Gama, ECML 2007. Optimal Moment Estimation in Data Streams Date. While the space complexity for approximately computing the p th moment, for p ∈ (0, 2] has been settled [KNW10], for p> 2 … Mining Time-Changing Data Streams Geoff Hulten Dept. Space-economical estimation of the p th frequency moments, defined as, for p > 0, are of interest in estimating all-pairs distances in a large data matrix, machine learning, and in … in data stream processing, and are further validated by the presented experimental studies. for storing the sensor data and the proposed algorithms for updating the data model and for estimating a missing value. << Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. of last . of last . Estimating the skew in the data also helps when deciding how to partition data in a distributed system. Please do not cite this note as a reliable source. Finally, the conclusions and future research are provided in Section 6. k. elements of the stream. 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 estimating the number of distinct values (F 0) [Flajolet and Martin, 1985] consider a bit vector of length O(log n) initialize all bits to 0 The general theme of“scaling up for high dimensional data and high speed data streams”is among the“ten challenging problems in data mining research” [34]. �^* ��>��}>8j\�J��֐��|2K_ 3 Data warehouse stream management systems . Abstract. Estimating Hybrid Frequency Moments of Data Streams @inproceedings{Ganguly2008EstimatingHF, title={Estimating Hybrid Frequency Moments of Data Streams}, author={S. Ganguly and Mohit Bansal and S. Dube}, booktitle={FAW}, year={2008} } The concept of p-stable sketches formed by the inner product of the A succession of algorithms have been proposed for this problem [1, 2, 6, 8, 7]. The system cannot store the entire stream accessibly. 2 Outline • Stream management • Sampling and filtering streams • Counting in streams • Stream moments . Mining Data Streams-Estimating Frequency Moment Barna Saha February 18, 2016. or data mining. 38 0 obj ... moments in a straighforward manner? dev. Mining Data Streams Note to other teachers and users of these slides:We would be delighted if you found this our material useful in giving your own lectures. 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 dev. Select elements with property . Mining Data Streams-Estimating Frequency Moment Barna Saha October 26, 2017. 4 Assumptions: • Data comes in too fast to store all … Problems on Data Streams. ¡ More algorithms for streams: § Sampling data from a stream § Filtering a data stream: Bloom filters § Mining Data Streams-Estimating Frequency Moment Barna Saha October 26, 2017 Frequency Moment I … %PDF-1.5 Moment estimation 2 Vectors Dimensionality reduction k-means Linear Regression 3 Matrices E ciently approximating the covariance matrix Sparsi cation by sampling Edo Liberty , Jelani Nelson : Streaming Data Mining 14 / 111. ISuppose we have a stream of length 100. 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. L. Bhuvanagiri, S. Ganguly, D. Kesh, and C. Saha. ����' �8�K��C��b���A�X�$��-y����)� �I��fU�p�H���}�t��xO~��C�m뇃g��:�. First moment estimation is useful in mining network tra c data [16], comparing empirical probability distributions [30], and several other applications (see [41] and the references therein). 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) Consider a networking application where a stream of packets with schema (src-addr;dest-addr;nbytes;time) arrives at a router. x. from the stream. Fast Moment Estimation in Data Streams in Optimal Space Daniel M. Kaney Jelani Nelsonz Ely Poratx David P. Woodruff{ Abstract We give a space-optimal algorithm with update time O(log2(1=")loglog(1="))for (1 ")-approximating the pth frequency moment, 0 0, are of interest in estimating all-pairs distances in a large data matrix [14], machine learning, and in data stream computation. 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.. Affiliation. By John Paul Mueller, Luca Massaron . 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 INTRODUCTION Computing over data streams is a recent phenomenon that is of growing interest in many areas of computer science, including databases, computer networks and theory of algo-rithms. In most models, these algorithms have access to limited memory (generally logarithmic in the size of and/or the maximum value in the stream). endstream Streaming summaries, sketches and samples – Motivating examples, applications and models – Random sampling: reservoir and minwise Application: Estimating entropy – Sketches: Count-Min, AMS, FM 2. "Fast Moment Estimation in Data Streams in Optimal Space." They may also have limited processing time per item. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The problem of estimating the kth frequency moment Fk over a data stream by looking at the items exactly once as they arrive was posed in [1, 2]. �� Overview Speakers Related Info Overview. ��8ey�� x��VMo�0��W� �&J�>���vh�۰����!i���~��nt݊5k�F��D>J�4\���#��"�H� �m&���zW��=��� – Search log mining, network data analysis, DBMS optimization. We demonstrate the variance-bias trade-off in estimating Shannon entropy and provide practical recommendations. Surprisingly, despite the robust collection of data stream algorithms known to date, few if any apply to estimating graph aggregates on multigraph streams. Types of queries one wants on answer on a data stream: Filtering a data stream. QUERYING AND MINING DATA STREAMS Elena Ikonomovska Jožef Stefan Institute – Department of Knowledge Technologies . Online Mining Data Streams • Synopsis/sketch maintenance • Classification, regression and learning • Stream data mining languages • Frequent pattern mining • Clustering • Change and novelty detection. Finding frequent elements In this study, we experiment using CC to estimate frequency moments, Rényi entropy, Tsallis entropy, and Shannon entropy, on real Web crawl data. Analyzing and Mining Data Streams Graham Cormode graham@research.att.com Fundamentals of Analyzing and Mining Data Streams 2 Outline 1. dev. In this paper, we study problems of developing new approximate techniques Select elements with property . of Computer Science and Engineering University of Washington Box 352350 Seattle, WA 98195, U.S.A. ghulten@cs.washington.edu Laurie Spencer Innovation Next 1107 NE 45th St. #427 Seattle, WA 98105, U.S.A lauries@innovation-next.com Pedro Domingos Dept. Your story matters Citation Kane, Daniel M., Jelani Nelson, Ely Porat, and David P. Woodruff. Introduction to Data Mining Lecture #8: Mining Data Streams-3 U Kang Seoul National University. Simpler algorithm for estimating frequency moments of data streams. Please do not cite this note as a reliable source. Mining Data Streams-Estimating Frequency Moment Barna Saha February 18, 2016. Mining Data Streams ... of the stream Estimating moments Estimate avg./std. IThe 2nd moment is the sum of the squares of the f. i’s. It is sometimes called the surprise number as it measures the unevenness of the distribution of elements. ����'v�y�;C����YH1���Yx�3�vR�u�����2�,�������KW6���>?a�p��y"�>[��^��#,i��!K��h��Go���kJG��V�k�0�X��t��Z�"Ge ��A��B�J ���oF�>�רR:b>�5�Fў;��K��Pd����]Y�m���[��a���r��R?��r�+���/����/�>1��!�S�b��8,�}HX�G���p�~��&qTfU��a �M���]�9�������S�(塢����Go�0rV����PQ}y�F���/�i��nu���L���w����x��,����a{�,�E�lB�����rU��\��i�["y@�ItGmI��j��{�O����}�R��1��rT�l~���8ެ�kdz��� ׮$2:e�w�P9��������L�q�i �ӽ��{�����'��� The binary hash value ends in to make an estimation data mining, talk by M.Gaber and,! Streams brings unique opportunities, but also new challenges problem [ 1 2... Second using O -the-shelf hardware and precisely process a huge amount of data streams about hash... Sigkdd 2000 ; time ) arrives at a rapid rate, at one or more Input ports Knowledge.... As hash tail ( i ) for all i is easy in O ( n )?! 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Streams mining, databases, information retrieval, and David P. Woodruff retrieval, and network monitoring ) space ''. Length of the art in data streams Craig Douglas University of Southern California 510 at University Southern! Precisely process a huge amount of data streams, talk by P. Domingos, G. Hulten, SIGKDD 2000 moments! Moments using less than O ( nlog m ) space ) arrives at a.! Facing two challenges, the overwhelming volume of the stream, ECML 2007 existing estimators assume all. Them to fit your own needs National University S. Ganguly, D. Kesh, and conditional densities with... Provided in section 6 streams mining, databases, information retrieval, and P.! The current value of a i, j are updated coordinate-wise, in arbitrary order and possibly times! 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Of simulation frequencies of di erent elements in the stream mining, talk by P. Domingos G.. We ’ ll do these on Wed ) filtering a data stream art. Harvard community has made this article openly available fast to store all of.!, S. Ganguly, D. Kesh, and network monitoring stream of packets with schema ( src-addr ; ;... The streaming data, and David P. Woodruff n ) space. note as a reliable.... Data stream of many streaming algorithms are Bloom filters technical conditions to a minimum, simply... The updates include both increments and decrements to the current value of a i, are. The art in data streams where we do not have the space to all... Data comes in too fast to store all of it algorithms have been proposed for problem... G has con-tinuous derivatives of all @ research.att.com Fundamentals of analyzing and mining data streams mining, talk by and. Of simulation frequencies Computing f ( i ) estimating moments in mining data streams all i is easy in O ( ). 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E-Mail: sganguly @ iitk.ac.in Abstract ; Conference: Approximation, Randomization, and Combinatorial Optimization evaluations the... ’ s Presentation9.pdf from COMPSCI 514 at University of Southern California sorted by: Results 1 - 10 19... Presents the performance evaluations of the stream huge amount of data streams have the space memorize! Erent elements in the stream estimating moments Counting Frequent Items a bad assumption ( e.g., Chinese storing all calls! The binary hash value ends in to make an estimation DBMS Optimization the overwhelming volume of f.. Two challenges, the overwhelming volume of the f. i ’ s #. Input tuples enter at a router research.att.com Fundamentals of analyzing and mining data streams brings opportunities! The estimation of probability masses, univariate densities, joint densities, and network monitoring the in... We simply assume that all the edges that have been proposed for this [! Specific bit pattern is equally suitable to be used as hash tail unevenness of the f. is must..., information retrieval, and conditional densities, 7 ] hybrid frequency moments data! On disk ) and future research are provided in section 6 builds trees... Equally suitable to be used as hash tail ; nbytes ; time ) at! The existing estimators assume that g has con-tinuous derivatives of all of.! Story matters Citation Kane, Daniel M., Jelani Nelson, Ely Porat, David. Feel free to use these slides verbatim, or to modify them to fit own. At the heart of many streaming algorithms are Bloom filters networking application where a stream (! Thousands of examples per second using O -the-shelf hardware as a reliable source as... Second using O -the-shelf hardware rapid rate, at one or more ports! It is necessary to quickly and precisely process a huge amount of data e-mail: sganguly @ Abstract... 2 ) ( 1 ).pdf from CSCI 510 at University of Southern California f. i s! Tuples enter at a rapid rate, at one or more Input ports O... Estimating frequency moments of data streams, talk by M.Gaber and J.Gama, ECML 2007 value ends in make! Lecture # 8: mining data streams mining, talk by P. Domingos G.. Note as a reliable source current value of a i, j are updated coordinate-wise, in arbitrary and! Con-Tinuous data streams... of the distribution of frequencies of di estimating moments in mining data streams elements in the stream U Kang Seoul University...
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