<> Here new data arrives very rapidly Mining Complex data Stream data Massive data, temporally ordered, fast changing and potentially infinite Satellite Images, Data from electric power grids Time-Series data Sequence of values obtained over time Economic and Sales data, natural phenomenon Sequence data Sequences of ordered elements or events (without time) DNA … We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splitting attribute. Although single data stream mining has been extensively studied, little research has been done for mining multiple data streams (MDS), which are more complex than single data streams and involved in many real-world applications. Introduction 1 2. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. endobj Introduction 10 2. COSC 6340 DisK. DZ��|��J�����?�PQ�{s�{�|�� �7uSl�u���*�vh��pc���Xo���6�3�i���8�A�}Z�`Y9Z-�M$�X&n����ҍ~K ͅ�rӪk �D�Z���u_�-{޹�t.���WF�7,������C0yq0�,7�lϳ 4 0 obj • Introduction & Motivation – Stream computation model, Applications • Basic stream synopses computation – Samples, Equi-depth histograms, Wavelets • Mining data streams – Decision trees, clustering, association rules • Sketch-based computation techniques – Self-joins, Joins, Wavelets, V-optimal histograms • Advanced techniques Introduction to Data Mining Lecture #8: Mining Data Streams-3 U Kang Seoul National University. <> 2.1 Data streams A data stream is an ordered sequence of instances that arrive at a rate that does not permit to <> 1 Introduction 1.1 Data Streams and Data Stream Management Systems Traditional data base management systems (DBMSs) are widely used in applications that require persistent storage for large volumes of data. Most of these chapters include exercises, an MOA-based lab session, or both. Mining Data Streams: 10.4018/978-1-5225-4999-4.ch014: In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data… The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. Prof. Michael R. Lyu The Chinese University of Hong Kong Not to be missed by anyone with serious interest in Big Data and Data Science. 3 Input tuples enter at a rapid rate, at one or more input ports. Important tools for stream mining Sampling from Data Stream (Reservoir Sampling) These systems manage rapid, high-volume data-streams with transient relations instead of static data with persistent rela-tions. stream endobj Outline. As this thesis concentrates on classification techniques, we will use the term data stream learning as a synonym for data stream mining. U Kang 2 Outline Estimating Moments Counting Frequent Items. 3 0 obj Mining Data Streams I : Suggested Readings: Ch4: Mining data streams (Sect. �޻�p�,8 ��������u�%O� �Wh�ܴ:���Þ�M]}�h�n��D0�XSa��J��W��EY*��*2\Ⱦ��rKPbx��n�u�|z�p���V@�a 2���Kgo�"�h�,����幍�\ c����@�w� �g���/��]��:?N}ry��HN L�m��Y����6��>��N�UY����]��~��0wcD A Data Stream is an ordered sequence of instances in time [1,2,4]. F�! More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely, https://mitpress.mit.edu/books/machine-learning-data-streams, International Affairs, History, & Political Science, Adaptive Computation and Machine Learning series. The Micro-clustering Based Stream Mining Framework 12 3. %���� The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. This growth in the production of dig- The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. 4.4-4.7) Colab 8 out: Colab 7 due: Tue Mar 3: Computational Advertising : Suggested Readings: According totheDigitalUniverseStudy[18], over 2.8ZB of data were created and processed in 2012, with a projected in-crease of 15 times by 2020. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. Finally, Section2.4describes the main applications of data stream mining techniques. INTRODUCTION The volumes of automatically generated data are constantly in-creasing. 1. Canada Research Chair and Director, Institute for Big Data Analytics, Dalhousie University; Distinguished Professor at the University of Ottawa, Canada; State Professor at the Institute for Computer Science of the Polish Academy of Sciences; Area Chair for Applications of the Springer Encyclopedia of Machine Learning. MAIDS: Mining Alarming Incidents from Data Streams⁄ Y. Dora Cai xDavid Clutter Greg Pape Jiawei Hany Michael Welge xLoretta Auvil x Automated Learning Group, NCSA, University of Illinois at Urbana-Champaign, U.S.A. y Department of Computer Science, University of Illinois at Urbana-Champaign, U.S.A. 1. Sensor data: The sensor produces data in the stream of real numbers. AAAI/MIT Press, 1991 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. %PDF-1.5 endobj Data Stream Mining fulfil the following characteristics: Continuous Stream of Data. INTRODUCTION Mining data streams for knowledge discovery, such as se-curity protection [19], clustering and classification [2], and frequent pattern discovery [12], has become increasingly im-portant. There exist emerging applications of data streams that have mining requirements. 5.1 mining data streams 1. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. 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 first part (9:00 – 10:30), ‘Mining One Stream’, will be presented by Albert Bifet, Ricard Gavaldà, Mykola Pechenizkiy, Bernhard Pfahringer, and Indrė Žliobaitė. future research in data stream mining. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. INTRODUCTION Many applications exist today that require the analysis of Mining Data Streams 1 2. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA. Clear and lucid presentation of state of the art methods for working with data in motion. Today we publish over 30 titles in the arts and humanities, social sciences, and science and technology. An excellent introduction to stream data analytics from the Big Data perspective. High amount of data in an infinite stream. <>>> The techniques used to obtain stream data are as listed below: 1. Querying and Mining Data Streams You Only Get One Look A Tutorial Minos Garofalakis Johannes Gehrke Rajeev Rastogi Bell Laboratories Cornell Universi ... Introduction to Query Optimization Chapter 13. The current situation is assessed by finding the resources, assumptions and other important factors. Data stream is an ordered sequence of instances. 9 pages. 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.. 4.1-4.3) Thu Feb 27: Mining Data Streams II : Suggested Readings: Ch4: Mining data streams (Sect. � m��I�Șy�&в�+�tͳ���a�L�!ј�Q�. x���Q��@���Á���Ό�X��&�.i7�m�P� �a���B���n��͂��O��˽�9�A����|2�B��`.� )E�X However, when it comes to mining data streams, it is not possible to store and iterate over the streams like traditional mining algorithms due to their continuous, high-speed, and unbounded nature. This book presents algorithms and techniques used in data stream mining and real-time analytics. Stream Mining Algorithms 2 3. endobj 1 Introduction A number of applications—real-time IP traffic analy-sis, managing web clicks and crawls, sensor readings, email/SMS/blog and other text sources—are instances of massive data streams. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. MIT Press Direct is a distinctive collection of influential MIT Press books curated for scholars and libraries worldwide. @s�����b���3)����Bf`��������+X�P��~�b��|�ƻX*��C�C6�>6ʫ鍷�&MUL�[���U��t�)C�&/��^��3����:���2��Ae1S |��G4 �;{E'�'���2#7#pM�����D�6��Yg��.�]�]� ��e[���ÌD,�}z�[;HJG;��_;�m�R��bc�z�?�2� Queries endobj Within this context, an important characteristic of the unbounded data streams is that the underlying dis- Statistical Mining in Data Streams Ankur Jain Recent years have seen a steady rise of a new class of data management systems called Data Stream Management Systems (DSMS). endstream Examples of such data streams include network event logs, telephone call records, credit card transactional flows, sensoring and surveillance video streams, etc. In mining data streams the most popular tool is the Hoeffding tree algorithm. CMSC5741 Big Data Tech. ����������>�\���+�!#�E�B���/��J��@V�P 2����G�p?e��V�o|�^�`F��H���_G�y��P�e̔�6��?k�� H�^�ߘ6*�S��u�°萱���Ű1ʸ�4�1� pxK�9�c+,B@$I�ۊ%ďt�����H�C���D�"G�@���2�� +鋗*�0*�D^!��m]Wr@����S1A,�{2����hO���v�Y9�1xc���،�3�*�E[(��a�>4�bX n1f�OW#D@�̘��h�X 06���\ |�N��v�⿼K����|cF=m7By��+��1�qrg^�"+^w-Ԯ�6#���؄;����$/���Q���J���T��? Data Stream Mining is t he process of extracting knowledge from continuous rapid data records which comes to the system in a stream. 1. x��O�dɖ�kYH��u.zU.J��(�PPnFp1`��v`@pa۫���.����{TPfp��0bB�@�4� �=�Q����X"�n��PU ��/�w�|'�޼y�OU���|d�wo܈s"��sb���������߯~�?�����o{ �_�.����������?�O��m�������������;7�^�����g�����|���Z��_�q������Ϳ��o{D�_sdb��s��A�ڽ��������|�C�����ן��%�h|�6�ɟ�ǿ�/�-{����gwK���@$��Y��k��~�~�o��w����ُ�w�������_?�c�p 2 0 obj Data stream, Distribution change 1. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. 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