The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. A Data Stream is an ordered sequence of instances in time [1,2,4]. <> High amount of data in an infinite stream. 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. @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� Data Stream Mining is t he process of extracting knowledge from continuous rapid data records which comes to the system in a stream. f���o�6�7�����W?D|~�� ���$�+�������������S(�_�;�y�*� p ��_��Y߸��Y�)��D����G�&�j~9�+ϳ����pg��10�ä@?so�b�� It brings a fresh, unique focus on sketches, often overlooked in monographs, as well as its highly practical, hands-on grounding in the open-source MOA system. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. 1. Conclusions and Summary 6 References 7 2 On Clustering Massive Data Streams: A Summarization Paradigm 9 Charu C. Aggarwal, Jiawei Han, Jianyong Wang and Philip S. Yu 1. Querying and Mining Data Streams You Only Get One Look A Tutorial Minos Garofalakis Johannes Gehrke Rajeev Rastogi Bell Laboratories Cornell Universi ... Introduction to Query Optimization Chapter 13. MIT Press Direct is a distinctive collection of influential MIT Press books curated for scholars and libraries worldwide. From Adaptive Computation and Machine Learning series, By Albert Bifet, Ricard Gavaldà, Geoff Holmes and Bernhard Pfahringer. The current situation is assessed by finding the resources, assumptions and other important factors. Online Mining of Data Streams: Problems, Applications and Progress Haixun Wang1 Jian Pei2 Philip S. Yu1 1IBM T.J. Watson Research Center, USA 2Simon Fraser University, Canada ����������>�\���+�!#�E�B���/��J��@V�P 2����G�p?e��V�o|�^�`F��H���_G�y��P�e̔�6��?k�� H�^�ߘ6*�S��u�°萱���Ű1ʸ�4�1� pxK�9�c+,B@$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��? <> U Kang 2 Outline Estimating Moments Counting Frequent Items. According totheDigitalUniverseStudy[18], over 2.8ZB of data were created and processed in 2012, with a projected in-crease of 15 times by 2020. 3 0 obj Data Streams Mining The process of obtaining the structure of knowledge or the information patterns from the existing data is called as 'Data Stream Mining'. 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. 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. 4 0 obj In mining data streams the most popular tool is the Hoeffding tree algorithm. This book presents algorithms and techniques used in data stream mining and real-time analytics. Research Issues In Mining Multiple INTRODUCTION Many applications exist today that require the analysis of 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. endobj Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. x���Q��@���Á���Ό�X��&�.i7�m�P� �a���B���n��͂��O��˽�9�A����|2�B��`.� )E�X 9 pages. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splitting attribute. future research in data stream mining. Mining Data Streams: 10.4018/978-1-60566-010-3.ch194: When a space shuttle takes off, tiny sensors measure thousands of data points every fraction of a second, pertaining to a variety of attributes like endobj 5 0 obj Therefore, many data mining and database operations such as classification, clustering, frequent pattern mining and indexing become significantly more challenging in this context. %���� endobj 2 0 obj Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. 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. Most of these chapters include exercises, an MOA-based lab session, or both. INTRODUCTION The volumes of automatically generated data are constantly in-creasing. This growth in the production of dig- 4.1-4.3) Thu Feb 27: Mining Data Streams II : Suggested Readings: Ch4: Mining data streams (Sect. In the literature the same Hoeffding's bound was used for any evaluation function (heuristic measure), e.g., information gain or Gini index. Within this context, an important characteristic of the unbounded data streams is that the underlying dis- 6 0 obj 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. 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. There exist emerging applications of data streams that have mining requirements. MIT Press began publishing journals in 1970 with the first volumes of Linguistic Inquiry and the Journal of Interdisciplinary History. A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. 6N�t��BZ�A��d��o~7�o�L� ��L��� ���dX�(����u��|�)�������F²��fy$$7�+��KY�T�C��'I��� tr�" |Xfh|�@h,� �Ϭj�������2r��Q��_�������v[�3��3Op�o�@�z�:�u��޳Ӧ�Vu����=:pv2q�s��Y @w�V]~�����*P�� P@��Y��p�+�-��7>�:��\�?Ґ�%�|;�I�*��x#My��\�X��,��]&�>���@�� ����7�)�X^����x����!���i|�]�2�;����Eʙ ��L�Y$ Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. Introduction to Data Mining Lecture #8: Mining Data Streams-3 U Kang Seoul National University. 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. 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.. Today we publish over 30 titles in the arts and humanities, social sciences, and science and technology. stream INTRODUCTION The scalability of data mining methods is constantly being chal-lenged by real-time production systems that generate tremendous amount of data at unprecedented rates. Introduction to data streams and drifting data; Adaptive predictive models; Clustering streaming data; Pattern Mining on streams; Tools for mining data streams 4.4-4.7) Colab 8 out: Colab 7 due: Tue Mar 3: Computational Advertising : Suggested Readings: The Micro-clustering Based Stream Mining Framework 12 3. 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. The techniques used to obtain stream data are as listed below: 1. DZ��|��J�����?�PQ�{s�{�|�� �7uSl�u���*�vh��pc���Xo���6�3�i���8�A�}Z�`Y9Z-�M$�X&n����ҍ~K ͅ�rӪk �D�Z���u_�-{޹�t.���WF�7,������C0yq0�,7�lϳ � m��I�Șy�&в�+�tͳ���a�L�!ј�Q�. <> Keywords: data stream analysis, data mining, Zipf distribution, power laws, heavy hitters, massive data. 12 pages. Mining Data Streams I : Suggested Readings: Ch4: Mining data streams (Sect. 1 Introduction A number of applications—real-time IP traffic analy-sis, managing web clicks and crawls, sensor readings, email/SMS/blog and other text sources—are instances of massive data streams. This tutorial is a gentle introduction to mining big data streams. 5.1 mining data streams 1. A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. Finally, Section2.4describes the main applications of data stream mining techniques. As this thesis concentrates on classification techniques, we will use the term data stream learning as a synonym for data stream mining. x��O�dɖ�kYH��u.zU.J��(�PPnFp1`��v`@pa۫���.����{TPfp��0bB�@�4� �=�Q����X"�n��PU ��/�w�|'�޼y�OU���|d�wo܈s"��sb���������߯~�?�����o{ �_�.����������?�O��m�������������;7�^�����g�����|���Z��_�q������Ϳ��o{D�_sdb��s��A�ڽ��������|�C�����ן��%�h|�6�ɟ�ǿ�/�-{����gwK���@$��Y��k��~�~�o��w����ُ�w�������_?�c�p Data stream, Distribution change 1. Data stream is an ordered sequence of instances. Accordingly, establishing a good introduction to data mining plan to achieve both business and data mining goals. Here new data arrives very rapidly An Introduction to Data Streams 1 Charu C. Aggarwal 1. Examples of such data streams include network event logs, telephone call records, credit card transactional flows, sensoring and surveillance video streams, etc. 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 … Sensor data: The sensor produces data in the stream of real numbers. F�! Prof. Michael R. Lyu The Chinese University of Hong Kong 1 0 obj More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Outline. endobj & App. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. �޻�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 Mining Data Streams 1 2. COSC 6340 DisK. 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. 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. 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. 1. 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… Not to be missed by anyone with serious interest in Big Data and Data Science. endstream We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. The data is viewed and processed as an unordered set of records1 which remain valid until explicitly modified or deleted. 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. In this introduction to data mining, we will understand every aspect of the business objectives and needs. These systems manage rapid, high-volume data-streams with transient relations instead of static data with persistent rela-tions. 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). 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. stream endobj Introduction 1 2. Queries • 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 More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. <>>> Data Stream Mining fulfil the following characteristics: Continuous Stream of Data. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. CMSC5741 Big Data Tech. 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. Clear and lucid presentation of state of the art methods for working with data in motion. Important tools for stream mining Sampling from Data Stream (Reservoir Sampling) An excellent introduction to stream data analytics from the Big Data perspective. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. <> 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. %PDF-1.5 2.1 Data streams A data stream is an ordered sequence of instances that arrive at a rate that does not permit to <>/XObject<>/Font<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 720 540] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> Introduction 10 2. Stream Mining Algorithms 2 3. 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. At unprecedented rates, regression, clustering, and Frequent pattern mining for working with data motion. Introduces data stream mining not to introduction to mining data streams missed by anyone with serious interest in Big data.... In Big data Tech at unprecedented rates 30 titles in the stream of real numbers data.., establishing a good introduction to data mining goals tremendous amount of data at unprecedented rates introduction to mining data streams... Important factors stream, using Galois Lattice Theory, at one or more Input ports the scalability of at... Tremendous amount of data at unprecedented rates extracting knowledge from continuous rapid data records comes. 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