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| 001 | 000045321472 | |
| 005 | 20070122155445 | |
| 008 | 070110s2007 nyua b 001 0 eng d | |
| 020 | ▼a 0387287590 | |
| 020 | ▼a 9780387287591 | |
| 035 | ▼a (OCoLC)77212493 | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 040 | ▼a MUB ▼c MUB ▼d YDX ▼d YDXCP ▼d FDA | |
| 049 | ▼a FDAA | |
| 050 | ▼a QA76.9.D343 ▼b D39 2007 | |
| 082 | 0 4 | ▼a 006.3 ▼2 22 |
| 090 | ▼a 006.3 ▼b D2323 | |
| 245 | 0 0 | ▼a Data streams : ▼b models and algorithms / ▼c edited by Charu C. Aggarwal. |
| 260 | ▼a New York : ▼b Springer , ▼c c2007. | |
| 300 | ▼a xviii, 354 p. : ▼b ill. ; ▼c 25 cm. | |
| 440 | 0 | ▼a Advances in database systems. |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Algorithms. |
| 650 | 0 | ▼a Computer science ▼x Mathematics. |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Aggarwal, Charu C. |
| 945 | ▼a KINS |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.3 D2323 | 등록번호 121137745 (3회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject.
This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. It is intended for a professional audience, but is also appropriate for advanced-level students in computer science.
Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.
This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions.
Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
New feature
In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams.
Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.
Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.
Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.
정보제공 :
저자소개
차루 C. 아가르왈(엮은이)
뉴욕 요크타운 하이츠의 IBM T. J. 왓슨 리서치 센터의 뛰어난 연구 회원(DRSM)이다. 1993년 IIT Kanpur에서 학사 학위를 받았고 1996년 MIT에서 박사 학위를 받았다. 데이터 마이닝 분야에서 폭넓게 일해왔고, 400개 이상의 논문을 콘퍼런스와 학술지에 발표했으며 80개 이상의 저작 특허권이 있다. 데이터 마이닝에 관한 교과서, 특이치 분석에 관한 포괄적인 책을 포함한 15권의 책을 저술하거나 편집했다. 특허의 상업적 가치 덕분에 IBM에서 마스터 발명가로 세 번이나 지정됐다. 데이터 스트림에서 생물 테러리스트 위협 탐지에 대한 연구로 IBM 기업상(2003)을 수상했고, 프라이버시 기술에 대한 과학적인 공헌으로 IBM 우수 혁신상(2008)을 수상했다. 데이터 스트림 및 고차원적인 작업에 대한 각각의 작업을 인정받아 두 개의 IBM 우수 기술 성과상(2009, 2015)을 수상했다. 응축 기반 프라이버시 보존 데이터 마이닝에 관한 연구로 EDBT 2014 Test of Time Award를 수상했다. 또한 데이터 마이닝 분야에서 영향력 있는 연구 공헌에 대한 두 가지 최고상 중 하나인I EEE ICDM 연구 공헌상(2015)을 수상했다. IEEE 빅데이터 콘퍼런스(2014)의 총괄 공동 의장직과 ACM CIKM 콘퍼런스(2015), IEEE ICDM 콘퍼런스(2015), ACM KDD 콘퍼런스(2016) 프로그램 공동 의장직을 역임했다. 2004년부터 2008년까지 「IEEE Transactions on Knowledge and Data Engineering」의 부편집장으로 일했다. 「ACM Transactions on Knowledge Discovery from Data」의 부편집장, 「IEEE Transactions on Big Data」의 부편집장, 「Data Mining and Knowledge Discovery Journal」과 「ACM SIGKDD Exploration」의 편집장, 「Knowledge and Information Systems Journal」의 부편집장이다. Springer의 간행물인 「Lecture Notes on Social Networks」 자문 위원회에서 활동하고 있으며 데이터 마이닝에 관한 SIAM 활동 그룹의 부사장을 역임했다. "contributions to knowledge discovery and data mining algorithms"에 관한 SIAM, ACM, IEEE의 펠로우다.
목차
An Introduction to Data Streams.- On Clustering Massive Data Streams: A Summarization Paradigm.- A Survey of Classification Methods in Data Streams.- Frequent Pattern Mining in Data Streams.- A Survey of Change Diagnosis Algorithms in Evolving Data Streams.- Multi-Dimensional Analysis of Data Streams Using Stream Cubes.- Load Shedding in Data Stream Systems.- The Sliding-Window Computation Model and Results.- A Survey of Synopsis Construction in Data Streams.- A Survey of Join Processing in Data Streams.- Indexing and Querying Data Streams.- Dimensionality Reduction and Forecasting on Streams.- A Survey of Distributed Mining of Data Streams.- Algorithms for Distributed Data Stream Mining.- A Survey of Stream Processing Problems and Techniques in Sensor Networks.
정보제공 :