Large-Scale Parallel Data Mining [Zaki & Ho 2000-02-23](1).pdf

(3910 KB) Pobierz
Lecture Notes in Artificial Intelligence
Subseries of Lecture Notes in Computer Science
Edited by J. G. Carbonell and J. Siekmann
1759
Lecture Notes in Computer Science
Edited by G. Goos, J. Hartmanis and J. van Leeuwen
Mohammed J. Zaki Ching-Tien Ho (Eds.)
Large-Scale
Parallel Data Mining
13
Series Editors
Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA
J¨ rg Siekmann, University of Saarland, Saarbr¨ cken, Germany
o
u
Volume Editors
Mohammed J. Zaki
Computer Science Department
Rensselaer Polytechnic Institute
Troy, NY 12180, USA
E-mail: zaki@cs.rpi.edu
Ching-Tien Ho
K55/B1, IBM Almaden Research Center
650 Harry Road, San Jose, CA 95120, USA
E-mail: ho@almaden.ibm.com
Cataloging-in-Publication Data applied for
Die Deutsche Bibliothek - CIP-Einheitsaufnahme
Large scale parallel data mining / Mohammed J. Zaki ; Ching-Tien Ho (ed.)
- Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London ;
Milan ; Paris ; Singapore ; Tokyo : Springer, 2000
(Lecture notes in computer science ; Vol. 1759 : Lecture notes in
artificial intelligence)
ISBN 3-540-67194-3
CR Subject Classification (1991): I.2.8, I.2.11, I.2.4, I.2.6, H.3, F.2.2, C.2.4
ISBN 3-540-67194-3 Springer-Verlag Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is
concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting,
reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication
or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965,
in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are
liable for prosecution under the German Copyright Law.
Springer-Verlag is a company in the specialist publishing group BertelsmannSpringer
c Springer-Verlag Berlin Heidelberg 2000
Printed in Germany
Typesetting: Camera-ready by author, data conversion by Christian Grosche
Printed on acid-free paper
SPIN 10719635
06/3142
543210
Preface
With the unprecedented rate at which data is being collected today in almost all
fields of human endeavor, there is an emerging economic and scientific need to
extract useful information from it. For example, many companies already have
data-warehouses in the terabyte range (e.g., FedEx, Walmart). The World Wide
Web has an estimated 800 million web-pages. Similarly, scientific data is reach-
ing gigantic proportions (e.g., NASA space missions, Human Genome Project).
High-performance, scalable, parallel, and distributed computing is crucial for
ensuring system scalability and interactivity as datasets continue to grow in size
and complexity.
To address this need we organized the workshop on Large-Scale Parallel KDD
Systems, which was held in conjunction with the 5th ACM SIGKDD Interna-
tional Conference on Knowledge Discovery and Data Mining, on August 15th,
1999, San Diego, California. The goal of this workshop was to bring researchers
and practitioners together in a setting where they could discuss the design, im-
plementation, and deployment of large-scale parallel knowledge discovery (PKD)
systems, which can manipulate data taken from very large enterprise or scien-
tific databases, regardless of whether the data is located centrally or is globally
distributed. Relevant topics identified for the workshop included:
–
How to develop a rapid-response, scalable, and parallel knowledge discovery
system that supports global organizations with terabytes of data.
–
How to address some of the challenges facing current state-of-the-art data
mining tools. These challenges include relieving the user from time and vol-
ume constrained tool-sets, evolving knowledge stores with new knowledge
effectively, acquiring data elements from heterogeneous sources such as the
Web or other repositories, and enhancing the PKD process by incrementally
updating the knowledge stores.
–
How to leverage high performance parallel and distributed techniques in
all the phases of KDD, such as initial data selection, cleaning and prepro-
cessing, transformation, data-mining task and algorithm selection and its
application, pattern evaluation, management of discovered knowledge, and
providing tight coupling between the mining engine and database/file server.
–
How to facilitate user interaction and usability, allowing the representation
of domain knowledge, and to maximize understanding during and after the
process. That is, how to build an adaptable knowledge engine which supports
business decisions, product creation and evolution, and leverages information
into usable or actionable knowledge.
This book contains the revised versions of the workshop papers and it also
includes several invited chapters, to bring the readers up-to-date on the recent
developments in this field. This book thus represents the state-of-the-art in paral-
lel and distributed data mining methods. It should be useful for both researchers
VI
Preface
and practitioners interested in the design, implementation, and deployment of
large-scale, parallel knowledge discovery systems.
December 1999
Mohammed J. Zaki
Ching-Tien Ho
Workshop Chairs
Workshop Chair: Mohammed J. Zaki (Rensselaer Polytechnic Institute, USA)
Workshop Co-Chair: Ching-Tien Ho (IBM Almaden Research Center, USA)
Program Committee
David Cheung (University of Hong Kong, Hong Kong)
Alok Choudhary (Northwestern University, USA)
Alex A. Freitas (Pontifical Catholic University of Parana, Brazil)
Robert Grossman (University of Illinois-Chicago, USA)
Yike Guo (Imperial College, UK)
Hillol Kargupta (Washington State University, USA)
Masaru Kitsuregawa (University of Tokyo, Japan)
Vipin Kumar (University of Minnesota, USA)
Reagan Moore (San Diego Supercomputer Center, USA)
Ron Musick (Lawrence Livermore National Lab, USA)
Srini Parthasarathy (University of Rochester, USA)
Sanjay Ranka (University of Florida, USA)
Arno Siebes (Centrum Wiskunde Informatica, Netherlands)
David Skillicorn (Queens University, Canada )
Paul Stolorz (Jet Propulsion Lab, USA)
Graham Williams (Cooperative Research Center for Advanced Computational
Systems, Australia)
Acknowledgements
We would like to thank all the invited speakers, authors, and participants for
contributing to the success of the workshop. Special thanks are due to the pro-
gram committee for their support and help in reviewing the submissions.

Plik z chomika:

Inne pliki z tego folderu:

Inne foldery tego chomika:

Zgłoś jeśli naruszono regulamin