Data Mining and Knowledge Discovery for Big Data_ Methodologies, Challenge and Opportunities [Chu 2013-10-09](1).pdf

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Studies in Big Data 1
Wesley W. Chu
Editor
Data Mining
and Knowledge
Discovery
for Big Data
Methodologies,
Challenge and Opportunities
Studies in Big Data
Volume 1
Series Editor
Janusz Kacprzyk, Warsaw, Poland
For further volumes:
http://www.springer.com/series/11970
Wesley W. Chu
Editor
Data Mining and Knowledge
Discovery for Big Data
Methodologies, Challenge and Opportunities
ABC
Editor
Wesley W. Chu
Department of Computer Science
University of California
Los Angeles
USA
ISSN 2197-6503
ISBN 978-3-642-40836-6
DOI 10.1007/978-3-642-40837-3
ISSN 2197-6511 (electronic)
ISBN 978-3-642-40837-3 (eBook)
Springer Heidelberg New York Dordrecht London
Library of Congress Control Number: 2013947706
c
Springer-Verlag Berlin Heidelberg 2014
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Preface
The field of data mining has made significant and far-reaching advances over
the past three decades. Because of its potential power for solving complex
problems, data mining has been successfully applied to diverse areas such as
business, engineering, social media, and biological science. Many of these ap-
plications search for patterns in complex structural information. This trans-
disciplinary aspect of data mining addresses the rapidly expanding areas of
science and engineering which demand new methods for connecting results
across fields. In biomedicine for example, modeling complex biological sys-
tems requires linking knowledge across many levels of science, from genes
to disease. Further, the data characteristics of the problems have also grown
from static to dynamic and spatiotemporal, complete to incomplete, and cen-
tralized to distributed, and grow in their scope and size (this is known as
big
data).
The effective integration of big data for decision-making also requires
privacy preservation. Because of the board-based applications and often in-
terdisciplinary, their published research results are scattered among journals
and conference proceedings in different fields and not limited to such jour-
nals and conferences in knowledge discovery and data mining (KDD). It is
therefore difficult for researchers to locate results that are outside of their
own field. This motivated us to invite experts to contribute papers that sum-
marize the advances of data mining in their respective fields.Therefore, to
a large degree, the following chapters describe problem solving for specific
applications and developing innovative mining tools for knowledge discovery.
This volume consists of nine chapters that address subjects ranging from
mining data from opinion, spatiotemporal databases, discriminative subgraph
patterns, path knowledge discovery, social media, and privacy issues to the
subject of computation reduction via binary matrix factorization. The fol-
lowing provides a brief description of these chapters.
Aspect extraction and entity extraction are two core tasks of aspect-based
opinion mining. In Chapter 1, Zhang and Liu present their studies on people’s
opinions, appraisals, attitudes, and emotions toward such things as entities,
products, services, and events.

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