Details of Session (including aim and scope):
This special session focuses on Data Science and Big Data which are two
of the hottest research areas in computer science and business. In recent
years, Big Data has attracted many researchers and business people in various
fields. Big Data is a resource which creates added value by using a data
science approach leading to significant innovations. We want to attract
researchers and business people whose expertise is related to Big Data
and Data Science, and encourage the sharing of information.
Technical issues include (but are not limited to):
High performance computing
Text and semi-structured data mining
Statistics and probability
Service Ontologies and Modelling
All Invited Session papers should be submitted through PROSE online paper
submission system. (PROSE submission system will open soon.) Please follow
the instructions at the KES 2014 website.
Papers will be reviewed by independent experts for their originarity, significance,
creativity and applicability.
All accepted papers must be presented by
one of the authors who must register and pay fees.
The papers should be no longer than eight pages in LNCS format.
Full papers should be detailed academic articles in conventional format.
The guide length for full papers is 8 to 10 pages (maximum). .. here ..
* We announce that the paper format has been changed as follows:
Guidance notes for the preparation of Full Papers is available .. here ..
An MS Word template is available .. here ..
A LaTeX template is available .. here ..
Submission of papers:
15 March 2014 11 April 2014
Notification of acceptance: 1 May 2014
Final paper to be received by: 1 June 2014
DSI Project in Kansai University
Data Mining Laboratory in Kansai University
Michelle Chen, University of Connecticut
Michele Gorgoglione, Politecnico di Bari
Naoki Katoh, Kyoto University
Wataru Sunayama, Hiroshima City University
Shusaku Tsumoto, Shimane University
Dirk Van den Poel, Ghent University
Takashi Washio, Osaka University
Yada, Katsutoshi (Kansai University, Japan)
Takahira Yamaguchi, Keio University
Katsutoshi Yada, Ph.D.
Professor of Management Information System
Data Mining Laboratory, Kansai University.
OSAKA, 564-8680, JAPAN.
[Contact email for this session]