Information Sciences, Elsevier
The sheer volume of new data, which is being generated at an =
increasingly fast pace, has already produced an anticipated data deluge =
that is difficult to challenge. We are in the presence of an =
overwhelming vast quantity of data, owing to how easy is to produce or =
derive digital data. Even the storage of this massive amount of data is =
becoming a highly demanding task, outpacing the current development of =
hardware and software infrastructure. Nonetheless, this effort must be =
undertaken now for the preservation, organization and long-term =
maintenance of these precious data. However, the collected data is =
useless without our ability fully understand and make use of it. =
Therefore, we need new algorithms to address this challenge.
Data mining techniques and algorithms to process huge amount of data in =
order to extract useful and interesting information have become popular =
in many different contexts. Algorithms are required to make sense of =
data automatically and in efficient ways. Nonetheless, even though =
sequential computer systems performance is improving, they are not =
suitable to keep up with the increase in the demand for data mining =
applications and the data size. Moreover, the main memory of sequential =
systems may not be enough to hold all the data related to current =
applications.
This Special Issue takes into account the increasing interest in the =
design and implementation of parallel and distributed data mining =
algorithms. Parallel algorithms can easily address both the running time =
and memory requirement issues, by exploiting the vast aggregate main =
memory and processing power of processors and accelerators available on =
parallel computers. Anyway, parallelizing existing algorithms in order =
to achieve good performance and scalability with regard to massive =
datasets is not trivial. Indeed, it is of paramount importance a good =
data organization and decomposition strategy in order to balance the =
workload while minimizing data dependences. Another concern is related =
to minimizing synchronization and communication overhead. Finally, I/O =
costs should be minimized as well. Creating breakthrough parallel =
algorithms for high-performance data mining applications requires =
addressing several key computing problems which may lead to novel =
solutions and new insights in interdisciplinary applications.
Moreover, increasingly the data is spread among different geographically =
distributed sites. Centralized processing of this data is very =
inefficient and expensive. In some cases, it may even be impractical and =
subject to security risks. Therefore, processing the data minimizing the =
amount of data being exchanged whilst guaranteeing at the same time =
correctness and efficiency is an extremely important challenge. =
Distributed data mining performs data analysis and mining in a =
fundamentally distributed manner paying careful attention to resource =
constraints, in particular bandwidth limitation, privacy concerns and =
computing power.
The focus of this Special Issue is on all forms of advances in =
high-performance and distributed data mining algorithms and =
applications. The topics relevant to the Special Issue include (but are =
not limited to) the following.
TOPICS OF INTEREST
Scalable parallel data mining algorithms using message-passing, =
shared-memory or hybrid programming paradigms
Exploiting modern parallel architectures including FPGA, GPU and =
many-core accelerators for parallel data mining applications
Middleware for high-performance data mining on grid and cloud =
environments
Benchmarking and performance studies of high-performance data mining =
applications
Novel programming paradigms to support high-performance computing for =
data mining
Performance models for high-performance data mining applications and =
middleware
Programming models, tools, and environments for high-performance =
computing in data mining
Map-reduce based parallel data mining algorithms
Caching, streaming, pipelining, and other optimization techniques for =
data management in high-performance computing for data mining
Novel distributed data mining algorithms
SUBMISSION GUIDELINES
All manuscripts and any supplementary material should be submitted =
electronically through Elsevier Editorial System (EES) at =
http://ees.elsevier.com/ins (http://ees.elsevier.com/ins). The authors =
must select as =E2=80=9CSI:PDDM=E2=80=9D when they reach the =E2=80=9CArti=
cle Type=E2=80=9D step in the submission process.
A detailed submission guideline is available as =E2=80=9CGuide to =
Authors=E2=80=9D at: =
http://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-=
authors.
IMPORTANT DATES
Submission deadline: December 1th, 2017
First round notification: March 1th, 2018
Revised version due: May 1st, 2018
Final notification: June 1st, 2018
Camera-ready due: July 1st, 2018
Publication tentative date: October 2018
Guest editors:
Massimo Cafaro, Email: massimo.cafaro@unisalento.it
University of Salento, Italy and Euro-Mediterranean Centre on Climate =
Change, Foundation
Italo Epicoco, Email: italo.epicoco@unisalento.it
University of Salento, Italy and Euro-Mediterranean Centre on Climate =
Change, Foundation
Marco Pulimeno, Email: marco.pulimeno@unisalento.it
University of Salento, Italy
-=20
=
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**********
Massimo Cafaro, Ph.D.
Associate Professor =
=20
Dept. of Engineering for Innovation =20
University of Salento, Lecce, Italy =20
Via per Monteroni =20
73100 Lecce, Italy =
=20
Voice/Fax +39 0832 297371 =20=
Web http://sara.unisalento.it/~cafaro =
=20
E-mail massimo.cafaro@unisalento.it
cafaro@ieee.org
cafaro@acm.org
CMCC Foundation
Euro-Mediterranean Center on Climate Change
Via Augusto Imperatore, 16 - 73100 Lecce
massimo.cafaro@cmcc.it
=
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