Friday, September 22, 2017

[DMANET] Special Issue on Parallel and Distributed Data Mining

Special Issue on Parallel and Distributed Data Mining
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


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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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