Machine Learning on Big Data
(MLBD 2016)
in conjunction with
15th IEEE International Conference
on Machine Learning and Applications (IEEE ICMLA 2016)
December 18-20, 2016, Anaheim, CA, USA
[Aim and Scope | Workshop
Location | Submission Guidelines and
Instructions | Paper Publication | Important
Dates | Program Committee]
Best Papers of MLBD
2016 will be Invited for Extended Submission to a Top-Quality Journal
The
Special Session
“Machine Learning on Big Data” (MLBD 2016) of the 15th IEEE
International Conference on Machine Learning and Applications (IEEE ICMLA 2016)
focuses on machine learning models, techniques and algorithms related to Big
Data, a vibrant and challenging research context playing a leading role in the
Machine Learning and Data Mining research communities. Big data is gaining
attention from researchers, being driven among others by technological
innovations (such as cloud interfaces) and novel paradigms (such as social
networks). Devising and developing machine learning models, techniques and
algorithms for big data represent a fundamental problem stirred-up by the
tremendous range of critical applications incorporating machine learning tools
in their core platforms. For example, in application settings where big data
arise and machine is useful, we recognize, among other things: (i) machine-learning-based processing (e.g., acquisition,
knowledge discovery, and so forth) over large-scale sensor networks introduces
important advantages over classical data-management-based approaches; similarly,
(ii) medical and e-heath information systems usually include successful machine
learning tools for processing and mining very large graphs modelling
patient-to-disease, patient-to-doctor, and patient-to-therapy networks; (iii)
genome data management and mining can gain important benefits from machine
learning algorithms. Some hot topics in machine learning on big data include: (i) machine learning on unconventional big data sources
(e.g., large-scale graphs in scientific applications, strongly-unstructured
social networks, and so forth); (ii) machine learning over massive big data in
distributed settings; (iii) scalable machine learning algorithms; (iv) deep
learning – models, principles, issues; (v) machine-learning-based predictive
approaches; (vi) machine-learning-based big data analytics; (vii)
privacy-preserving machine learning on big data; (viii) temporal analysis and
spatial analysis on big data; (ix) heterogeneous machine learning on big data;
(x) novel applications of machine learning on big data (e.g., healthcare,
cybersecurity, smart cities, and so forth).
The
MLBD 2016 special session focuses on all the research aspects of machine
learning on Big Data. Among these, an unrestricted list includes:
The
Special Session
“Machine Learning on Big Data” (MLBD 2016) of the 15th IEEE
International Conference on Machine Learning and Applications (IEEE ICMLA 2016)
will be held in Anaheim, CA, USA, during December 18-20, 2016, and it aims to
synergistically connect the research community and industry practitioners. It
provides an international forum where scientific domain experts and Machine
Learning and Data Mining researchers, practitioners and developers can share
their findings in theoretical foundations, current methodologies, and practical
experiences on Machine Learning on Big Data. MLBD 2016 will provide a
stimulating environment to encourage discussion, fellowship, and exchange of
ideas in all aspects of research related to Machine Learning on Big Data. This
includes both original research contributions and insights from practical
system design, implementation and evaluation, along with new research
directions and emerging application domains in the target area. An expected
outcome from MLBD 2016 is the identification of new problems in the main
topics, and moves to achieve consolidated solutions to already-known problems.
Other goals are to help in creating a focused community of scientists who create
and drive interest in the area of Machine Learning on Big Data, and
additionally to continue on the success of the event across future years.
Anaheim, CA,
USA.
Submission Guidelines and Instructions
Contributions
are invited from prospective authors with interests in the indicated session
topics and related areas of application. All contributions should be high
quality, original and not published elsewhere or submitted for publication
during the review period.
Submitted
papers should strictly follow the IEEE official template. Maximum paper length allowed
is:
· Full
Papers: 6 pages (+2 extra pages);
· Short
Papers: 4 pages;
· Demo
Papers: 4 pages;
· Position
Papers: 4 pages;
Submitted
papers will be thoroughly reviewed by members of the Special Session Program
Committee for quality, correctness, originality and relevance. All accepted
papers must be presented by one of the authors, who must register.
Papers must be
submitted via the CMT System by selecting the track “Special Session on
Machine Learning on Big Data”.
Accepted
papers will appear in the proper ICMLA 2016 proceedings, published by IEEE.
Authors of
selected papers from the workshop will be invited to submit an extended version
of their paper to a special issue of a high-quality international journal.
Paper
submission: August 12, 2016
Notification of acceptance: September 15, 2016
Camera-ready paper due: October 1, 2016
Alfredo Cuzzocrea,
University of Trieste & ICAR-CNR, Italy
Program Committee
Michelangelo
Ceci, University of Bari, Italy
Alfredo Cuzzocrea, University of Trieste and ICAR-CNR, Italy
Joao Gama, University of Porto, Portugal
Marwan Hassani, TU Eindhoven, The Netherlands
Mark Last, Ben-Gurion University of the Negev, Israel
Carson K. Leung, University of Manitoba, Canada
Sofian Maabout, LABRI, Bordeaux University, France
Anirban Mondal, Shiv Nadar University, India
Enzo Mumolo, University of Trieste, Italy
Apostolos Papadopoulos, Aristotle University of Thessaloniki, Greece
For more information and any inquire, please contact Alfredo Cuzzocrea