Machine Learning on Big Data
(MLBD 2017)
in conjunction with
16th IEEE International Conference on
Machine Learning and Applications (IEEE ICMLA 2017)
December 18-21, 2017, Cancun, Mexico
[Aim and Scope | Workshop
Location | Submission Guidelines and
Instructions | Paper Publication | Important
Dates | Program Committee]
Best Papers of MLBD
2017 will be Invited for Extended Submission to a
Top-Quality Journal
The Special Session “Machine Learning on Big Data” (MLBD 2017)
of the 16th IEEE
International Conference on Machine Learning and Applications (IEEE ICMLA 2017)
follows the great success of the 2016
edition and 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 2017 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 2017) of the 16th IEEE
International Conference on Machine Learning and Applications (IEEE ICMLA 2017)
will be held in Cancun, Mexico, during December 18-21,
2017, 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
2017 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 2017 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.
Cancun,
Mexico.
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 2017 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, 2017
Notification of acceptance: September 9, 2017
Camera-ready paper due: October 1, 2017
Alfredo Cuzzocrea,
University of Trieste & ICAR-CNR, Italy
Technical Chairs
Danilo Amendola, IRCCS “Bonino-Pulejo”
Messina, Italy
Enzo Mumolo, University of Trieste, 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
Rocco Langone, Deloitte, Belgium
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