Machine Learning on Big Data
in conjunction with
December 18-21, 2017, Cancun, Mexico
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.
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.
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
Danilo Amendola, IRCCS “Bonino-Pulejo”
Enzo Mumolo, University of Trieste, Italy
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