Special Sessions & Tutorials

MOD 2017  Special Sessions and Tutorials


Special Session

Metaheuristics and Multi-Objective Optimization for Big Data

Clarisse Dhaenens, University of Lille, France  – Clarisse.Dhaenens@univ-lille1.fr
Laetitia Jourdan, University of Lille, France – laetitia.jourdan@univ-lille1.fr


Aim and scope

Even if the term Big Data, is not always used with the same meaning, man agrees to say that it brings many challenges. When regarding the whole process related to the big-data context, starting from the generation of data, its storage and management, and analyzes that can be driven in order to help decision making, at each phase some important challenges arise. Indeed, during the generation and capture of data, some challenges may be related to technological aspects linked to the acquisition of real-time data, for example. But at this phase, challenges are also related to the identification of meaningful data. The storage and management phase leads to two critical challenges, first on the infrastructures for the storage of data and its transportation, but also on conceptual models to provide well-formed available data that may be used for analysis purpose. Then, the analysis phase has its own challenges, with the manipulation of heterogeneous massive data. In particular, when considering the knowledge extraction, in which unknown patterns have to be discovered, analysis may be very complex due to the nature of data manipulated. This is the heart of the data mining. A way to address data mining problems is to model them as optimization problems that can be of multi-objective nature. In the context of Big Data, most of these problems are large scale ones. Hence meta-heuristics seem to be good candidates to tackle them. But, it should be noticed that meta-heuristics are not only suitable to address the large size aspect of the problem but also to deal with other aspects of Big Data, such as variety and velocity for example. The aim of this special session is to group contributions in which meta-heuristics and multi-objective optimization can provide answers to some of the challenges induced by the Big Data context, and in particular within the data analytics phase. The scope of the special session MMO-BD includes, but is not limited to the following topics:

– Meta-heuristics for supervised data mining tasks (classification, association rules…)

– Meta-heuristics for unsupervised data mining tasks (clustering, bi-clustering…)

– Meta-heuristics for mining heterogeneous data

– Meta-heuristics for text mining

– Multi-objective models for data mining tasks

Short bio of the organizers

o Clarisse Dhaenens (Professor, CRIStAL, Univ Lille / CNRS, France) Clarisse Dhaenens is a full professor at the University of Lille. She is currently the vice-head of CRIStAL research laboratory. She obtained her PhD in 1998 from the polytechnicum University of Grenoble (INPG). She became an associate professor in 1999 at the University of Lille and a full professor in 2006. Clarisse Dhaenens works deal with operations research, combinatorial optimization with applications in knowledge discovery for bioinformatics and healthcare. She is, for example, interested in multi-objective optimization and links between structures of problems and their solving. She has just written a book with Laetitia Jourdan “meta-heuristics for big-data”

o Laetitia Jourdan (Professor, CRIStAL, Univ Lille / CNRS, France) Pr. Laetitia JOURDAN (F) is currently full Professor in Computer Sciences at University of Lille/CRIStAL. Her areas of research are modeling data mining task as combinatorial optimization problems, solving methods based on meta-heuristics, incorporate learning in meta-heuristics and multi-objective optimization. Pr. Jourdan received a master degree in computer science and mathematics for University Paris Dauphine in 1999. Pr. Jourdan hold a PhD in combinatorial optimization from the University of Lille 1 (France). From 2004 to 2005, she was research associate at University of Exeter (UK). Then she was researcher with tenure at INRIA. She holds her dissertation to lead researches (“HDR: Habilitation à Diriger des Recherches”) from the Univ. of Lille in 2010. Her areas of research are modeling data mining task as combinatorial optimization problems, solving methods based on meta-heuristics, incorporate learning in meta-heuristics and multi objective optimization with application to health and bioinformatics. She directed and co-supervised nine PhD and twelve Master students. She is (co)author of more than 100 papers published in international journals, book chapters, and conference proceedings. She organized several international conferences (LION 2015, MIC 2015, etc) and is reviewer editor for frontier in Big Data

o Contact information of the organizers

Clarisse Dhaenens CRIStAL Univ. Lille / CNRS France CRIStAL Bat M3 Cité Scientifique 59655 Villeneuve d’Ascq Cedex FRANCE Clarisse.Dhaenens@univlille1.fr http://www.cristal.univlille.fr/~dhaenens/

Laetitia Jourdan CRIStAL Univ. Lille / CNRS France CRIStAL Bat M3 Cité Scientifique 59655 Villeneuve d’Ascq Cedex FRANCE Laetitia.Jourdan@univlille1.fr http://www.cristal.univlille.fr/~jourdan/





Scalable Data Mining on Cloud Computing Systems
Domenico Talia

Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica

Università della Calabria, Italy

Summary. The analysis of the massive and distributed data repositories is a challenging task and it requires the combined use of intelligent data analysis techniques, machine learning algorithms, and scalable architectures to find and extract useful information from them. Parallel computers, distributed systems and Cloud computing platforms offer an effective support for addressing both the computational and data storage needs of Big Data mining and parallel analytics applications. In fact, complex data mining tasks involve data- and compute-intensive algorithms that require large storage facilities together with high performance processors to get results in suitable times. In this tutorial we introduce the most relevant topics and the main research issues in high performance data mining including parallel data mining strategies, distributed analysis techniques, and Cloud-based data mining. We also present some data mining frameworks designed for developing distributed data analytics applications as workflows of services on Clouds. In these environment data sets, analysis tools, data mining algorithms and knowledge models are implemented as single services that are combined through a visual programming interface in distributed workflows. Application design and execution of data analysis use cases are discussed. Programming issues on exascale systems and applications will be also introduced.

Syllabus. Parallel data mining techniques, distributed data mining, Cloud-based data analytics workflows, exascale programming.

Day: TBA

Short CV of the lecturer: Domenico Talia is a full professor of computer engineering at the University of Calabria. He is a partner of two startups, Exeura and DtoK Lab. His research interests include parallel and distributed data mining, cloud computing, social data analysis, mobile computing, peer-to-peer systems, and parallel programming. Talia published ten books and more than 300 papers in archival journals such as CACM, Computer, IEEE TKDE, IEEE TSE, IEEE TSMC-B, IEEE Micro, ACM Computing Surveys, FGCS, Parallel Computing, IEEE Internet Computing and international conference proceedings. He is a member of the editorial boards of IEEE Transactions on Cloud Computing, the Future Generation Computer Systems journal, the International Journal on Web and Grid Services, the Scalable Computing: Practice and Experience journal, MultiAgent and Grid Systems: An International Journal, International Journal of Web and Grid Services, and the Web Intelligence and Agent Systems International journal. Talia has been a project for several international institutions such as the European Commission, Aeres in France, Austrian Science Fund, Croucher Foundation, and the Russian Federation Government. He served as a chair, organizer, or program committee member of several international conferences and gave many invited talks and seminars in conferences and schools. Talia is a member of the ACM and the IEEE Computer Society.

Industrial Session on Machine Learning, Optimization and Data Science for Real-World Applications

Day: TBA 

MOD 2017 Industrial Session aims to bring together participants from academia and industry in a venue that highlights practical and real-world studies of machine learning, optimization and data science. 

The ultimate goal of this event is to encourage mutually-beneficial exchange between scientific researchers and practitioners working to improve data science analytics. 

The session will consist of a series of invited presentations from leading experts in industry on selected topics in machine learning, optimization and data science from industry perspective and with a special focus on real-world applications. 




The MOD 2017 Organizing Committee invites proposals for Special Sessions and Tutorials.

Call for Special Sessions

Special session proposals are invited to the 3rd International Conference on Machine learning, Optimization and big Data (MOD) to be held in Volterra (Pisa) Tuscany, Italy on September 14-17, 2017.

A special session proposal should include the title, aim and scope of the proposed session, list of potential contributors, and the names, e-mail addresses, affiliations and short bios of the organizers.

Special session proposals will be evaluated based on the timeliness of the topic, its uniqueness, and qualifications of the proposers. The proposers are expected to have a PhD degree and have a good publication track record in the proposed area. A tentative accept/reject decision on the proposal will be sent to the proposers within a few weeks after its receipt by the Special Sessions Chair. Accepted special sessions will be listed on the website. However, it is likely that an accepted proposal will be combined with similar proposals to avoid multiple special sessions covering a similar topic. A final decision will be made two weeks after the special session proposal deadline (April 15, 2017).

Submissions of papers to special sessions should be done through the paper submission website of MOD 2017 where authors can choose a special session title as the main topic of their paper from a list of regular session topics and special session titles. All papers submitted to special sessions will be subject to the same peer-review review procedure as the regular papers. Special sessions having fewer than accepted papers will be cancelled and the accepted papers will be moved to regular sessions.

Special Session Proposals should be sent by email to:  modworkshop2017@gmail.com

All Special Session Proposals should be submitted by March 31, 2017

Call for Tutorials

The internal organization of the satellite tutorials is entirely left up to their respective organizers. MOD 2017 provides the onsite logistics (seminar rooms, projectors and coffee breaks).

Tutorials Information and/or submission proposals: modworkshop2017@gmail.com

Tutorial Proposals

MOD 2017 tutorials will be presented by domain experts to cover current topics relevant to Machine Learning, Optimization and/or Big Data researchers and practitioners. Each tutorial will be 2 hours, then we encourage to include into the tutorial also demos and interactive activities. Accepted tutorial’s slide sets will be published on MOD 2017 website.

Submission Process

Each tutorial proposal should include:

  1. title of the tutorial;
  2. name and affiliation of the lecturer, with relative contact details;
  3. a short CV of the lecturer;
  4. a brief description (half-page) of the tutorial topics.

All tutorial proposals must be sent to: modworkshop2017@gmail.com

Important Dates

  • Submission Tutorial proposals: February  15, 2017 
  • Notification: February 26, 2017
  • MOD 2017 Conference: September 14-17, 2017


Important Dates

  • Submission Special Session proposals: March 31, 2017 
  • Special Session Notification: April  15, 2017
  • MOD 2017 Conference: September 14-17, 2017