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 datamining. A way to address datamining 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 metaheuristics seem to be good candidates to tackle them. But, it should be noticed that metaheuristics 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 metaheuristics 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:

• Metaheuristics for supervised datamining tasks (classification, association rules…)
• Metaheuristics for unsupervised datamining tasks (clustering, bi-clustering…)
• Metaheuristics for mining heterogeneous data
• Metaheuristics for text mining
• Multi-objective models for datamining tasks

Important date

Submission: May 15th 2017 May 31 2017

• Decision Notification to Authors: June 30, 2017
• Camera Ready Submission Deadline: July 15, 2017
• Deadline for early Registration as Presenting Author: July 15, 2017

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 “metaheuristics 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 datamining task as combinatorial optimization problems, solving methods based on metaheuristics, incorporate learning in metaheuristics and multiobjective 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 datamining task as combinatorial optimization problems, solving methods based on metaheuristics, incorporate learning in metaheuristics 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