• The 9th International ACM Conf. on Management of Digital EcoSystems (MEDES'17) From 7-10 November 2017, Bangkok - Thailand

  • Paper Submission Deadline: May 20th, 2017 ____________________________________________ 7-10 November 2017, Bangkok - Thailand

  • Several Special Tracks/Workshops are organized _______________________________________ 7-10 November 2017, Bangkok - Thailand

  • Several interesting topics are addressed (Big data, web, intelligence, security) _______________ 7-10 November 2017, Bangkok - Thailand

  • Several keynotes will be provided during the conference __________________________________ 7-10 November 2017, Bangkok - Thailand

Big Data Analytics:
SQL to Hadoop and Beyond
Use of Data Analytics &
Computational Intelligence
for services computing
Nicolas Spyratos
Marouane Kessentini

Big Data Analytics: From SQL to Hadoop and Beyond


 by Nicolas Spyratos

University of Paris South


We present in this talk a high level query language, called HiFun, for defining analytic queries over big data sets. An analytic query in HiFun is defined to be a well-formed expression of a functional algebra, whose operations combine functions to create HiFun queries (in much the same way as the operations of the relational algebra combine relations to create relational algebra queries. We show that a HiFun query can be encoded as a map-reduce job, and also as a SQL group-by query when the data set resides in a relational database. We also present a formal method for rewriting HiFun queries and defining query execution plans. As a case study, we show how the rewriting method for HiFun queries can be applied in the rewriting of map-reduce jobs and SQL group-by queries.


Nicolas Spyratos is currently professor emeritus at the University of Paris South, scientific advisor of the Japan Science and Technology agency (JST), member of the Greek National Council of Research and Innovation and Adjunct Senior Researcher at the FORTH Institute of Computer Science in Greece. His research interests include databases, big data analytics, digital libraries and conceptual modeling. He has published over 200 papers in refereed international journals and conferences and has participated in over 20 European and international research projects. He has supervised 24 doctoral theses and has been evaluator for the European programs Esprit and Esprit-Bra as well as for the National Science Foundation (NSF) and major scientific journals.

Use of Data Analytics & Computational Intelligence for Services Computing


 by Marouane Kessentini

University of Michigan-Dearborn


A growing trend has begun in recent years to move software engineering problems from human-based search to machine-based search that balances a number of constraints to achieve optimal or near-optimal solutions. As a result, human effort is moving up the abstraction chain to focus on guiding the automated search, rather than performing the search itself. This emerging software engineering paradigm is known as Search Based Software Engineering (SBSE). It uses data analytics and computational intelligence techniques, mainly those from the evolutionary computation literature to automate the search for optimal or near-optimal solutions to software engineering problems. The SBSE approach can and has been applied to many problems in software engineering that span the spectrum of activities from requirements to maintenance and reengineering. Already success has been achieved in requirements, refactoring, project planning, testing, maintenance and reverse engineering. However, several challenges have to be addressed to mainly tackle the growing complexity of software systems nowadays in terms of number of objectives, large amount of data (history of changes and commits), constraints and inputs/outputs. In this talk, I will give, first, an overview about SBSE then I will focus on some contributions that I proposed, along with my research group and my industrial partners, addressing the above challenges in the area of services computing, including: Web services quality, services composition, Services refactoring, etc. Finally, I will discuss possible new research directions in SBSE.


Marouane Kessentini is an Assistant Professor in the Department of Computer and Information Science at the University of Michigan, Dearborn, MI. He is the founder of the Search-Based Software Engineering (SBSE) research lab. He is recognized by many research surveys published in various venues as a leading research in the areas of SBSE and software refactoring. Dr. Kessentini has several collaborations with different industrial companies on the use computational search, machine learning and evolutionary algorithms to address several software engineering and services computing problems such as software quality, software testing, software migration, software evolution, services quality, services composition, services refactoring, etc. He received a best PhD award from University of Montreal in 2012 and a Presidential BSc Award from the President of Tunisia in 2007. He received many grants from both industry and federal agencies and published over 100 papers in software engineering, services computing and computational intelligence journals and conferences, including 3 best paper awards. He has served as program committee member in over 100 major conferences (GECCO, ASE, MODELS, ICWS, ICSOC, ICMT, SSBSE, etc.), an editorial board member of several journals, and an organization member of many conferences and workshops. He was also the co-chair of the SBSE track at the GECCO2014 and GECCO2015 conferences and he was the general chair of the 8th Search Based Software Engineering Symposium (SSBSE2016). He is also the founder of the North American Symposium on Search Based Software Engineering, funded by the National Science Foundation (NSF) and an invited speaker at the 2016 IEEE World Congress on Computational Intelligence (Vancouver, Canada).



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