Track 9

Track 9: Data Science

Track Chair:
Khalid Benabdeslem, University of Lyon, France

Nowadays, Data increases in a large scale in various fields. Huge volumes are generated every day by industries, Internet companies, medias and social networks. This explosive increase of big masses of structured and unstructured data need more efficient analysis that challenges the existing traditional tools. Within such context, Data Science aims to address the underlying scientific challenges associated to the enhancement of these large datasets with the purpose to extract knowledge from data, build real time decision-making tools and create high value- added services for citizens. Many scientific challenges can be mentioned ranging from storage, preprocessing, mining, visualizing massive, heterogeneous and complex datasets. In that sense, Data Science is naturally interdisciplinary and includes different fields such as machine learning, mathematical and computational statistics, information retrieval, optimization and visualization.
The purpose of this track is to bring together researchers who develop, investigate, or apply data science approaches in the context of real-world applications.
We welcome both research papers and system and application papers. Research papers describe innovative research on all aspects of data science, from theoretical foundations work to novel models and algorithms. System and application papers describe applied work addressing real-world problems and systems demonstrating tangible impact in their respective domains. Topics of interest include, but are not limited to:

• Data mining
• Machine learning and statistics
• Modeling and forecasting
• Dataset retrieval and search
• Data cleaning
• New visualization techniques for massive data
• Business analytics
• Large-scale data analytics
• Large-scale computation
• Optimization for big data
• Provenance management and analytics
• Reproducibility and transparency
Examples of application areas include, but are not limited to:
• Finance
• Marketing and advertising
• Bioinformatics
• Healthcare
• Social sciences
• Recommender platforms
• Logistics
• Transportation
• Urban planning
• Public safety and crime prevention
• Resource management (energy, water, air quality, waste management)
• Smart cities
• Environmental protection
• Telecommunications
• Security

PC Members

- Omar Al Zoubi, Jordan University, Jordan
- Ferhat Attal, UPEC, France
- Khalid Benabdeslem, University of Lyon, France
- Bruno Canitia, Lizeo IT, France
- Amal Elgammal, Servtech, Germany
- Hakim Hacid, Cheikh Zaid University, UAE
- Dou El Kefel Mansouri, University of Tiaret, Algeria
- José Luis Zechinelli-Martini, UDLAP, Mexico
- Javier Espinosa, University of Lyon, France
- Hassan Badir, ENSA Tangier, Morooco
- João Batista de Souza Neto, UFRN, Brazil
- Lamia Labed, ISG, Tunisia