Decision-support information systems notably rest on data warehouses and on-line analytical processing (OLAP), two pillars of business intelligence (BI) that are nowadays confronted to new scientific and technological challenges. Traditional BI indeed rests on so-called classical data warehouses that can handle hundreds of terabytes of data, but hardly manage little-structured or unstructured data such as texts and graphs, nor quickly incoming data, thus inefficiently supporting real-time BI.
In parallel, with the emergence of big data-related technologies and the diffusion of data mining and machine learning algorithms, new perspectives open for analytical and decision-support uses. The proliferation of little-structured or unstructured data, data flows from, e.g., sensors, the Internet of things or social media, coming in huge volumes, is indeed a huge opportunity for BI and sparkles new research areas in data warehousing and OLAP.
Data warehousing and OLAP still operate in various organizations to structure and analyze both legacy and new data. They retain a key role in piloting such organizations (administrations, companies, hospitals…). Then the question is how to preserve their strengths while moving data warehouses and OLAP toward solving big data issues? Big data bring us into a new scientific and technological era offering architectures and infrastructures (clouds, Hadoop-like computing, NoSQL databases…) that allow better data management and analytics for decision-making. Such an evolution raises new problems that require the design of new approaches for data integration, modeling, querying, analysis, optimization and security, both in traditional and big data warehouses.
Since 2005, the French-speaking conference on data warehousing and OLAP (EDA) has been offering a regular meeting framework to researchers, industrials and users interested in the latest scientific and technological advances in this domain. We call all researchers and experts in this domain to present their work. We particularly encourage young researchers to participate. Submitted papers may be written either in French or in English and must describe original research and applications related to the following topics (non-exhaustive list).
Theme #1 – Data warehousing and OLAP
- Data warehouse architectures
- Data warehouse design
- Extract, transform, load (ETL)
- Data warehouse physical organization
- Multidimensional modeling
- On-line analytical processing (OLAP)
- Data warehouse maintenance and administration
- Performance optimization and tuning, benchmarks
Theme #2 – Big data management
- Big data integration and modeling
- Big data quality and integrity
- Big data security and reliability
- Data lakes
- NoSQL databases
- Text, graph, stream… data
- Distributed environments: Hadoop, Spark…
- Big data engineering: virtualization, elasticity, distributed computing, big data frameworks
- Semantic web, ontologies
Theme #3 – Big data analytics
- Business analytics
- Data mining and machine learning
- Information retrieval
- Social media analysis
- Cloud BI
- Data visualization
Theme #4 – User-centric BI
- User-centric OLAP
- Personalization, recommendation
- On-demand BI
- Personal BI
Theme #5 – Applications
- Applications: administration, science, health, society, bioinformatics…
- Open data exploitation
- Alert systems
- Mobile applications
- Customer relationship management
Conference website: http://eric.univ-lyon2.fr/eda2017/
- January 20, 2017: Paper submission
- March 10, 2017: Notification
- March 28, 2017: Camera-ready copy
- May 3-5, 2017 Conference
Submitted papers may be written either in French or in English. At least three members of the program committee will review each of them. The conference proceedings will be published in a special issue of the French RNTI journal (Revue des Nouvelles Technologies de l’Information).
Papers must be submitted on EasyChair, comply with the RNTI format and be at most 15 pages long. Submissions may either be written in LaTeX or Word format, but in case of acceptation, the only format allowed by the publisher is LaTeX.
A selection of the best papers, extended and translated into English if necessary, will be submitted to a special issue of the International Journal of Data Warehousing and Mining (IJDWM).
For any further question, please contact eda2017 __AT__ eric.univ-lyon2.fr