Dauphine Digital – Our Research in Big Data Management
Big Data: Converting the World Into Data
In an increasingly digital world, large volumes of complex data (big data) are now central to any decision-making process.
Organizing this data to efficiently query it and envisioning new programming paradigms is a fundamental step in order to extract knowledge from this data, a crucial precondition in data science.
Big data
Semi-structured data
Graphs
Workflows
Web services
Integration
Languages
Crowdsourcing
Provenance
Scalability
Crucial application contexts:
The types of data researched at Dauphine-PSL are increasingly utilized in crucial application contexts.
- Workflow management
- Social networks
- Semantic Web
- Traffic analysis
- Detection/prevention of fraud and criminal activity
- Bioinformatics
Our Research in the University's Labs
The goal of studies conducted at Dauphine is to design, examine, and analyze, on an experimental basis, techniques for the management and analysis of semi-structured data, with a particular focus on web data and graph-structured data.
The research is structured around the following areas:
- Retrieval of aggregate information on workflows
- Secure, efficient processing of massive amounts of graphical data
- Big data integration via crowdsourcing
- Discovery, composition, and reliable execution of web services
- Aggregated search of data and services for linked data
Our Researchers
Dario Colazzo, Daniela Grigori, Khalid Belhajjame, Maud Manouvrier, Joyce Elhaddad, Elsa Negre, Marta Rukoz, and Michel Zamfiroiu
Published Research
- Baazizi MA., Colazzo D., Ghelli G., Sartiani C. Parametric schema inference for massive JSON datasets. VLDB J. 28(4) : 497-521 (2019)
- Baazizi MA., Colazzo D., Ghelli G., Sartiani C. Schemas and Types for JSON Data : From Theory to Practice. SIGMOD Conference 2019: 2060-2063
- Alper P., Belhajjame K., Curcin V., Goble CA. LabelFlow Framework for Annotating Workflow Provenance. Informatics 5(1): 11 (2018)
- Belhajjame K., Grigori D., Harmassi M., Ben Yahia M. Keyword-Based Search of Workflow Fragments and Their Composition. Trans. Computational Collective Intelligence 26 : 67-90 (2017)
- CardinaleJ., El Haddad J., Manouvrier M., Rukoz M. Fuzzy ACID properties for self-adaptive composite cloud services execution. Concurrency and Computation : Practice and Experience 31(2) (2019)