CEDAR Research team
Rich Data Exploration at Cloud Scale
In today's data-intensive application, variety is the norm, and is likely to remain so for a while. This is because different applications are best served by different kinds of data: traditional commerce-oriented applications use relational databases, Web content management systems handle semistructured documents, sensors provide numerical streams, science applications manipulate arrays, highly heterogeneous data sets is often exported in RDF graphs, software system logs consist of structured text etc.
At the scale and speed of consumption of today's Big Data, unifying data across such formats into a single architecture (approach formerly known as extract-transform-load in a data warehouse context) is no longer feasible. Instead, Cedar aims at inventing expressive models and highly efficient data management tools, focused from the start on Big Data variety. Our tools are designed for deployment in the cloud, and validated at large scale.
Our research can be viewed as pertaining to two broad areas.
Within the cloud (under the hood of the data processing system), our research aims at building efficient platforms for highly scalable data analytics at very large scale. Particular interest in this area will be devoted to:
1. Scalable heterogeneous stores
2. Semantic query answering
Outside the cloud, at the interface between the data management system and its users, we seek to revisit the paradigms of interaction between users or application and the system, by endowing the former with novel data access primitives to facilitate and enrich the user interaction. We consider in particular the following axes:
3. Exploratory querying of semantic graphs
4. Representative semantic query answering
International and industrial relations
Outside France, we collaborate with: UCSD (Alin Deutsch), AT&T (D. Srivastava), U. Madison Wisconsin (D. DeWitt) and U. Berkeley (M. Franklin), TU Dresden (S. Rudolph), U. Bolzano (D. Calvanese).
Industrial partners include Business & Décision (EOLAS), SemSoft and Le Monde.
Research teams of the same theme :
- GRAPHIK - GRAPHs for Inferences and Knowledge representation
- LACODAM - Large scale Collaborative Data Mining
- LINKS - Linking Dynamic Data
- MAGNET - Machine Learning in Information Networks
- MOEX - Evolving Knowledge
- ORPAILLEUR - Knowledge representation, reasonning
- PETRUS - PErsonal & TRUSted cloud
- TYREX - Types and Reasoning for the Web
- VALDA - Value from Data
- WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
- ZENITH - Scientific Data Management
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