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Archived Team - HADAS - Heterogeneous and Adaptive distributed DAta management Systems

Joint research team between CNRS, Grenoble INP, UGA
Area Intelligent Systems for Bridging Data, Knowledge and Humans

The HADAS research project addresses new challenges raised by continuous generation of huge, distributed, and heterogeneous data. These challenges concern collection/harvesting, integration, lookup and querying, filtering and indexing and many more. Our research focuses on new efficient and largely distributed, scalable, adaptive and intelligent data and knowledge management infrastructures. More specifically, our research goals concern:

Management of massive datasets

  • Adaptive and distributed storage and cache for storing large heterogeneous datasets.
  • Indexing data on the fly to facilitate efficient data manipulation.
  • Economy and energy oriented integration of big datasets management: economic cost model.
  • Quality-based continuous data/event stream processing and composition.

Adaptive querying systems

  • Declarative hybrid languages for expressing data (streams) processing.
  • Learning-based distributed query optimization for efficient (continuous) query evaluation with scarce metadata.
  • Query operators for on-the-fly data reorganization facilitating future data manipulations
  • Service Level Agreement guided optimization of continuous and mobile queries

We aim at deploying the data technologies in different types of architectures and environments: grids, peer-to-peer networks, sensor networks, cloud, HPC, GPU, ARM/Raspberry. Sustainable mobility and urban systems like smart cities, energy, clean, safe and efficient technologies like Smart Grids, smart energy, clean technologies and data markets for extracting business value from data, are examples of applications we explore.


Big Data management    Query optimization    Polyglot persistence systems    Event-based systems    Service-oriented data management systems

Submitted on March 5, 2024

Updated on March 5, 2024