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From Data to Knowledge, from Data to Decision

From Data to Knowledge, from Data to Decision

Chapo
The advent of massive data is pushing technical limits in many areas, specifically in decision-making and optimization. On the one hand, mainstream statistical models and decision-making algorithms are challenged by heterogeneous, semi-structured, complex, incomplete and/or uncertain data. On the other hand, data management raises new operability constraints in terms of security integrity and traceability.
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In addition, producing knowledge requires the construction of models that provide explainable, statistically valid and calculable decisions. Acceptance of results also requires that confidentiality and loyalty be strengthened.

At the same time, new developments in optimization should make it possible to improve estimation procedures.

Challenges:

  • Heterogeneous, semi-structured, complex, incomplete and/or uncertain data
  • Fast big data: new methodologies to use data
  • Online learning, methodology for massive data, efficient methods
  • Improved storage, calculation and estimation for data science
  • Modeling of interactions between agents (human or artificial) by game theory
  • Multiscale and multimodal representation and algorithms
  • Theoretical analysis of heuristic methods (complexity theory, information geometry, Markov chain theory)
  • Human-machine co-evolution in autonomous systems: conversational agents, cars, social robots