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Effective and efficient distributed management of big clinical data: a framework

Author

Listed:
  • Alfredo Cuzzocrea
  • Giorgio Mario Grasso
  • Massimiliano Nolich

Abstract

Managing big data in distributed environments is a critical research challenge that has driven the attention from the community. In this context, there are several issues to be faced-off, including: 1) dealing with massive and heterogeneous data; 2) inconsistency problems; 3) query optimisation bottlenecks, and so forth. Clinical data represent a vibrant case of big data, due to both practical as well as methodological challenges exposed by such data. Following these considerations, in this paper we present an architecture for the storage, exchange and use of health data for administrative and epidemiological purposes, which focuses on the patient, who in a safe and easy way can make use of their data for therapeutic and research purposes. The proposed architecture would bring benefits both to patients, giving them the desired centrality in the care process, and to health administration, which could exploit the same infrastructure for better addressing health policies.

Suggested Citation

  • Alfredo Cuzzocrea & Giorgio Mario Grasso & Massimiliano Nolich, 2019. "Effective and efficient distributed management of big clinical data: a framework," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 11(3), pages 284-313.
    • Handle: RePEc:ids:ijdmmm:v:11:y:2019:i:3:p:284-313
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