A Solution for Problems in the Organization, Storage and Processing of Large Data Banks of Physiological Variables


  • Fredi Palominos Departaments: Mathematics, Biology, Industrial Technology and Industrial Engineering. Universidad de Santiago de Chile Avda. Bernardo O’Higgins 3363, Santiago, Chili.
  • Hernan Dí­az Departaments: Mathematics, Biology, Industrial Technology and Industrial Engineering. Universidad de Santiago de Chile Avda. Bernardo O’Higgins 3363, Santiago, Chili.
  • Felisa Córdova Director at Engineering School Finis Terrae University
  • Lucio Cañete Departaments: Mathematics, Biology, Industrial Technology and Industrial Engineering. Universidad de Santiago de Chile Avda. Bernardo O’Higgins 3363, Santiago, Chili.
  • Claudia Durí¡n Departaments: Mathematics, Biology, Industrial Technology and Industrial Engineering. Universidad de Santiago de Chile Avda. Bernardo O’Higgins 3363, Santiago, Chili.


data models, big data, EEG data organization, physiological information, metadata


The proliferation and popularization of new instruments for measuring different types of electrophysiological variables have generated the need to store huge volumes of information, corresponding to the records obtained by applying this instruments on experimental subjects. Together with this must be added the data derived from the analysis and purification processes. Moreover, several stages involved in the processing of data is associated with one or more specific methods related to the area of research and to the treatment at which the base information (RAW) is subjected. As a result of this and with the passage of time, various problems occur, which are the most obvious consequence of that data and metadata derived from the treatment processes and analysis and can end up accumulating and requiring more storage space than the base data. In addition, the enormous amount of information, as it increases over time, can lead to the loss of the link between the processed data, the methods of treatment used, and the analysis performed so that eventually all becomes simply a huge repository of biometric data, devoid of meaning and sense. This paper presents an approach founded on a data model that can adequately handle different types of chronologies of physiological and emotional information, ensuring confidentiality of information according to the experimental protocols and relevant ethical requirements, linking the information with the methods of treatment used and the technical and scientific documents derived from the analysis. Consequently, the need to generate specific data model is justified by the fact that the tools currently associated with the storage of large volumes of information are not able to take care of the semantic elements that make up the metadata and information relating to the analysis of base records of physiological information. This work is an extension of our paper [25].


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