Improved Data Granularity Management Through a Generalized Model for Sensor Data and Data Mining Outputs in Telemonitoring Applications

Telemonitoring systems are expected to accomplish two basic tasks: continuously collect data from data sources wherever they may be; and allow remote communication between stakeholders to access data. The implementation and maintenance of these systems requires specific attention of software engineers for data management because of the complexity of the management of various data sources and because of privacy-related issues of personal data. In this paper we propose a data model that is generic enough to describe and to support many kinds of telemonitoring applications, especially those combining sensor data with data mining techniques and outputs. We show that our data model is useful for a smooth management of data mining outputs and that it avoids the integration effort for dealing with different heterogeneous storage mechanisms. We show also that our data model eases the management of the granularity of data and that it facilitates software designers' tasks for the implementation of privacy protection mechanisms.

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