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An Adaptable Framework for Integrating and Querying Sensor Data

Abstract : Sensor data generated by pervasive applications are very diverse and are rarely described in standard or established formats. Consequently, one of the greatest challenges in pervasive systems is to integrate heterogeneous repositories of sensor data into a single view. The traditional approach to data integration, where a global schema is designed to incorporate the local schemas, may not be suitable to sensor data due to their highly transient schemas and formats.  Furthermore, researchers and professionals in healthcare need to combine relevant data from various data streams and other data sources, and to be able to perform searches over all of these collectively using a single interface or query.  Often, users express their search in terms of a small set of predefined fields from a single schema that is the most familiar to them, but they want their search results to include data from other compatible schemas as well. We have designed and implemented a framework for a sensor data repository that gives access to heterogeneous sensor metadata schemas in a uniform way. In our framework, the user specifies a query in an arbitrary schema and specifies the mappings from this schema to all the collections he wants to access. To ease the task of mapping specification, our system remembers metadata mappings previously used and uses them to propose other relevant mapping choices for the unmapped metadata elements. That way, users may build their own metadata mappings based on earlier mappings, each time specifying (or improving) only those components that are different. We have created a repository using data collected from various pervasive applications in a healthcare environment, such as activity monitoring, fall detection, sleep-pattern identification, and medication reminder systems, which are currently undergoing at the Heracleia Lab. We have also developed a flexible query interface to retrieve relevant records from the repository that allows users to specify their own choices of mappings and to express conditions to effectively access fine-grained data.
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Shahina Ferdous, Sarantos Kapidakis, Leonidas Fegaras, Fillia Makedon. An Adaptable Framework for Integrating and Querying Sensor Data. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.430-438, ⟨10.1007/978-3-642-23960-1_50⟩. ⟨hal-01571472⟩



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