Abstract
While sensor networks play a significant role in the modern information society, they output data in proprietary format and with little or no associated semantics. As a consequence, sensed data must be managed on a case by case basis, requiring significant human efforts. In this paper, we present an approach that: seamlessly supports any kind of network by exposing sensed data in a standard format; enables users to specify at a high level how to enrich sensed data with the semantics in which data is generate; facilitates end users in transforming data to meet their analytical requirements.
Jointly funded by Enterprise Ireland Grants TD-2007-201 and CFTD-2008-231.
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Cappellari, P., Shi, J., Roantree, M., Tobin, C., Moyna, N. (2011). Enabling Knowledge Extraction from Low Level Sensor Data. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23091-2_34
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DOI: https://doi.org/10.1007/978-3-642-23091-2_34
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