Spatio-Temporal Functional Dependencies for Sensor Data Streams

Manel Charfi 1 Yann Gripay 1 Jean-Marc Petit 1, 2
1 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Nowadays, sensors are cheap, easy to deploy and immediate to integrate into applications. Since huge amounts of sensor data can be generated , selecting only relevant data to be saved for further usage, e.g. long-term query facilities, is still an issue. In this paper, we adapt the declarative approach developed in the seventies for database design and we apply it to sensor data streams. Given sensor data streams, the key idea is to consider both spatio-temporal dimensions and Spatio-Temporal Functional Dependencies as first class-citizens for designing sensor databases on top of any relational database management system. We propose an axiomatisation of these dependencies and the associated attribute closure algorithm, leading to a new normalization algorithm.
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Communication dans un congrès
Springer. SSTD'17, Aug 2017, Arlington, Virginia, United States. Lecture Notes in Computer Science (10411), pp.182-199, 2017, Advances in Spatial and Temporal Databases. 〈10.1007/978-3-319-64367-0_10〉
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Dernière modification le : jeudi 19 avril 2018 - 14:38:05
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Manel Charfi, Yann Gripay, Jean-Marc Petit. Spatio-Temporal Functional Dependencies for Sensor Data Streams. Springer. SSTD'17, Aug 2017, Arlington, Virginia, United States. Lecture Notes in Computer Science (10411), pp.182-199, 2017, Advances in Spatial and Temporal Databases. 〈10.1007/978-3-319-64367-0_10〉. 〈hal-01527525〉

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