Abstract
Sensor data streams exhibit unique characteristics such as inherent information uncertainty, intrinsic data sample correlations (both within and across streams) and resource constraints. In this paper, we introduce a new data model, called Probabilistic Stream Relational Algebra (PSRA), that extends conventional relational model to capture these new characteristics faced in managing data in sensor networks. New data types, new operations and essential strategies are incorporated into PSRA to facilitate flexible data modeling and resource-efficient operations. We show that operators in PSRA are non-blocking and more expressive than conventional relational model and existing deterministic data stream processing models. A demonstrating application implementing key operations in PSRA is provided to show the advantages of utilizing PSRA in managing data in sensor networks.
Original language | English (US) |
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Title of host publication | Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006 |
Pages | 540-547 |
Number of pages | 8 |
Volume | 2006 II |
DOIs | |
State | Published - Dec 15 2006 |
Event | IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Taichung, Taiwan, Province of China Duration: Jun 5 2006 → Jun 7 2006 |
Other
Other | IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing |
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Country | Taiwan, Province of China |
City | Taichung |
Period | 6/5/06 → 6/7/06 |