PSRA: A data model for managing data in sensor networks

Haiyang Liu; San Yih Hwang; Jaideep Srivastava

doi:10.1109/SUTC.2006.1636222

PSRA: A data model for managing data in sensor networks

Haiyang Liu, San Yih Hwang, Jaideep Srivastava

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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)
Title of host publication	Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006
Pages	540-547
Number of pages	8
DOIs	https://doi.org/10.1109/SUTC.2006.1636222
State	Published - 2006
Event	IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Taichung, Taiwan, Province of China Duration: Jun 5 2006 → Jun 7 2006

Publication series

Name	Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
Volume	2006 II

Other

Other	IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
Country/Territory	Taiwan, Province of China
City	Taichung
Period	6/5/06 → 6/7/06

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10.1109/SUTC.2006.1636222

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Liu, H., Hwang, S. Y., & Srivastava, J. (2006). PSRA: A data model for managing data in sensor networks. In Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006 (pp. 540-547). Article 1636222 (Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing; Vol. 2006 II). https://doi.org/10.1109/SUTC.2006.1636222

PSRA: A data model for managing data in sensor networks. / Liu, Haiyang; Hwang, San Yih; Srivastava, Jaideep.
Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006. 2006. p. 540-547 1636222 (Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing; Vol. 2006 II).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, H, Hwang, SY & Srivastava, J 2006, PSRA: A data model for managing data in sensor networks. in Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006., 1636222, Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, vol. 2006 II, pp. 540-547, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Taichung, Taiwan, Province of China, 6/5/06. https://doi.org/10.1109/SUTC.2006.1636222

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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.",

author = "Haiyang Liu and Hwang, {San Yih} and Jaideep Srivastava",

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AB - 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.

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PSRA: A data model for managing data in sensor networks

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