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
Volume	2006 II
DOIs	https://doi.org/10.1109/SUTC.2006.1636222
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
Country	Taiwan, Province of China
City	Taichung
Period	6/5/06 → 6/7/06

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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. vol. 2006 II, 1636222, 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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title = "PSRA: A data model for managing data in sensor networks",

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",

year = "2006",

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doi = "10.1109/SUTC.2006.1636222",

language = "English (US)",

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pages = "540--547",

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T2 - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

AU - Liu, Haiyang

AU - Hwang, San Yih

AU - Srivastava, Jaideep

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Y1 - 2006/12/15

N2 - 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.

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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M3 - Conference contribution

AN - SCOPUS:33845423029

SN - 0769525539

SN - 9780769525532

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SP - 540

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BT - Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006

Y2 - 5 June 2006 through 7 June 2006

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PSRA: A data model for managing data in sensor networks

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