Multi-scale dissemination of time series data

Qingsong Guo; Yongluan Zhou; Li Su

doi:10.1145/2484838.2484878

Multi-scale dissemination of time series data

Qingsong Guo, Yongluan Zhou, Li Su

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Abstract

In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time-series compression models. Due to the bandwidth limits regarding to potentially sheer speed of data, it is inevitable to compress and re-compress data along the dissemination paths according to the subscription level of each node. Compression would caused the accuracy loss of data, thus we devise several algorithms to optimize the average accuracies of the data received by all subscribers within the dissemination network. Finally, we have conducted extensive experiments to study the performance of the algorithms.

Original language	English
Title of host publication	Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Editors	Alex Szalay, Tamas Budavari, Magdalena Balazinska, Alexandra Meliou, Ahmet Sacan
Number of pages	12
Publisher	Association for Computing Machinery
Publication date	Jul 2013
Article number	14
ISBN (Print)	978-1-4503-1921-8
DOIs	https://doi.org/10.1145/2484838.2484878
Publication status	Published - Jul 2013
Externally published	Yes
Event	25th International Conference on Scientific and Statistical Database Management - Baltimore, United States Duration: 29 Jul 2013 → 31 Jul 2013 Conference number: 25

Conference

Conference	25th International Conference on Scientific and Statistical Database Management
Number	25
Country/Territory	United States
City	Baltimore
Period	29/07/2013 → 31/07/2013

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10.1145/2484838.2484878

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Multi-scale dissemination of time series data. / Guo, Qingsong; Zhou, Yongluan; Su, Li.

Proceedings of the 25th International Conference on Scientific and Statistical Database Management. ed. / Alex Szalay; Tamas Budavari; Magdalena Balazinska; Alexandra Meliou; Ahmet Sacan. Association for Computing Machinery, 2013. 14.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Guo, Q, Zhou, Y & Su, L 2013, Multi-scale dissemination of time series data. in A Szalay, T Budavari, M Balazinska, A Meliou & A Sacan (eds), Proceedings of the 25th International Conference on Scientific and Statistical Database Management., 14, Association for Computing Machinery, 25th International Conference on Scientific and Statistical Database Management, Baltimore, Maryland, United States, 29/07/2013. https://doi.org/10.1145/2484838.2484878

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AB - In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time-series compression models. Due to the bandwidth limits regarding to potentially sheer speed of data, it is inevitable to compress and re-compress data along the dissemination paths according to the subscription level of each node. Compression would caused the accuracy loss of data, thus we devise several algorithms to optimize the average accuracies of the data received by all subscribers within the dissemination network. Finally, we have conducted extensive experiments to study the performance of the algorithms.

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DO - 10.1145/2484838.2484878

M3 - Article in proceedings

SN - 978-1-4503-1921-8

BT - Proceedings of the 25th International Conference on Scientific and Statistical Database Management

A2 - Szalay, Alex

A2 - Budavari, Tamas

A2 - Balazinska, Magdalena

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PB - Association for Computing Machinery

T2 - 25th International Conference on Scientific and Statistical Database Management

Y2 - 29 July 2013 through 31 July 2013

ER -

Multi-scale dissemination of time series data

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