Efficient buffer management for piecewise linear representation of multiple data streams

Xie, Qing, Zhu, Jia, Sharaf, Mohamed A., Zhou, Xiaofang and Pang, Chaoyi (2012). Efficient buffer management for piecewise linear representation of multiple data streams. In: CIKM '12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 21st ACM International Conference on Information and Knowledge Management, Maui, HI, United States, (2114-2118). 29 October - 2 November 2012. doi:10.1145/2396761.2398584

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Author Xie, Qing
Zhu, Jia
Sharaf, Mohamed A.
Zhou, Xiaofang
Pang, Chaoyi
Title of paper Efficient buffer management for piecewise linear representation of multiple data streams
Conference name 21st ACM International Conference on Information and Knowledge Management
Conference location Maui, HI, United States
Conference dates 29 October - 2 November 2012
Proceedings title CIKM '12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Journal name ACM International Conference Proceeding Series
Place of Publication New York, NY, United States
Publisher ACM
Publication Year 2012
Sub-type Fully published paper
DOI 10.1145/2396761.2398584
ISBN 9781450311564
Start page 2114
End page 2118
Total pages 5
Collection year 2013
Language eng
Abstract/Summary Piecewise Linear Representation (PLR) has been a widely used method for approximating data streams in the form of compact line segments. The buffer-based approach to PLR enables a semi-global approximation which relies on the aggregated processing of batches of streamed data so that to adjust and improve the approximation results. However, one challenge towards applying the buffer-based approach is allocating the necessary memory resources for stream buffering. This challenge is further complicated in a multi-stream environment where multiple data streams are competing for the available memory resources, especially in resource-constrained systems such as sensors and mobile devices. In this paper, we address precisely those challenges mentioned above and propose efficient buffer management techniques for the PLR of multiple data streams. In particular, we propose a new dynamic approach called Dynamic Buffer Management with Error Monitoring (DBMEM), which leverages the relationship between the buffer demands of each data stream and its exhibited pattern of data values towards estimating its sufficient buffer size. This enables DBMEM to provide a global buffer allocation strategy that maximizes the overall PLR approximation quality for multiple data streams as shown by our experimental results.
Keyword Data streams
Dynamic buffer allocation
PLR
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 18 Apr 2013, 14:00:47 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering