Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube

Chao Liu; Ming Zhang; Minrui Zheng; Yixin Chen

doi:10.1007/3-540-36175-8_44

Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube

Chao Liu, Ming Zhang, Minrui Zheng, Yixin Chen

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

9 Scopus citations

Abstract

Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Editors	Kyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
Publisher	Springer Verlag
Pages	437-448
Number of pages	12
ISBN (Electronic)	3540047603, 9783540047605
DOIs	https://doi.org/10.1007/3-540-36175-8_44
State	Published - 2003
Event	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of Duration: Apr 30 2003 → May 2 2003

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2637
ISSN (Print)	0302-9743

Conference

Conference	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/Territory	Korea, Republic of
City	Seoul
Period	04/30/03 → 05/2/03

Access to Document

10.1007/3-540-36175-8_44

Cite this

Liu, C., Zhang, M., Zheng, M., & Chen, Y. (2003). Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube. In K.-Y. Wang, J. Jeon, K. Shim, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 437-448). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637). Springer Verlag. https://doi.org/10.1007/3-540-36175-8_44

Liu, Chao ; Zhang, Ming ; Zheng, Minrui et al. / Step-by-step regression : A more efficient alternative for polynomial multiple linear regression in stream cube. Advances in Knowledge Discovery and Data Mining. editor / Kyu-Young Wang ; Jongwoo Jeon ; Kyuseok Shim ; Jaideep Srivastava. Springer Verlag, 2003. pp. 437-448 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

@inproceedings{52fb2de7304a4564a6d692e76745eb36,

title = "Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube",

abstract = "Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.",

author = "Chao Liu and Ming Zhang and Minrui Zheng and Yixin Chen",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.; 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 ; Conference date: 30-04-2003 Through 02-05-2003",

year = "2003",

doi = "10.1007/3-540-36175-8_44",

language = "English",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

publisher = "Springer Verlag",

pages = "437--448",

editor = "Kyu-Young Wang and Jongwoo Jeon and Kyuseok Shim and Jaideep Srivastava",

booktitle = "Advances in Knowledge Discovery and Data Mining",

}

Liu, C, Zhang, M, Zheng, M & Chen, Y 2003, Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube. in K-Y Wang, J Jeon, K Shim & J Srivastava (eds), Advances in Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2637, Springer Verlag, pp. 437-448, 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003, Seoul, Korea, Republic of, 04/30/03. https://doi.org/10.1007/3-540-36175-8_44

Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube. / Liu, Chao; Zhang, Ming; Zheng, Minrui et al.
Advances in Knowledge Discovery and Data Mining. ed. / Kyu-Young Wang; Jongwoo Jeon; Kyuseok Shim; Jaideep Srivastava. Springer Verlag, 2003. p. 437-448 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637).

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

TY - GEN

T1 - Step-by-step regression

T2 - 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003

AU - Liu, Chao

AU - Zhang, Ming

AU - Zheng, Minrui

AU - Chen, Yixin

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2003.

PY - 2003

Y1 - 2003

N2 - Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.

AB - Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.

UR - http://www.scopus.com/inward/record.url?scp=7444226341&partnerID=8YFLogxK

U2 - 10.1007/3-540-36175-8_44

DO - 10.1007/3-540-36175-8_44

M3 - Conference contribution

AN - SCOPUS:7444226341

T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

SP - 437

EP - 448

BT - Advances in Knowledge Discovery and Data Mining

A2 - Wang, Kyu-Young

A2 - Jeon, Jongwoo

A2 - Shim, Kyuseok

A2 - Srivastava, Jaideep

PB - Springer Verlag

Y2 - 30 April 2003 through 2 May 2003

ER -

Liu C, Zhang M, Zheng M, Chen Y. Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube. In Wang KY, Jeon J, Shim K, Srivastava J, editors, Advances in Knowledge Discovery and Data Mining. Springer Verlag. 2003. p. 437-448. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/3-540-36175-8_44

Step-by-step regression: A more efficient alternative for polynomial multiple linear regression in stream cube

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this