Adaptive run-time overhead adjustments for optimizing multiple continuous query processing

Hyun Hon Lee, Hong Kyu Park, Jin Chul Park, Won Suk Lee, Kil Hong Joo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The time-varying characteristics of infinite data streams require continuous queries to be adaptively processed. The order in which multiple join operations are evaluated has serious consequences for the algorithm performance because the selectivity of each join operation can differ significantly from the selectivity of the other operations. The evaluation order may be effectively determined using the k-EGA and A-SEGO schemes, as proposed in previous studies. These methods optimize target continuous queries by monitoring a set of their promising subplans simultaneously. Each scheme also employs a user-defined cost-bound parameter for controlling the number of monitored subplans. A more optimized global plan may be generated by using a more highly configured cost-bound parameter. However, this approach can increase the overhead associated with monitoring the subplans. This paper proposes a new scheme, Adaptive Run-time Overhead Adjustment (AROA), which provides a novel method for adaptively determining the value of a cost-bound parameter based on the system environment. Unlike the previously described A-SEGO scheme, the scheme proposed here automatically selects the cost-bound parameter to reflect the system workloads (e.g., the input tuple rate, and other parameters). This method not only augments the probability of generating an optimized execution plan, it reduces the run-time delay caused by the optimization process. Experimental verification of the proposed scheme AROA demonstrated that AROA outperforms the previous schemes.

Original languageEnglish
Pages (from-to)183-196
Number of pages14
JournalInternational Journal of Software Engineering and its Applications
Volume8
Issue number11
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Query processing
Costs
Monitoring
Time delay

All Science Journal Classification (ASJC) codes

  • Software

Cite this

@article{80c99cbab3ca49d78bd1847866388298,
title = "Adaptive run-time overhead adjustments for optimizing multiple continuous query processing",
abstract = "The time-varying characteristics of infinite data streams require continuous queries to be adaptively processed. The order in which multiple join operations are evaluated has serious consequences for the algorithm performance because the selectivity of each join operation can differ significantly from the selectivity of the other operations. The evaluation order may be effectively determined using the k-EGA and A-SEGO schemes, as proposed in previous studies. These methods optimize target continuous queries by monitoring a set of their promising subplans simultaneously. Each scheme also employs a user-defined cost-bound parameter for controlling the number of monitored subplans. A more optimized global plan may be generated by using a more highly configured cost-bound parameter. However, this approach can increase the overhead associated with monitoring the subplans. This paper proposes a new scheme, Adaptive Run-time Overhead Adjustment (AROA), which provides a novel method for adaptively determining the value of a cost-bound parameter based on the system environment. Unlike the previously described A-SEGO scheme, the scheme proposed here automatically selects the cost-bound parameter to reflect the system workloads (e.g., the input tuple rate, and other parameters). This method not only augments the probability of generating an optimized execution plan, it reduces the run-time delay caused by the optimization process. Experimental verification of the proposed scheme AROA demonstrated that AROA outperforms the previous schemes.",
author = "Lee, {Hyun Hon} and Park, {Hong Kyu} and Park, {Jin Chul} and Lee, {Won Suk} and Joo, {Kil Hong}",
year = "2014",
month = "1",
day = "1",
doi = "10.14257/ijseia.2014.8.11.17",
language = "English",
volume = "8",
pages = "183--196",
journal = "International Journal of Software Engineering and its Applications",
issn = "1738-9984",
publisher = "Science and Engineering Research Support Society",
number = "11",

}

Adaptive run-time overhead adjustments for optimizing multiple continuous query processing. / Lee, Hyun Hon; Park, Hong Kyu; Park, Jin Chul; Lee, Won Suk; Joo, Kil Hong.

In: International Journal of Software Engineering and its Applications, Vol. 8, No. 11, 01.01.2014, p. 183-196.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive run-time overhead adjustments for optimizing multiple continuous query processing

AU - Lee, Hyun Hon

AU - Park, Hong Kyu

AU - Park, Jin Chul

AU - Lee, Won Suk

AU - Joo, Kil Hong

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The time-varying characteristics of infinite data streams require continuous queries to be adaptively processed. The order in which multiple join operations are evaluated has serious consequences for the algorithm performance because the selectivity of each join operation can differ significantly from the selectivity of the other operations. The evaluation order may be effectively determined using the k-EGA and A-SEGO schemes, as proposed in previous studies. These methods optimize target continuous queries by monitoring a set of their promising subplans simultaneously. Each scheme also employs a user-defined cost-bound parameter for controlling the number of monitored subplans. A more optimized global plan may be generated by using a more highly configured cost-bound parameter. However, this approach can increase the overhead associated with monitoring the subplans. This paper proposes a new scheme, Adaptive Run-time Overhead Adjustment (AROA), which provides a novel method for adaptively determining the value of a cost-bound parameter based on the system environment. Unlike the previously described A-SEGO scheme, the scheme proposed here automatically selects the cost-bound parameter to reflect the system workloads (e.g., the input tuple rate, and other parameters). This method not only augments the probability of generating an optimized execution plan, it reduces the run-time delay caused by the optimization process. Experimental verification of the proposed scheme AROA demonstrated that AROA outperforms the previous schemes.

AB - The time-varying characteristics of infinite data streams require continuous queries to be adaptively processed. The order in which multiple join operations are evaluated has serious consequences for the algorithm performance because the selectivity of each join operation can differ significantly from the selectivity of the other operations. The evaluation order may be effectively determined using the k-EGA and A-SEGO schemes, as proposed in previous studies. These methods optimize target continuous queries by monitoring a set of their promising subplans simultaneously. Each scheme also employs a user-defined cost-bound parameter for controlling the number of monitored subplans. A more optimized global plan may be generated by using a more highly configured cost-bound parameter. However, this approach can increase the overhead associated with monitoring the subplans. This paper proposes a new scheme, Adaptive Run-time Overhead Adjustment (AROA), which provides a novel method for adaptively determining the value of a cost-bound parameter based on the system environment. Unlike the previously described A-SEGO scheme, the scheme proposed here automatically selects the cost-bound parameter to reflect the system workloads (e.g., the input tuple rate, and other parameters). This method not only augments the probability of generating an optimized execution plan, it reduces the run-time delay caused by the optimization process. Experimental verification of the proposed scheme AROA demonstrated that AROA outperforms the previous schemes.

UR - http://www.scopus.com/inward/record.url?scp=84913604102&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84913604102&partnerID=8YFLogxK

U2 - 10.14257/ijseia.2014.8.11.17

DO - 10.14257/ijseia.2014.8.11.17

M3 - Article

AN - SCOPUS:84913604102

VL - 8

SP - 183

EP - 196

JO - International Journal of Software Engineering and its Applications

JF - International Journal of Software Engineering and its Applications

SN - 1738-9984

IS - 11

ER -