Adaptive continuous query reoptimization over data streams

Hong Kyu Park, Won Suk Lee

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.

Original languageEnglish
Pages (from-to)1421-1428
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE92-D
Issue number7
DOIs
Publication statusPublished - 2009 Jan 1

Fingerprint

Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

@article{95faf4cc1fd444a58cd387fffa94b2cf,
title = "Adaptive continuous query reoptimization over data streams",
abstract = "A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.",
author = "Park, {Hong Kyu} and Lee, {Won Suk}",
year = "2009",
month = "1",
day = "1",
doi = "10.1587/transinf.E92.D.1421",
language = "English",
volume = "E92-D",
pages = "1421--1428",
journal = "IEICE Transactions on Information and Systems",
issn = "0916-8532",
publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
number = "7",

}

Adaptive continuous query reoptimization over data streams. / Park, Hong Kyu; Lee, Won Suk.

In: IEICE Transactions on Information and Systems, Vol. E92-D, No. 7, 01.01.2009, p. 1421-1428.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive continuous query reoptimization over data streams

AU - Park, Hong Kyu

AU - Lee, Won Suk

PY - 2009/1/1

Y1 - 2009/1/1

N2 - A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.

AB - A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.

UR - http://www.scopus.com/inward/record.url?scp=74549218250&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=74549218250&partnerID=8YFLogxK

U2 - 10.1587/transinf.E92.D.1421

DO - 10.1587/transinf.E92.D.1421

M3 - Article

AN - SCOPUS:74549218250

VL - E92-D

SP - 1421

EP - 1428

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

SN - 0916-8532

IS - 7

ER -