The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Strim data ialah satu siri tupel besar tanpa sempadan yang dijana secara berterusan pada kadar yang pantas. Pertanyaan berterusan untuk aliran data harus diproses secara berterusan, supaya kekangan masa yang ketat diperlukan. Dalam kebanyakan kajian penyelidikan terdahulu, untuk menjamin kekangan ini, susunan penilaian gabungan predikat dalam pertanyaan berterusan dioptimumkan menggunakan strategi tamak. Walau bagaimanapun, kerana strategi tamak hanya mengesan pelan yang menjanjikan pertama, ia sering menemui rancangan yang tidak optimum. Untuk mengurangkan kemungkinan menghasilkan pelan suboptimum, dalam kertas ini, kami mencadangkan skim yang lebih baik, k-Extended Greedy Algorithm (k-EGA), yang secara serentak memeriksa satu set rancangan yang menjanjikan dan mengoptimumkan semula pelan pelaksanaan secara adaptif. Bilangan pelan yang menjanjikan dikawal secara fleksibel oleh pembolehubah julat yang ditentukan pengguna. Skim ini mengesahkan prestasi pelan semasa secara berkala. Jika pelan itu tidak lagi cekap, pelan yang baru dioptimumkan akan dihasilkan. Prestasi skim yang dicadangkan disahkan melalui pelbagai eksperimen untuk mengenal pasti pelbagai cirinya.
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Salinan
Hong Kyu PARK, Won Suk LEE, "Adaptive Continuous Query Reoptimization over Data Streams" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 7, pp. 1421-1428, July 2009, doi: 10.1587/transinf.E92.D.1421.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1421/_p
Salinan
@ARTICLE{e92-d_7_1421,
author={Hong Kyu PARK, Won Suk LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Continuous Query Reoptimization over Data Streams},
year={2009},
volume={E92-D},
number={7},
pages={1421-1428},
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.},
keywords={},
doi={10.1587/transinf.E92.D.1421},
ISSN={1745-1361},
month={July},}
Salinan
TY - JOUR
TI - Adaptive Continuous Query Reoptimization over Data Streams
T2 - IEICE TRANSACTIONS on Information
SP - 1421
EP - 1428
AU - Hong Kyu PARK
AU - Won Suk LEE
PY - 2009
DO - 10.1587/transinf.E92.D.1421
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2009
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.
ER -