کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10325321 | 670618 | 2005 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Iterative-improvement-based declustering heuristics for multi-disk databases
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Data declustering is an important issue for reducing query response times in multi-disk database systems. In this paper, we propose a declustering method that utilizes the available information on query distribution, data distribution, data-item sizes, and disk capacity constraints. The proposed method exploits the natural correspondence between a data set with a given query distribution and a hypergraph. We define an objective function that exactly represents the aggregate parallel query-response time for the declustering problem and adapt the iterative-improvement-based heuristics successfully used in hypergraph partitioning to this objective function. We propose a two-phase algorithm that first obtains an initial K-way declustering by recursively bipartitioning the data set, then applies multi-way refinement on this declustering. We provide effective gain models and efficient implementation schemes for both phases. The experimental results on a wide range of realistic data sets show that the proposed method provides a significant performance improvement compared with the state-of-the-art declustering strategy based on similarity-graph partitioning.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Systems - Volume 30, Issue 1, March 2005, Pages 47-70
Journal: Information Systems - Volume 30, Issue 1, March 2005, Pages 47-70
نویسندگان
Mehmet Koyutürk, Cevdet Aykanat,