80 research outputs found

    An evaluation of PERF joins for a two-way semijoin based algorithm.

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    Distributed database system is becoming more widely used instead of centralized database systems in business world due to business expansion and network technology development. Query optimization provides a strategy for executing each query over the networks in the most cost-effective way, which aims to minimize the transmission cost over the networks. Many techniques and algorithms have been proposed to optimize queries, such as semijoin[BC81][BGW+81], 2-way semijoin[KR87], composite semijoin[PC90], hash semijoin[TC92], PERF join[LR95], etc. In distributed query processing, the semijoin has been used as an effective operator to reduce the total amount of data transmission. 2-way semijoin is an extended version of semijoin for more cost-effective distributed query processing. PERF joins are 2-way semijoins using a bit vector during the backward phase. PERF[LR95] is designed to minimize the cost of the backward reduction. It is based on the tuple scan order instead of hashing. Thus it does not suffer any loss of join information incurred by hash collisions. Algorithm UPSJ and Algorithm CPSJ are proposed based on a 2-way semijoin algorithm. Two variants of PERF joins are applied to the 2-way semijoin algorithm. In Algorithm UPSJ, uncompressed PERF joins and 2-way semijoin techniques are combined. In Algorithm CPSJ, compressed PERF joins are applied during the backward processing. Programs are designed to implement both original and the enhanced algorithms. Several experiments are conducted and the results showed a considerable enhancement obtained by applying the PERF join concept.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Y36. Source: Masters Abstracts International, Volume: 44-03, page: 1419. Thesis (M.Sc.)--University of Windsor (Canada), 2005

    Tractable Optimization Problems through Hypergraph-Based Structural Restrictions

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    Several variants of the Constraint Satisfaction Problem have been proposed and investigated in the literature for modelling those scenarios where solutions are associated with some given costs. Within these frameworks computing an optimal solution is an NP-hard problem in general; yet, when restricted over classes of instances whose constraint interactions can be modelled via (nearly-)acyclic graphs, this problem is known to be solvable in polynomial time. In this paper, larger classes of tractable instances are singled out, by discussing solution approaches based on exploiting hypergraph acyclicity and, more generally, structural decomposition methods, such as (hyper)tree decompositions

    Implementation of composite semijoins using a variation of Bloom filters.

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    Different from a centralized database system, distributed query processing involves data transmission among different sites and this communication cost is a dominant factor compared to local processing cost. So, the objective of distributed query optimization is to find strategies to minimize the amount of data transmitted over the network. Since optimal query processing in distributed database systems has been shown to be an NP-hard problem, heuristics are applied to find a near-optimal processing strategy. Previous research has mainly focused on the use of joins, semijoins, and hash semijoins (Bloom filters). The semijoin is a commonly recognized operator, which provides efficient query results. As a variation of semijoin, the composite semijoin is beneficial to do semijoins as one composite rather than as multiple single column semijoins. The Hash semijoin (which uses a Bloom filter) is used to minimize the cost of a semijoin operation. This thesis report provides a summary of each category of query processing techniques and optimization algorithms. Also in this thesis, we propose a new algorithm called Composite Semijoin Filter by combining the idea of composite semijoins, Bloom filters and PERF joins. One of the advantages of this algorithm is to avoid collisions. The algorithm is evaluated and compared with initial feasible solution (IFS) and another filter-based algorithm. It has been shown that the algorithm gives substantial reduction on relations and the total cost.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z58. Source: Masters Abstracts International, Volume: 43-01, page: 0249. Adviser: Joan Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 2004

    Compressed positionally encoded record filters in distributed query processing.

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    Different from a centralized database system, distributed query processing involves data transmission among distributed sites, which makes reducing transmission cost a major goal for distributed query optimization. A Positionally Encoded Record Filter (PERF) has attracted research attention as a cost-effective operator to reduce transmission cost. A PERF is a bit array generated by relation tuple scan order instead of hashing, so that it inherits the same compact size benefit as a Bloom filter while suffering no loss of join information caused by hash collisions. Our proposed algorithm PERF_C (Compressed PERF) further reduces the transmission cost in algorithm PERF by compressing both the join attributes and the corresponding PERF filters using arithmetic coding. We prove by time complexity analysis that compression is more efficient than sorting, which was proposed by earlier research to remove duplicates in algorithm PERF. Through the experiments on our synthetic testbed with 36 types of distributed queries, algorithm PERF_C effectively reduces the transmission cost with a cost reduction ratio of 62%--77% over IFS. And PERF_C outperforms PERF with a gain of 16%--36% in cost reduction ratio. A new metric to measure the compression speed in bits per second, compression bps , is defined as a guideline to decide when compression is beneficial. When compression overhead is considered, compression is beneficial only if compression bps is faster than data transfer speed. Tested on both randomly generated and specially designed distributed queries, number of join attributes, size of join attributes and relations, level of duplications are identified to be critical database factors affecting compression. Tested under three typical real computing platforms, compression bps is measured over a wide range of data size and falls in the range from 4M b/s to 9M b/s. Compared to the present relatively slow data transfer rate over Internet, compression is found to be an effective means of reducing transmission cost in distributed query processing. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z565. Source: Masters Abstracts International, Volume: 43-01, page: 0249. Adviser: J. Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 2004

    A model for equi-join query processing in distributed relational databases

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    "December 1981"Bibliography: leaf [1]"Contract ONR/N00014-77-C-0532"Kuan-Tsae Huang, Wilbur B. Davenport, Jr
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