Cost Estimation for Large Queries via Fractional Analysis and Probabilistic Approach in Dynamic Multidatabase Environments

Zhu, Qiang; Motheramgari, S.; Sun, Yu

doi:10.1007/3-540-44469-6_48

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…On the other hand, if the environment changes very rapidly (e.g., within a few seconds, minutes, or hours), the evolutionary techniques are not applicable either since an evolutionary cost model may not be stable. Special techniques such as the qualitative approach [19], the fractional analysis approach [20], and the probabilistic approach [20] are needed to solve query cost estimation issues in such an environment. Besides, the conventional dynamic query optimization techniques could also be adopted in such a case.…”

Section: Implementation Considerationsmentioning

confidence: 99%

“…To take the frequently changing factors (Type I) into consideration for estimating query costs, Zhu et al suggested three techniques in [19,20], i.e., the qualitative approach, the fractional analysis approach, and the probabilistic approach. The qualitative approach is suitable for estimating the cost of a small query using a cost model with a qualitative variable indicating system contention states.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary techniques for updating query cost models in a dynamic multidatabase environment

Rahal

Zhu

Larson

2004

The VLDB Journal The International Journal on Very Large Data B

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Deriving local cost models for query optimization in a dynamic multidatabase system (MDBS) is a challenging issue. In this paper, we study how to evolve a query cost model to capture a slowly-changing dynamic MDBS environment so that the cost model is kept up-to-date all the time. Two novel evolutionary techniques, i.e., the shifting method and the block-moving method, are proposed. The former updates a cost model by taking up-to-date information from a new sample query into consideration at each step, while the latter considers a block (batch) of new sample queries at each step. The relevant issues, including derivation of recurrence updating formulas, development of efficient algorithms, analysis and comparison of complexities, and design of an integrated scheme to apply the two methods adaptively, are studied. Our theoretical and experimental results demonstrate that the proposed techniques are quite promising in maintaining accurate cost models efficiently for a slowly changing dynamic MDBS environment. Besides the application to MDBSs, the proposed techniques can also be applied to the automatic maintenance of cost models in self-managing database systems.

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Section: Implementation Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary techniques for updating query cost models in a dynamic multidatabase environment

Rahal

Zhu

Larson

2004

The VLDB Journal The International Journal on Very Large Data B

Self Cite

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“…The key idea is to use sampling query technique to collect the cost model parameters for each local database and to keep the collected information in the MDBS catalog and to use these parameters during query optimization. The effects of the workload of a server on the cost of a query were examined from a different perspective in [14]. If a server was very busy, the server was likely to take longer time to answer the query, and the cost of the query was high.…”

Section: Introductionmentioning

confidence: 99%

Determining remote system contention states in query processing over the Internet

Liu

Liao

Hong

2003

Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)

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In the environment of data integration over the Internet, three major factors affect the cost of a query: network congestion situation, server contention states (workload), and data/query complexity. In this paper, we concentrate on system contention states. For a remote data source, we first determine the total number of contention states of the system through applying clustering techniques to the costs of sample queries. We then develop a set of cost formulae for each of the contention states using a multiple regression process. Finally, we estimate the system's current contention state when a query is issued and using either a time slides method or a statistical method depending on the information we have about the system. Our method can accurately predict the system contention state so that the effect of the contention states on the cost of queries can be estimated precisely.

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“…Therefore, some methods of deriving cost models for autonomous data sources at a global level are significantly important in order to accurately process queries. Several methods discussed in [1,2,[8][9][10] assume that the system environment does not change significantly over time. Therefore, the impact of the two factors is not explicitly involved in query cost estimation.…”

Section: Introductionmentioning

confidence: 99%

“…The significance of recognizing the impact of the overall system contention states has been studied in two separate research projects recently. In [10], the effects of the workload of a server on the cost of a query are investigated and a method to decide the contention states of a server is developed. Cost models derived through sampling queries for each contention state are also constructed for estimating the costs of further queries.…”

Section: Introductionmentioning

confidence: 99%

Analysing the effect of network states in query cost estimation over the internet

Liao¹,

Liu²,

Jiang³

Proceedings. International Database Engineering and Applications Symposium, 2004. IDEAS '04.

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Query processing over the Internet has been proven to be one of the most difficult and important problems in modern e-data sharing society. In this new data processing environment, three major factors affect the cost of a query: network congestion situation, server states (server workload), and data/query complexity. In this paper, we split the cost of queries into two parts: the cost of the network and the cost of servers and estimate each cost separately. We will study the behaviour of the wide area network to derive the server states from the system contention states and the network congestion situation, and analyze two cases of network changing behaviour and propose different methods to deal with them respectively. So we can be more accurate to choose the best query plan according to the network situation, the system contention states and the server states at the query optimization stage.

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Cost Estimation for Large Queries via Fractional Analysis and Probabilistic Approach in Dynamic Multidatabase Environments

Cited by 6 publications

References 10 publications

Evolutionary techniques for updating query cost models in a dynamic multidatabase environment

Evolutionary techniques for updating query cost models in a dynamic multidatabase environment

Determining remote system contention states in query processing over the Internet

Analysing the effect of network states in query cost estimation over the internet

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