A Neural Networks Approach for Query Cost Evaluation

概要

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This paper presents a new approach for query cost evaluation that may help or replace the known analytical approach. Our proposed approach is based on neural networks and the connectionist concept. A neural network is trained to learn the execution cost of the implementation algorithm(s) for a logical algebra operation (or query) with some predicates; after that, this network is used to estimate this operation (query) cost with other entries. The approach is based on a curve fitting like since neural networks have been proven to be "universal approximators." An additional advantage of this approach is its applicability to user defined methods where the user does not need to estimate the cost of his method since the system may apply this method several times, collects measurements, and captures its behavior with its curve fitting capacity.
一般社団法人情報処理学会の論文
1997-12-15