Handbook of Statistics 9: Computational Statistics by C. R. Rao

By C. R. Rao

Hardbound. The chapters during this quantity, written by means of experts in desktop technology and facts, illustrate the fad in sleek facts of uncomplicated method supported through the cutting-edge computational and graphical amenities, and their functions to different fields of human endeavour. particularly, the instruction manual is designed to function a realistic consultant to consulting statisticians; to supply study staff with an outline of present advancements in computing and point out their attainable use in statistical paintings; to carry the newest advancements in yes components of computing and calls for for the long run to the eye of desktop scientists; and to advertise an interface among statisticians and machine scientists for the advantage of both.

This paintings is a worthy advisor to laptop scientists, statistical specialists, computing device programmers and learn staff in all fields taken with information research.

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We proved theoretically that the method converges to the global optimum. We also presented examples where our algorithm actually gives better solutions than the solutions given by the state-of-the-art heuristics which explore the index configuration space. The method does not require any phase for preparatory candidate index selection. In addition it shows very low sensitivity to parameter settings, therefore it can be especially useful in the implementations of self-tuning, autonomic database systems.

7 Convergence curves for the TPC-H workload Fig. 8 Estimated costs of TPC-H workload as a function of the storage space limit We have also checked how the plan selection algorithm affects the performance of the method. As a reference, we have implemented the uniform plan selection algorithm. The proportional selection gave much better average results over the uniform selection (Fig. 5). When using the uniform selection, sometimes running even 100,000 iterations did not produce optimal results. The best recorded convergence curve for the uniform selection was also much worse than that for the proportional selection (Fig.

The recommended index sets and their estimated benefits were similar, but not exactly the same in both solutions. The observed differences were presumably caused by the query plan cost models that did not exactly match. Materializing the indexes recommended by our tool for the storage size limit of 40%, applied for the DB/2 database, improved the estimated workload cost by over 55%, but obviously not as much as the original DB/2 recommendation. The same situation was observed when applied the DB/2 recommendation to our cost model — the value of the objective function was better for our solution than for the DB/2 advisor’s one.

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