Advances in Data Management by Piotr Kołaczkowski, Henryk Rybiński (auth.), Zbigniew W.

By Piotr Kołaczkowski, Henryk Rybiński (auth.), Zbigniew W. Ras, Agnieszka Dardzinska (eds.)

Data administration is the method of making plans, coordinating and controlling
data assets. extra usually, purposes have to shop and seek a wide
amount of knowledge. handling info has been always challenged
by calls for from numerous parts and functions and has developed in parallel
with advances in and computing suggestions.

This quantity makes a speciality of its fresh advances and it really is composed of 5 parts
and a complete of eighteen chapters. the 1st a part of the e-book includes 5
contributions within the sector of data retrieval & internet intelligence: a unique
approach to fixing index choice challenge, built-in retrieval from net of
documents and information, bipolarity in database querying, deriving facts summarization
through ontologies, and granular computing for net intelligence.
The moment a part of the ebook comprises 4 contributions in wisdom discovery sector.
Its 3rd half includes 3 contributions in details integration & information
security region. the rest components of the booklet include six contributions
in the world of clever brokers and purposes of information administration
in clinical domain.

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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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