Improving Accuracy and Robustness of Self-Tuning Histograms by Subspace Clustering
Abstract— Improving Accuracy and Robustness of Self-Tuning Histograms by Subspace Clustering. In large databases, the amount and the complexity of the data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a < Final Year Projects 2016 > class of model-free data summaries and are widely used in database systems. So-called self-tuning histograms look at query-execution results to reﬁne themselves. An assumption with such histograms, which has not been questioned so far, is that they can learn the dataset from scratch, that is – starting with an empty bucket conﬁguration. We show that this is not the case. Self-tuning methods are very sensitive to the initial conﬁguration. Three major problems stem from this. Traditional self-tuning is unable to learn projections of multi-dimensional data, is sensitive to the order of queries, and reaches only local optima with high estimation errors.
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