Sensitivity of Self-Tuning Histograms: Query Order Affecting Accuracy and Robustness
- Quelle: Proceedings of the 24th International Conference on Scientific and Statistical Database Management (SSDBM 2012), Chania, Crete, Greece
In scientific databases, the amount and the complexity of data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a prominent 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 is that they can learn the dataset from scratch. We show that this is not the case and highlight a major challenge that stems from this. Traditional self-tuning is overly sensitive to the order of queries, and reaches only local optima with high estimation errors. We show that a self-tuning method can be improved significantly if it starts with a carefully chosen initial configuration. We propose initialization by subspace clusters in projections of the data. This improves both accuracy and robustness of self-tuning histograms.