Adaptive-sampling algorithms for answering aggregation queries on Web sites

Many Web sites publish their data in a hierarchical structure. For instance, Amazon.com organizes its pages on books as a hierarchy, in which each leaf node corresponds to a collection of pages of books in the same class (e.g., books on Data Mining). Users can easily browse this class by following a path from the root to the corresponding leaf node, such as “Computers & Internet – Databases – Storage – Data Mining”. Business applications often require to submit aggregation queries on such data, such as “finding the average price of books on Data Mining”. On the other hand, it is computationally expensive to compute the exact answer to such a query due to the large amount of data, its dynamicity, and limited Web-access resources. In this paper, we study how to answer such aggregation queries approximately with quality guarantees using sampling. We study how to use adaptive-sampling techniques that allocate the resources adaptively based on partial samples retrieved from different nodes in the hierarchy. Based on statistical methods, we study how to estimate the quality of the answer using the sample. Our experimental study using real and synthetic data sets validates the proposed techniques.