Real-world datasets exhibit a complex dependency structure among the data attributes. Learning this structure is a key task in automatic statistics configuration for query optimizers, as well as in data mining, metadata discovery, and system management. In this paper, we provide a new method for discovering dependent attribute pairs based on query feedback. Our approach avoids the problem of searching through a combinatorially large space of candidate attribute pairs, automatically focusing system resources on those pairs of demonstrable interest to users. Unlike previous methods, our technique combines all of the pertinent feedback for a specified pair of attributes in a principled and robust manner, while being simple and fast enough to be incorporated into current commercial products. The method is similar in spirit to the CORDS algorithm, which proactively collects frequencies of data values and computes a chi-squared statistic from the resulting contingency table. In the reactive query-feedback setting, many entries of the contingency table are missing, and a key contribution of this paper is a variant of classical chi-squared theory that handles this situation. Because we typically discover a large number of dependent attribute pairs, we provide novel methods for ranking the pairs based on degree of dependency. Such ranking information, e.g., enables a database system to avoid exceeding the space budget for the system catalog by storing only the currently most important multivariate statistics. Experiments indicate that our dependency rankings are stable even in the presence of relatively few feedback records.
['Peter J. Haas', 'Fabian Hueske', 'Volker Markl']
Detecting attribute dependencies from query feedback