Semi-Automated Exploration of Data Warehouses

Exploratory data analysis tries to discover novel dependencies and unexpected patterns in large databases. Traditionally, this process is manual and hypothesis-driven. However, analysts can come short of patience and imagination. In this paper, we introduce Claude, a hypothesis generator for data warehouses. Claude follows a 2-step approach: (1) It detects interesting views, by exploiting non-linear statistical dependencies between the dimensions and the measure. (2) To explain its findings, it detects local patterns in these views and describes them with SQL queries. Technically, we derive a model of interestingness from fundamental information theory. To exploit this model, we present aggressive approximations and heuristics, allowing Claude to be fast and more accurate than state-of-art view selection algorithms.