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Abstract

Halls of Fame are fascinating constructs. They represent the elite of an often very large amount of entities|persons, companies, products, countries etc. Beyond their practical use as static rankings, changes to them are particularly interesting|for decision making processes, as input to common media or novel narrative science applications, or simply consumed by users. In this work, we aim at detecting events that can be characterized by changes to a Hall of Fame ranking in an automated way. We describe how the schema and data of a database can be used to generate Halls of Fame. In this database scenario, by Hall of Fame we refer to distinguished tuples; entities, whose characteristics set them apart from the majority. We dene every Hall of Fame as one specic instance of an SQL query, such that a change in its result is considered a noteworthy event. Identied changes (i.e., events) are ranked using lexicographic tradeos over event and query properties and presented to users or fed in higher-level applications. We have implemented a full-edged prototype system that uses either database triggers or a Java based middleware for event identication. We report on an experimental evaluation using a real-world dataset of basketball statistics.