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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.
