  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 define every Hall of Fame
as one specific instance of an SQL query, such that a change in its result is
considered a noteworthy event. Identified changes (i.e., events) are ranked
using lexicographic tradeoffs over event and query properties and presented to
users or fed in higher-level applications. We have implemented a full-fledged
prototype system that uses either database triggers or a Java based middleware
for event identification. We report on an experimental evaluation using a
real-world dataset of basketball statistics.
