Uppror Media Group
High energy physics scientists analyze large amounts of data looking for interesting events when particles collide. These analyses are easily expressed using complex queries that filter events. The authors developed a cost model for aggregation operators and other functions used in such queries and show that it substantially improves performance. However, the query optimizer still produces suboptimal plans because of estimate errors. Furthermore, the optimization is very slow because of the large query size. They improved the optimization by a profiled grouping strategy where the scientific query is first automatically fragmented into sub-queries based on application knowledge.