FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments
Abstract— FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments. Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Existing approaches to range-aggregate queries are insufﬁcient to quickly provide accurate results in big data environments. In this paper, we propose Fast RAQ—a fast approach to range-aggregate queries in big data environments. Fast < Final Year Projects 2016 > ﬁrst divides big data into Independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, Fast RAQ obtains the result directly by summarizing local estimates from all partitions.