Dynamic approach of Range Aggregate queries in Big Data environment

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 FastRAQ a fast approach to range-aggregate queries in big data environments. FastRAQ approach on the Linux platform, and evaluate its performance with about 10 billions data records. Experimental results demonstrate that FastRAQ can solve the 1: n format Range aggregate queries problem, i.e., there is one aggregation column and n index columns in a record. We plan to investigate how our solution can be extended to the case of m : n format problem, i.e., there are m aggregation columns and n index columns in a same record. In this range aggregation, insertions of dynamic environments. With the geometric aggregation queries expressions.