Answering Temporal Analytic Queries over Big Data Based on Precomputing Architecture

Abstract

Big data explosion brings revolutionary changes to many aspects of our lives. Huge volume of data, along with its complexity poses big challenges to data analytic applications. Techniques proposed in data warehousing and online analytical processing (OLAP), such as precomputed multidimensional cubes, dramatically improve the response time of analytic queries based on relational databases. There are some recent works extending similar concepts into NoSQL such as constructing cubes from NoSQL stores and converting existing cubes into NoSQL stores. However, only few works are studying the precomputing structure deliberately within NoSQL databases. In this paper, we present an architecture for answering temporal analytic queries over big data by precomputing the results of granulated chunks of collections which are decomposed from the original large collection. By using the precomputing structure, we are able to answer the drill-down and roll-up temporal queries over large amount of data within reasonable response time.