Precomputing architecture for flexible and efficient big data analytics

The rising of big data 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, 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 limited attention in literature have been devoted to precomputing structure within the 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. In extensive experimental evaluations on drill-down and roll-up temporal queries over large amount of data we demonstrated the effectiveness and efficiency under different settings.