Query optimisation in real-time spatial big data

Nowadays, real-time spatial applications have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of databases and data warehouses especially that users expect to receive the results of each query within a short time period without holding into account the load of the system. To solve this problem, several optimisation techniques are used. Thus, we propose, as a first contribution, a novel data partitioning approach for real-time spatial big data named vertical partitioning approach for real-time spatial big data (VPA-RTSBD). This contribution is an implementation of the matching algorithm for traditional vertical partitioning. Then, as a second contribution, we propose a new frequent itemset mining approach which relaxes the notion of window size and proposes a new algorithm named PrePost*-RTSBD. Thereafter, a simulation study is shown to prove that our contributions can achieve a significant performance improvement.