In recent years, we have witnessed the proliferation of applications that generate thousands of terabytes of data per day, due to the explosive increase in storage capacity across various devices. As a consequence, a new concept called Data Deluge has emerged. Data deluge refers to the situation where the quantity of data generated exceeds the processing power available, and spatio-temporal data is no exception to this phenomenon. In this context, the efficient processing of spatio-temporal queries becomes crucial to address this challenge, as slow query processing can result in obsolete answers, which may lead to errors. Considering this dynamic context of storage and processing, we explore a new online workload algorithm in a distributed parallel environment using hybrid spatio-temporal indexes. This algorithm is able to update the indexes with the most appropriate data, aiming to achieve more efficient query processing. To measure the efficiency of this algorithm, we present its time complexity along with an empirical evaluation of its performance, considering processing time, number of accessed nodes, and communication costs. The empirical results show a significant reduction in processing time, communication costs, and number of accessed nodes.

Parallel Algorithms, Spatio-temporal Data, Hybrid Indexes, Query Efficiency