Storing and Processing Massive Trajectory Data on SAP HANA

Haozhou Wang, Kai Zheng, Hoyoung Jeung, Shane Bracher, Asadul K. Islam, Wasim Sadiq, Shazia Wasim Sadiq, Xiaofang Zhou

Abstract: Owing to the development of cheap RAM-based storage technology, modern computing hardware can afford much larger main memory. Consequently, traditional database systems can be re-designed to store and manage all the data in main memory permanently. Such kind of in-memory database systems (IMDB) have attracted increasing attention from both academia and industry due to its outstanding performance in processing large amount of data. In this work, we will exploit the computational power of SAP HANA, the in-memory column-oriented data analytics platform designed by SAP, to support efficient query processing for moving object trajectories. We have tailored the frame-based data structure designed by our previous SharkDB project and made the trajectory data with variable lengths and sampling rates suitable for relational database model in SAP HANA. Extensive experiments based on large-scale real dataset have demonstrated superior performance of our frame-based design in processing a variant of queries.