Efficient Compression and Preprocessing for Facilitating Large Scale Spatiotemporal Data Mining  A Case Study based on Automatic Identification System Data The large scale deployment of sensor, Global Positioning System (GPS) and other mobile devices generates large volumes of spatiotemporal data, which facilitates the understandings of objectsu0027 movement trajectories and activities. However, it is very challenging to store, transfer and load such a large volume of data into system memory for processing and analysis. In this study, we look into a study case that processes the large scale of Automatic Identification System (AIS) data in the maritime sector, and propose a computational framework to efficiently compress, transfer and acquire necessary information for further data analysis. The framework is composed of two parts: The first is a lossless compression algorithm that compresses the AIS data into binary form for efficient storage, speedy loading and easy transfer across networks and systems within the organization; the second is an aggregation algorithm which derives movement and activity information of vessels grouped by grid andor time window from the compressed binary files, therefore improves data accessibility and reduces storage demand. The proposed framework has been applied to extract vessel movement information within Singapore port with high compression rate and fast access speed, and it can be extensively applied for other data processing applications.

An Effective SpatioTemporal Query Framework for Massive Trajectory Data in Urban Computing With the development of IoT techniques, urban computing has become an emerging topic in academia and industry. The goal of urban computing is to address some issues of urban planning by using the big data generated in urban facilities. The massive trajectory data processing is viewed as an important issue in urban computing. To satisfy the storage and processing requirements of massive trajectory data, a distributed system is usually adopted. However, existing distributed systems face challenges of data locality aware partitioning and various trajectory queries. In this paper, we propose a distributed framework of massive trajectory data analysis based on HBase, to realize spatiotemporal query more effectively. We first design a temporalbased prepartitioning strategy to improve the performance of data written. Then we develop a MultiLevel Index to speed up the process of spatiotemporal query. Extensive experiments on real trajectory datasets demonstrate that the proposed framework significantly improves efficiency and usability.