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A Survey on Big Data for Trajectory Analytics

Campina Grande, Department of Computer Science, Federal University of Campina Grande, Paraíba 58429-900, Brazil
Federal Institute of Paraíba, Cajazeiras, Paraíba 58900-000, Brazil
Institute for Big Data Analytics, Dalhousie University, Halifax, NS B3H 1W5, Canada
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 88;
Received: 13 January 2020 / Revised: 25 January 2020 / Accepted: 27 January 2020 / Published: 1 February 2020
Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data. View Full-Text
Keywords: data warehouse; mobility data; semantic trajectory; big data; analytics data warehouse; mobility data; semantic trajectory; big data; analytics
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MDPI and ACS Style

Ribeiro de Almeida, D.; de Souza Baptista, C.; Gomes de Andrade, F.; Soares, A. A Survey on Big Data for Trajectory Analytics. ISPRS Int. J. Geo-Inf. 2020, 9, 88.

AMA Style

Ribeiro de Almeida D, de Souza Baptista C, Gomes de Andrade F, Soares A. A Survey on Big Data for Trajectory Analytics. ISPRS International Journal of Geo-Information. 2020; 9(2):88.

Chicago/Turabian Style

Ribeiro de Almeida, Damião, Cláudio de Souza Baptista, Fabio Gomes de Andrade, and Amilcar Soares. 2020. "A Survey on Big Data for Trajectory Analytics" ISPRS International Journal of Geo-Information 9, no. 2: 88.

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