Abstract
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.
Highlights
The quick development of wireless communication and data acquisition technologies, combined with the evolution of technologies that enable storing and processing large data volumes, have contributed to the significant growth of applications that deal with trajectory data
To the best of our knowledge, only one survey [14] was found that summarizes the research focused on the traditional architecture of Online Analytical Processing (OLAP) systems applied to trajectory analytics, but it still does not comment on various aspects related to Trajectory Data Warehouses (TDW) types, semantic trajectories, and Big Data
Large volumes of mobility data are being generated through devices with a Global Positioning System (GPS) and stored in data repositories
Summary
The quick development of wireless communication and data acquisition technologies, combined with the evolution of technologies that enable storing and processing large data volumes, have contributed to the significant growth of applications that deal with trajectory data. Dealing with large-scale spatial data is a research topic called Spatial Big Data [8] in which the issues related to Big Data applications are handled to enable the development of geographical information systems. Since the volume of trajectory data is usually very large, it is necessary to deploy an infrastructure that can analyze these massive data properly, solving complex queries, extracting relevant insights, and supporting the decision-making process This problem is solved using a Data Warehouse, which is an infrastructure that summarizes the data available in the operational level of the DBMS to generate analysis and reports that aid the decision support process making in organizations. To the best of our knowledge, only one survey [14] was found that summarizes the research focused on the traditional architecture of OLAP systems applied to trajectory analytics, but it still does not comment on various aspects related to TDW types, semantic trajectories, and Big Data.
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