A moving object database is a database that tracks the movements of objects. As such, these databases have business intelligence applications in areas like trajectory-based advertising, disease control and prediction, hurricane path prediction, and drunk-driver detection. However, in order to extract knowledge from these objects, it is necessary to efficiently query these databases. To this end, databases incorporate special data structures called indexes. Multiple indexing techniques for moving object databases have been proposed. Nonetheless, indexing large sets of objects poses significant computational challenges. To cope with these challenges, some moving object indexes are designed to work with parallel architectures, such as multicore CPUs and GPUs (Graphics Processing Units), which can execute multiple instructions simultaneously. This chapter discusses business intelligence applications of parallel moving object indexes, identifies issues and features of these techniques, surveys existing parallel indexes, and concludes with possible future research directions.trajectory-based mobile advertising (Ammar, Elsayed, Sabri, & Terry, 2015), where shopping malls, by tracking the positions of shoppers using the mall's WiFi, can increase their revenue by sending online advertising that has been tailored to the shoppers based on their movement patterns around the mall (Ghose, 2017); for city planning in places like Shanghai, to help planners decide where to build new bike lanes, while taking into account Shanghai's budget limitations, and the way existing bike lanes are utilized (Bao, He, Ruan, Li, & Zheng, 2017); in online trajectory-sharing applications, to suggest attractive travel destinations based on the trajectories that others have enjoyed ; and in sports, to deduce the common plays of a given sports team (Buchin, Dodge, & Speckmann, 2014) from video footage, and then help coaches make decisions about their team's next play.However, in order to obtain these spatio-temporal patterns used for decision making, data must be retrieved from the datasets. The problem is that moving object data have a large volume, come at a high velocity from different sources, and are uncertain. Faced with these challenges, moving object databases use indexes that guide the execution of the query by reducing the number of data entries that need to be accessed, thus providing better query performance. Indexes are also in charge of accurately retrieving the moving objects that satisfy the query predicate. Nonetheless, in order to have scalable moving object index algorithms that are able to cope with Big Data, indexes should be designed to be parallel, thereby allowing simultaneous execution of multiple instructions. In this chapter, these indexes that are designed to exploit the characteristics of parallel computer architectures (multicore CPUs, GPUs and MapReduce) are called parallel moving object indexes. We now formalize the context in which these indexes work.The context of parallel moving object indexes is the following: the...
