1 - 6 of 6 Chapters
[The wide availability and use of location and mobile technologies (e.g. cheap GPS devices, ubiquitous cellular networks, RFID (Radio-Frequency Identification) tags), has enabled many applications that generate and maintain large amounts of data in the form of trajectories (see  for a survey...
[The wide adaptation of GPS and cellular technologies has created many applications (e.g., location-based, tracking, geo-fencing, and surveillance) that collect and maintain large repositories of data in the form of trajectories. Each trajectory contains the spatial locations collected for a...
[Call Detail Record (CDR) databases contain many millions of records with information about mobile phone calls, including the users’ location, when the call was made/received, and call duration, among other data. This huge amount of spatio-temporal data opens the door for the study of human...
[With the recent advancements and wide usage of location detection devices, large quantities of data are collected, for example, by GPS and cellular technologies in the form of trajectories. While most previous work on trajectory-based queries has concentrated on traditional range,...
[In this chapter we describe a general framework, called DivDB, for evaluation and optimization of methods for diversifying query results. In these methods, an initial ranking candidate set produced by a query is used to construct a result set. The elements in the result set are ranked with...
[This book presents several novel complex motion pattern queries for trajectory data. Previous works on querying trajectory data have mainly focused on traditional spatio-temporal queries, similarity/clustering based tasks, or spatio-temporal joins.]
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