ABSTRACT As smartphones and GPS-enabled devices proliferate, location-based services become all the more important in social networking, mobile applications, advertising, trac monitoring, and many other domains. Managing the locations and trajectories of numerous people, vehicles, vessels, commodities, and so forth must be ecient and robust, since this information must be processed online and should provide answers to users’ requests in real time. In this geostreaming context, such long-running continuous queries must be repeatedly evaluated against the most recent positions relayed by moving objects, for instance, reporting which people are now moving in a specic area or nding friends closest to the current location of a mobile user. In essence, modern processing engines must cope with huge amounts of streaming, transient, uncertain, and heterogeneous spatiotemporal data, which can be characterized as big trajectory data. In this chapter, we examine Big Data processing techniques over frequently updated locations and trajectories of moving objects. Rapidly evolving trajectory data pose several research challenges with regard to their acquisition, storage, indexing, analysis, discovery, and interpretation in order to be really useful for intelligent, cost-eective decision making. Indeed, the Big Data issues regarding volume, velocity, variety, and veracity also arise in this case. us, we foster a close synergy between the established stream processing paradigm and spatiotemporal properties
CONTENTS 14.1 Introduction 258 14.2 Trajectory Representation and Management 261 14.3 Online Trajectory Compression with Spatiotemporal Criteria 264 14.4 Amnesic Multiresolution Trajectory Synopses 267 14.5 Continuous Range Search over Uncertain Locations 270 14.6 Multiplexing of Evolving Trajectories 272 14.7 Toward Next-Generation Management of Big Trajectory Data 275 References 277
inherent in motion features. Taking advantage of the spatial locality and temporal timeliness that characterize each trajectory, we present methods and heuristics from our recent research results that address such problems. We highlight certain aspects of big trajectory data management through several case studies. Regarding volume, we suggest single-pass algorithms that can summarize each object’s course into succinct, reliable representations. To cope with velocity, an amnesic trajectory approximation structure may oer fast, multiresolution synopses by dropping details from obsolete segments. Detection of objects that travel together can lead to trajectory multiplexing, hence reducing the variety inherent in raw positional data. As for veracity, we discuss a probabilistic method for continuous range monitoring against user locations with varying degrees of uncertainty, due to privacy concerns in geosocial networking. Last, but not least, as we are heading toward a next-generation framework in trajectory data management, we point out interesting open issues that may provide rich opportunities for innovative research and applications.