Computing with Spatial Trajectories by Wang-Chien Lee, John Krumm (auth.), Yu Zheng, Xiaofang Zhou

By Wang-Chien Lee, John Krumm (auth.), Yu Zheng, Xiaofang Zhou (eds.)

Spatial trajectories were bringing the exceptional wealth to quite a few learn groups. A spatial trajectory documents the trails of quite a few relocating items, comparable to those who log their trip routes with GPS trajectories. the sector of relocating items similar learn has turn into tremendous energetic in the previous couple of years, specially with all significant database and information mining meetings and journals.

Computing with Spatial Trajectories introduces the algorithms, applied sciences, and platforms used to approach, deal with and comprehend present spatial trajectories for various functions. This ebook additionally provides an outline on either basics and the cutting-edge study encouraged via spatial trajectory information, in addition to a unique specialise in trajectory development mining, spatio-temporal facts mining and location-based social networks. each one bankruptcy offers readers with a tutorial-style advent to 1 very important element of situation trajectory computing, case reports and plenty of beneficial references to different proper study paintings.

Computing with Spatial Trajectories is designed as a reference or secondary textual content e-book for advanced-level scholars and researchers mostly eager about machine technology and geography. pros engaged on spatial trajectory computing also will locate this publication very useful.

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Computing with Spatial Trajectories

Spatial trajectories were bringing the remarkable wealth to quite a few learn groups. A spatial trajectory documents the trails of quite a few relocating gadgets, akin to those that log their shuttle routes with GPS trajectories. the sector of relocating gadgets similar examine has turn into super energetic in the previous couple of years, specifically with all significant database and knowledge mining meetings and journals.

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3 4 Wang-Chien Lee and John Krumm ranging from fleet management, traffic information services, transportation logistics, location-based games, to location-based social networks. 2 To support LBS applications, the database community has made a tremendous research effort on the development of mobile object databases (MODs) in the past decade [33, 14, 13, 28]. In addition to the conventional search functions of moving objects, trajectory management are essential operations of MODs since many LBS applications require analyzing and mining moving phenomenons/patterns of the monitored objects.

The subsections below develop the model for the Kalman filter for the example trajectory from above. We use notation from the book by Gelb, which is one of the standard references for Kalman filtering [1]. 1 Measurement Model While the mean and median filters can only estimate what is directly measured, the Kalman filter can estimate other variables like speed and acceleration. In order to do this, the Kalman formulation makes a distinction between what is measured and what is estimated, as well as formulating a linear relationship between the two.

The methods include mean and median filtering, the Kalman filter, and the particle filter. 1 Introduction Owing to the rapid advent of wireless communication and mobile computing technologies, the vision of pervasive computing is becoming a reality. Mobile devices, including smart phones, PDAs, navigational systems on vehicles, and RFIDs on cargos, have played a growing important role in various applications in our daily life. 1 Indeed, there is a tremendous demand for location tracking of moving objects from various location-based services (LBS), Wang-Chien Lee Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA.

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