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Big trajectory data introduces severe challenges for data storage and communication. In this paper, we propose a novel compression framework called Clockwise Compression Framework (CCF) for big trajectory data compression under road network constraints. In CCF, we design several new methods: 1) a spatial compression algorithm called Enhanced Clockwise Encoding (ECE), 2) a temporal compression algorithm...
A trajectory is a polygonal line consisting of the positions that a moving object occupies as time passes, and as such, it can be derived by periodically sampling the positions of the object. In this manner, and due to the proliferation of location-sensing devices, it has been possible to create large datasets of trajectories. Using these datasets it is possible to derive much information about the...
The advances in location-acquisition technologies have generated massive spatio-temporal trajectory data, which represent the mobility of a diversity of moving objects over time, such as people, vehicles, and animals. Discovery of traveling companions on trajectory data has many real-world applications. Most of existing discovery approaches are limited to centralized computing, while these techniques...
Unprecedented volumes of location-based information have been produced as a result of the widespread adoption of social network applications and GPS-enabled devices and sensors. Publication of such location data can provide valuable resources for researchers and government agencies in applications ranging from near real-time population-wide health monitoring to planning for future cities. However,...
Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of...
Implementing trajectory data stream analysis in parallel has technical issues of data partition and improvements of the analysis operations. In this paper, we define the trajectory analysis problem as discovering trajectory companies of moving objects. We develop a discovery workflow in parallel batch processing. We solve technical issues of data partition and data locality in the steps of analysis...
We seek to extract and explore statistics that characterize New York City traffic flows based on 700 million taxi trips in the 2010–2013 New York City taxi data. This paper presents a two-part solution for intensive computation: space and time design considerations for estimating taxi trajectories with Dijkstra's algorithm, and job parallelization and scheduling with HTCondor. Our contribution is...
This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular...
With the advancements in spatiotemporal co-occurrence pattern and event sequence mining algorithms, spatiotemporal knowledge discovery from solar event datasets has been prominent in solar data mining. This work presents an efficient and extensible data access mechanism specifically designed for spatiotemporal relationships among the solar event instances. Previous indexing strategies primarily focus...
This paper introduces three interpolation methods that enrich complex evolving region trajectories that are captured every day from numerous ground-based and space-based solar observatories. The interpolation module takes a trajectory as its input and generates an enriched trajectory with interpolated time-geometry pairs. we created three different interpolation techniques that are: MBR-Interpolation...
Automatic identification system (AIS) has been widely equipped on vessels for maritime communication, positioning and traffic monitoring. The comprehensive data obtained by AIS provides spatio-temporal traces depicting the vessels' trajectories and can be used as a coherent source of information for vessels' behavior and the overall maritime traffic analysis, in supporting of the better traffic planning...
Big data processing has introduced new ideas in the applications of bacterial analysis in recent years. This paper aims to develop an effective framework to automatically extract quantitative knowledge relating to bacterial motility through processing a sequence of large-scale microscopic images of bacterial movements. It was hypothesized that motile bacteria move according to a conceptual model referred...
We present a scheme to extract large micro-scale severe weather region trajectories from meteorological radar data. Moving regions representing micro-scale severe weather are extracted from radar data and form into large trajectories automatically, which shows the moving and changing of severe weather, and can be a base for prediction of micro-scale severe weather.
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