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  1. Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '16)
  2. On computing temporal functions for time-dependent networks using trajectory data streams
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Formalization of network-constrained moving object queries with application to benchmarking
Continuous detection of black holes for moving objects at sea
Categorizing spatiotemporal aggregates for moving regions
On computing temporal functions for time-dependent networks using trajectory data streams
ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams
A survey of techniques and open-source tools for processing streams of spatio-temporal events
A general feature-based map matching framework with trajectory simplification
MobiDict: a mobility prediction system leveraging realtime location data streams
Mining frequent episodes from multivariate spatiotemporal event sequences
Mining frequent trajectory patterns from online footprints

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On computing temporal functions for time-dependent networks using trajectory data streams

Content Provider ACM Digital Library
Author Machado, Javam Nascimento, Samara M. Chucre, Mirla R. R. Braga Casanova, Marco Antonio de, José Antonio F.
Abstract Time dependent networks in mobility scenario are key for many applications that need to cope with real world dynamics. However, the quality of a time dependent network relies on the accuracy of its temporal functions. To this aim, we propose a new method for computing temporal functions for a time dependent network using Trajectory Data Streams. This proposal extends the previous Piecewise linear model, which uses a smooth curve approach, called LOESS, that can estimate where the breakpoints values occurs in a Piecewise linear function. A challenge faced by the use of trajectory data streams is related with the time constraint to update time dependent network time functions. Our model computes the time dependent network and update the temporal function that needs to reflect recent data and discard old data. We described our solution and present experimental results, which show that our approach is efficient and effective comparing to their competitors.
Starting Page 1
Ending Page 10
Page Count 10
File Format PDF
ISBN 9781450345798
DOI 10.1145/3003421.3003425
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-31
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Data stream Piecewise Temporal function Time dependent network
Content Type Text
Resource Type Article
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