Demo Abstract: Frequent Pattern-based Trajectory Completion

Zhiqiang Zhang, Weixiong Rao, Xiaolei Di, Peng Zhao, Xiaofeng Xu, Fehmi Ben Abdesslem

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

GPS sensors have been widely used to track people’s everyday life trajectories, generating massive trajectory datasets. The trajectory data typically contains sparse GPS points, and completing trajectories is often necessary. State-of-the-art methods [3, 4] essentially complete the entire route by using a single metric, e.g., either the shortest distance or the fastest driving/walking time. Unfortunately, using a single metric may not always work in real life due to the diversity of mobility patterns. In this demo abstract, we propose a frequent pattern (FP)-based trajectory completion approach, and demonstrate a system prototype to showcase the advantages of our approach over four previous works, in terms of accuracy and running time.

Original languageEnglish
Title of host publicationSenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages311-312
Number of pages2
ISBN (Electronic)9781450359528
DOIs
Publication statusPublished - 4 Nov 2018
Externally publishedYes
Event16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018 - Shenzhen, China
Duration: 4 Nov 20187 Nov 2018

Publication series

NameSenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems

Conference

Conference16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018
Country/TerritoryChina
CityShenzhen
Period4/11/187/11/18

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