TRIPS: A System for Translating Raw Indoor Positioning Data into Visual Mobility Semantics

Huan Li, Hua Lu, Feichao Shi, Gang Chen, Ke Chen, Lidan Shou

Research output: Contribution to journalConference article in JournalResearchpeer-review

7 Citations (Scopus)
183 Downloads (Pure)

Abstract

The rapid accumulation of indoor positioning data is increasingly booming the interest in indoor mobility analyses. As a fundamental analysis, it is highly relevant to translate raw indoor positioning data into mobility semantics that describe what, where and when in a more concise and semantics-oriented way. Such a translation is challenging as multiple data sources are involved, raw indoor positioning data is of low quality, and translation results are hard to assess. We demonstrate a system TRIPS that streamlines the entire translation process by three functional components. The Configurator provides a standard but concise means to configure multiple input sources, including the indoor positioning data, indoor space information, and relevant contexts. The Translator cleans the indoor positioning data and exports reliable mobility semantics without manual interventions. The Viewer offers a suite of flexible operations to trace the input, output and intermediate data involved in the translation. Data analysts can interact with TRIPS to obtain the desired mobility semantics in a visual and convenient way.
Original languageEnglish
JournalProceedings of the VLDB Endowment
Volume11
Issue number12
Pages (from-to)1918-1921
ISSN2150-8097
DOIs
Publication statusPublished - 2018
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: 27 Aug 201731 Aug 2017

Conference

Conference44th International Conference on Very Large Data Bases, VLDB 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period27/08/201731/08/2017

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