An automated framework for explaining facts extracted from mobility datasets

Anique Tahir, Yuhan Sun, Mohamed Elsayed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When a data scientist analyzes mobility data (e.g., using a data visualization tool), she may find out some interesting facts in the dataset. An example of a fact can be: 'The number of Taxi trips in NYC on January 23, 2016, dropped drastically as compared to other days of the same month'. However, the data scientist may be left clueless if they cannot find a crisp explanation to such a fact. Furthermore, the tedious task of finding an explanation by manually scraping the data becomes even impossible with big data. Existing techniques are designed for non-spatial data which cannot be applied to spatial data because it does not consider the spatial proximity. In this paper, we propose an automatic framework which guides the data scientist to explain the fact discovered from mobility data. Our approach expands on the aggravation and intervention techniques while using spatial partitioning/clustering to improve explanations for spatial data. Experiments show that the proposed approach outperforms the state-of-The-Art approaches in finding the explanation for facts extracted from NYC taxi real mobility dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages269-278
Number of pages10
ISBN (Electronic)9781728133638
DOIs
StatePublished - Jun 1 2019
Event20th International Conference on Mobile Data Management, MDM 2019 - Hong Kong, Hong Kong
Duration: Jun 10 2019Jun 13 2019

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2019-June
ISSN (Print)1551-6245

Conference

Conference20th International Conference on Mobile Data Management, MDM 2019
CountryHong Kong
CityHong Kong
Period6/10/196/13/19

Keywords

  • Database
  • Mobile spatial data
  • Spatial explanation

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Tahir, A., Sun, Y., & Elsayed, M. (2019). An automated framework for explaining facts extracted from mobility datasets. In Proceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019 (pp. 269-278). [8788788] (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MDM.2019.00-48