Data pre-processing: Case of sensor data consistency based on Bi-temporal concepts

Faiza Allah Bukhsh, Patricio De Alencar Silva, Hans Wienen

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

    Abstract

    The volume, velocity, variety, veracity and value of data currently produced and consumed by different types of information systems turned big Data into a phenomena of study. For data variety, temporal data commonly represents a source of potential inconsistency. This paper reports on a research endeavor for treating the problem of how to minimize inconsistencies in temporal databases due to unavailability of big data. This problem often occurs in situations where a same query is executed on the same data set at different points in time. To address this issue, we propose query optimization strategies based on query transformation and rewriting rules, to amend data consistency in temporal databases. We validate these strategies proposed via case scenario in sensor data analysis, and via manual data input, both for local and distributed query environments.

    Original languageEnglish
    Title of host publication2017 13th International Conference on Emerging Technologies (ICET)
    PublisherIEEE
    Pages1-6
    Number of pages6
    ISBN (Electronic)9781538622605
    DOIs
    Publication statusPublished - 5 Feb 2018
    Event13th International Conference on Emerging Technologies, ICET 2017 - Islamabad, Pakistan
    Duration: 27 Dec 201728 Dec 2017
    Conference number: 13

    Conference

    Conference13th International Conference on Emerging Technologies, ICET 2017
    Abbreviated titleICET
    CountryPakistan
    CityIslamabad
    Period27/12/1728/12/17

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  • Cite this

    Bukhsh, F. A., De Alencar Silva, P., & Wienen, H. (2018). Data pre-processing: Case of sensor data consistency based on Bi-temporal concepts. In 2017 13th International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICET.2017.8281746