Parallel search processing of tree-structured data in a big data environment

Lingxiao Li, David Taniar, Maria Indrawan-Santiago

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

    5 Citations (Scopus)

    Abstract

    Every database systems needs to employ searching algorithms to locate and retrieve data. With the proliferation of NoSQL databases, there is a need to design search algorithms that are optimised for the non-relational files and record structures. We propose several search algorithms for document-based databases. The algorithms were designed with parallelism in mind, considering many of the NoSQL databases have very large volume of data. The algorithms were implemented and extensively tested on MongoDB and Apache Spark environment. The test results shows a promising performance of our proposed algorithms.

    Original languageEnglish
    Title of host publicationIEEE AINA 2017
    Subtitle of host publicationIEEE 31st International Conference on Advanced Information Networking and Applications, 27-29 March 2017, Taipei, Taiwan [Proceedings]
    EditorsLeonard Barolli, Makoto Takizawa, Tomoya Enokido, Hui-Huang Hsu, Chi-Yi Lin
    Place of PublicationPiscataway NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages379-386
    Number of pages8
    ISBN (Electronic)9781509060290
    ISBN (Print)9781509060306
    DOIs
    Publication statusPublished - 5 May 2017
    EventInternational Conference on Advanced Information Networking and Applications 2017 - Tamkang University, Taipei, Taiwan
    Duration: 27 Mar 201729 Mar 2017
    Conference number: 31st
    https://ieeexplore.ieee.org/xpl/conhome/7920264/proceeding (Proceedings)

    Conference

    ConferenceInternational Conference on Advanced Information Networking and Applications 2017
    Abbreviated titleAINA 2017
    CountryTaiwan
    CityTaipei
    Period27/03/1729/03/17
    Internet address

    Keywords

    • Big Data
    • Parallel Query Processing
    • Parallel Search
    • Tree-Structured Data

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