QDaS: Quality driven data summarisation for effective storage managementin Internet of Things

Publication Year: 2019 Publication Type : JournalArticle


The proliferation of Internet of Things (IoT) has led to the emergence of enabling many interesting applications within the realm of several domains including smart cities. However, the accumulation of data from smart IoT devices poses significant challenges for data storage while there are needs to deliver relevant and high quality services to consumers. In this paper, we propose QDaS, a novel domain agnostic framework as a solution for effective data storage and management of IoT applications. The framework incorporates a novel data summarisation mechanism that uses an innovative data quality estimation technique. This proposed data quality estimation technique computes the quality of data (based on their utility) without requiring any feedback from users of this IoT data or domain awareness of the data. We evaluate the effectiveness of the proposed QDaS framework using real world datasets.


    author = {Jonathan Liono and Prem Prakash Jayaraman and A. Kai Qin and Thuong Nguyen and Flora D. Salim},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/journals/jpdc/LionoJQNS19.bib},
    doi = {10.1016/j.jpdc.2018.03.013},
    journal = {J. Parallel Distributed Comput.},
    pages = {196--208},
    timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
    title = {QDaS: Quality driven data summarisation for effective storage management in Internet of Things},
    url = {https://doi.org/10.1016/j.jpdc.2018.03.013},
    volume = {127},
    year = {2019}


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