Large-scale Data Integration Using Graph Probabilistic Dependencies (GPDs)

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Zada, Muhammad Sadiq Hassan, Yuan, Bo, Anjum, Ashiq, Azad, Muhammad Ajmal, Khan, Wajahat Ali and Reiff-Marganiec, Stephan 2020. Large-scale Data Integration Using Graph Probabilistic Dependencies (GPDs). IEEE. https://doi.org/10.1109/bdcat50828.2020.00028
AuthorsZada, Muhammad Sadiq Hassan, Yuan, Bo, Anjum, Ashiq, Azad, Muhammad Ajmal, Khan, Wajahat Ali and Reiff-Marganiec, Stephan
Abstract

The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.

Keywordsdata integration; information retrieval; graph probabilistic dependencies
Year2020
Journal2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/bdcat50828.2020.00028
Web address (URL)http://hdl.handle.net/10545/625607
http://creativecommons.org/licenses/by-nc-sa/4.0/
hdl:10545/625607
ISBN9780738123967
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Publication dates28 Dec 2020
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Deposited08 Feb 2021, 15:54
Accepted30 Oct 2020
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Attribution-NonCommercial-ShareAlike 4.0 International

ContributorsUniversity of Derby and University of Leicester
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Kiani, Saad Liaquat, Anjum, Ashiq, Bessis, Nik, Hill, Richard and Knappmeyer, Michael 2013. Energy conservation in mobile devices and applications: a case for context parsing, processing and distribution in clouds. Mobile Information Systems.
Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework
Ikram, Ahsan, Anjum, Ashiq, Hill, Richard, Antonopoulos, Nikolaos, Liu, Lu and Sotiriadis, Stelios 2013. Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework. Concurrency and Computation: Practice and Experience. https://doi.org/10.1002/cpe.3131
Performance simulation of a context provisioning middleware based on empirical measurements
Reetz, Eike Steffen, Knappmeyer, Michael, Kiani, Saad Liaquat, Anjum, Ashiq, Bessis, Nik and Tönjes, Ralf 2012. Performance simulation of a context provisioning middleware based on empirical measurements. https://doi.org/10.1016/j.simpat.2012.03.002
Dot-base62x: building a compact and user-friendly text representation scheme of ipv6 addresses for cloud computing
Liu, Zhenxing, Liu, Lu, Hardy, J., Anjum, Ashiq, Hill, Richard and Antonopoulos, Nikolaos 2012. Dot-base62x: building a compact and user-friendly text representation scheme of ipv6 addresses for cloud computing. Journal of Cloud Computing: Advances, Systems and Applications. https://doi.org/10.1186/2192-113X-1-3