GDC-a-CGI: efficient algorithms for dynamic graph data cleaning and indexing Online publication date: Tue, 04-Jan-2022
by D.K. Santhosh Kumar; Demian Antony D'Mello
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 6, 2021
Abstract: The era of big data has led the graph data collection and analytics to grow rapidly in numerous fields. Data quality and data access are the two decisive factors of performance (accuracy and efficiency) for graph data analytics model. The authors propose graph data cleaning (GDC) technique, which removes erroneous messy data, leading to a better data quality. The GDC is a dynamic cleaning technique that facilitates the user to update rules and expressions at runtime and support inheritance rules from inter-domains. In addition to cleaning, GDC verifies and validates the graph data. The authors present cache-based graph indexing (CGI) technique to address data access, which is built using the tree structure 'CSS-tree' on the Hadoop distributed framework. The CGI is a scalable index construction technique, which builds efficient indexing for an extensive graph dataset. We carried out experiments with different graph datasets and results reveal that, the proposed GDC and CGI techniques outperform the state-of-the-art.
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