A Query Framework for Massive RDF Graph Data in Pay-As-You-Go Fashion
Xiaolong Liu, Ying Pan · 2022
In the context of big data, faster and more accurate methods are required for RDF data retrieval. The current research on querying RDF graph data has made some progress, but it has a certain delay and high up-front cost. Given the above shortcomings, we propose a more efficient framework for querying RDF graph data based on the pay-as-you-go (PAYG) approach. Firstly, we annotate the evolution process of data content and association and then construct the evolution update operation set and dynamic incremental graph to describe the dynamic data. Secondly, we design a query algorithm supporting the best-effort query, which returns the data information with the highest similarity to the user, thus improving the search efficiency. Finally, we apply the investment income theory and information retrieval evaluation methods to construct an evaluation mechanism for PAYG RDF data management.