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- W2769391036 abstract "Since many real applications such as web connectivity, social networks, and so on, are emerging now-a-days, thus graph databases have been widely used as important tools to model and query complex graph data wherein each vertex(node) in a graph usually contains information, which can be modelled by a set of tokens or elements. The method for subgraphs extraction by considering set similarity query over a large graph database has already been proposed, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic/fixed) weighted set similarity in a centralized system. This paper explains the efficient implementation of subgraphs extraction in a large graph database in a distributed environment by considering both vertex set similarity and graph topology which offers a better price/performance ratio and increases availability using redundancy when parts of a system fail than centralized systems in case of a large dataset (i.e., a graph with millions/billions of nodes wherein each node contains some information) by performing parallel processing." @default.
- W2769391036 created "2017-12-04" @default.
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- W2769391036 creator A5023314738 @default.
- W2769391036 date "2017-07-30" @default.
- W2769391036 modified "2023-09-27" @default.
- W2769391036 title "An Improved Method of Subgraphs Extraction in a Large Graph Database in a Distributed System by considering both Graph Topology and Set Similarity" @default.
- W2769391036 doi "https://doi.org/10.23956/ijarcsse/v7i6/0233" @default.
- W2769391036 hasPublicationYear "2017" @default.
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