BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs

Abstract

Over the past era, subgraph mining from a large collection of a graph database is a crucial problem. our proposed work introduces a Blockchain-based Triune Layered Architecture for authenticated subgraph query search in large-scale dynamic graphs. The two-fold process is handled in BTLA-LSDG: graph indexing and authenticated query search (query processing), which are implemented in triune layers (Data Generation Layer, Data Storage Layer, and Service Layer). Initially, data owners are authenticated to the blockchain using the Four-Q-Curve algorithm. The graph index is constructed by data owners and the merged graph index is constructed by service providers. Based on the uploaded graph index, the hash index is constructed using SHA-3. On the other hand, the data user submits a query with authentication. For every authenticated query, the four-fold process is handled. Firstly, Multi-Constraint-based Belief Entropy function is used for feature set computations for a given query. Then dual similarity-based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Thirdly, a Recurrent Neural Network (RNN) is used for subgraph isomorphic testing. Finally, the graph index refinement process is undertaken to improve the query results. This experiment is implemented using a Hadoop environment and the results show better efficiency in terms of Scalability, Security and Storage. Furthermore, it is tested with Precision, Recall, F-measure, Accuracy, Error Rate, Query Response Time and Positive Results.