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

References

• S. Velampalli, and V. R. M. Jonnalagedda, “Frequent subgraph mining algorithms: Framework, classification, analysis, comparisons,” in Data Engineering and Intelligent Computing, Singapore, 2017, pp. 327–36.
   Google Scholar
• Z. Peng, T. Wang, W. Lu, H. Huang, X. Du, F. Zhao, and A. K. H. Tung, “Mining frequent subgraphs from tremendous amount of small graphs using MapReduce,” Knowl. Inf. Syst., Vol. 56, no. 3, pp. 663–90, 2017.
   Web of Science ®Google Scholar
• W. Wang, Y. Yao, L. Zhu, X. Hei, and Y. Wang, “A novel subgraph querying method on directed weighted graphs,” in 2018 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, China, 2018.
   Google Scholar
• S. U. Rehman, S. Asghar, and S. J. Fong, “Optimized and frequent subgraphs: How are they related?,” IEEE Access., Vol. 6, pp. 37237–49, 2018.
   Web of Science ®Google Scholar
• N.-T. Le, B. Vo, L. Nguyen, H. Fujita, and B. Le, “Mining weighted subgraphs in a single large graph,” Inf. Sci., Vol. 514, pp. 149–65, 2019.
   Web of Science ®Google Scholar
• L. B. Q. Nguyen, B. Vo, N.-T. Le, V. Snasel, and I. Zelinka, “Fast and scalable algorithms for mining subgraphs in a single large graph,” Eng. Appl. Artif. Intell., Vol. 90, pp. 103539, 2020.
   Web of Science ®Google Scholar
• A. Zhou, L. Zhu, X. Wu, and H. Qiu, “Accurate querying of frequent subgraphs in power grid graph data,” Glob. Energy Interconnect., Vol. 2, no. 1, pp. 78–84, 2019.
   Google Scholar
• Z. Zhang, D. Chen, J. Wang, L. Bai, and E. R. Hancock, “Quantum-based subgraph convolutional neural networks,” Pattern Recognit., Vol. 88, pp. 38–49, 2019.
   Web of Science ®Google Scholar
• P. Fournier-Viger, C. Cheng, Z. Cheng, J. C.-W. Lin, and N. Selmaoui-Folcher, “Mining significant trend sequences in dynamic attributed graphs,” Knowl. Based. Syst., Vol. 182, pp. 104797, 2019.
   Web of Science ®Google Scholar
• Z. Wang, Y. Zhao, Y. Yuan, G. Wang, and L. Chen, “Extreme learning machine for large-scale graph classification based on mapreduce,” Neurocomputing, Vol. 261, pp. 106–14, 2017.
   Web of Science ®Google Scholar
• S. Linoy, H. Mahdikhani, S. Ray, R. Lu, N. Stakhanova, and A. Ghorbani, “Scalable privacy-preserving query,” in Processing over Ethereum Blockchain. 2019 IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, 2019.
   Google Scholar
• H. Gupta, S. Mehta, S. Hans, B. Chatterjee, P. Lohia, and C. Rajmohan, “Provenance in context of Hadoop as a Service (HaaS) – state of the art and research directions,” in 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honololu, HI, 2017, pp. 680–3. doi:10.1109/CLOUD.2017.91.
   Google Scholar
• N. Abdullah, A. Hakansson, and E. Moradian, “Blockchain based approach to enhance big data authentication in distributed environment,” in 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Honololu, HI, 2017.
   Google Scholar
• T. Renner, J. Muller, and O. Kao, “Endolith: a blockchain-based framework to enhance data retention in cloud storages,” in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), Cambridge, UK, 2018.
   Google Scholar
• D. T. Jose, A. Chakravorty, and C. Rong, “TOTEM: Token for controlled computation: Integrating blockchain with big data,” in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019.
   Google Scholar
• S. K. Singh, S. Rathore, and J. H. Park, “BlockIoTIntelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence,” Future Gener. Comput. Syst., Vol. 110, pp. 721–43, 2019.
   Web of Science ®Google Scholar
• L. Yue, H. Junqin, Q. Shengzhi, and W. Ruijin, “Big data model of security sharing based on blockchain,” in 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), Chengdu, China, 2017.
   Google Scholar
• W. Dhifli, S. Aridhi, and E. M. Nguifo, “MR-SimLab: Scalable subgraph selection with label similarity for big data,” Inf. Syst., Vol. 69, pp. 155–63, 2017.
   Web of Science ®Google Scholar
• Q. Zhang, X. Song, Y. Yang, H. Ma, and R. Shibasaki, “Visual graph mining for graph matching,” Comput. Vis. Image Underst, Vol. 178, pp. 16–29, 2019.
   Web of Science ®Google Scholar
• L. Yuan, J. Bin, and P. Pan, “Optimized distributed subgraph matching algorithm based on partition replication,” Electronics, Vol. 9, pp. 184, 2020.
   Web of Science ®Google Scholar
• M. Abulaish, Z. A. Ansari, and Jahiruddin, “SubISO: A scalable and novel approach for subgraph isomorphism search in large graph,” in 2019 11th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India, 2019.
   Google Scholar
• M. Fang, Z. Zhang, C. Jin, and A. Zhou, “High-performance smart contracts concurrent execution for permissioned blockchain using SGX,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 2021, pp. 1907–12.
   Google Scholar
• D. Chae, B. Kim, S. Kim, and S. Kim, “Incremental feature selection for efficient classification of dynamic graph bags,” Concurrency Comput. Pract. Exp., Vol. 32, no. 18, pp. e5502, 2020.
   Web of Science ®Google Scholar
• B. Lang, B. Wu, Y. Liu, X. Liu, and B. Zhang, “Fast graph similarity search via hashing and its application on image retrieval,” Multimed. Tools. Appl., Vol. 77, no. 13, pp. 16177–98, 2017.
   Web of Science ®Google Scholar
• B. Lyu, L. Qin, X. Lin, L. Chang, and J. X. Yu, “Supergraph search in graph databases via hierarchical feature-tree,” IEEE Trans. Knowl. Data Eng., Vol. 31, no. 2, pp. 385–400, 2018.
   Web of Science ®Google Scholar
• X. Wang, Y. Wang, C. Gao, K. Lin, and Y. Li, “Automatic diagnosis with efficient medical case searching based on evolving graphs,” IEEE Access, Vol. 6, pp. 53307–18, 2018.
   Web of Science ®Google Scholar
• Y. Zhu, L. Qin, J. X. Yu, and H. Cheng, “Answering top-k graph similarity queries in graph databases,” IEEE Trans. Knowl. Data Eng., Vol. 32, pp. 1459–74, 2019.
   Web of Science ®Google Scholar
• J. V. Muñoz, M. A. Gonçalves, Z. Dias, and R. D. S. Torres, “Hierarchical clustering-based graphs for large scale approximate nearest neighbor search,” Pattern Recognit., Vol. 96, pp. 106970, 2019.
   Web of Science ®Google Scholar
• P. Moutafis, G. Mavrommatis, M. Vassilakopoulos, and S. Sioutas, “Efficient processing of all-k-nearest-neighbor queries in the MapReduce programming framework,” Data. Knowl. Eng., Vol. 121, pp. 42–70, 2019.
   Web of Science ®Google Scholar
• S. Fathimabi, R. B. V. Subramanyam, and D. V. L. N. Somayajulu, “MSP: multiple sub-graph query processing using structure-based graph partitioning strategy and MapReduce,” J. Kind Saud Univ. Comput. Inf. Sci., Vol. 31, no. 1, pp. 22–34, 2019.
   Web of Science ®Google Scholar
• X. Chen, H. Huo, and V. J. S. Huan, “Efficient graph similarity search in external memory,” IEEE Access, Vol. 5, pp. 4551–60, 2017.
   Web of Science ®Google Scholar
• X. Wan, H. Wang, and J. Li, “LKAQ: Large-scale knowledge graph approximate query algorithm,” Inf. Sci., Vol. 505, pp. 306–24, 2019.
   Web of Science ®Google Scholar
• S. Surati, D. C. Jinwala, and S. Garg, “BMMI-tree: A peer-to-peer m-ary tree using 1-m node splitting for an efficient multidimensional complex query search,” J. Parallel. Distrib. Comput., Vol. 125, pp. 1–17, 2019.
   Web of Science ®Google Scholar
• Q. Qu, I. Nurgaliev, M. Muzammal, C. Jensen, and J. Fan, “On spatio-temporal blockchain query processing,” Future Gener. Comput. Syst., Vol. 98, pp. 208–18, 2019.
   Web of Science ®Google Scholar
• J. Song, X. Luo, J. Gao, C. Zhou, J. Yu, and H. Wei, “Uniwalk: Unidirectional random walk based scalable SimRank computation over large graph,” IEEE Trans. Knowl. Data Eng., Vol. 30, no. 5, pp. 992–1006, 2017.
   Web of Science ®Google Scholar
• C. Zhang, L. Zhu, C. Xu, K. Sharif, C. Zhang, and X. Liu, “PGAS: Privacy-preserving graph encryption for accurate constrained shortest distance queries,” Inf. Sci., Vol. 506, pp. 325–45, 2020.
   Web of Science ®Google Scholar
• Z. Yu, A. Abraham, X. Yu, Y. Liu, J. Zhou, and K. Ma, “Improving the effectiveness of keyword search in databases using query logs,” Eng. Appl. Artif. Intell., Vol. 81, pp. 169–79, 2019.
   Web of Science ®Google Scholar
• G. Sun, G. Liu, Y. Wang, and X. Zhou, “Updates-aware graph pattern based node matching.” 2020.
   Google Scholar
• A. Kansal, and F. Spezzano, “A scalable graph-coarsening based index for dynamic graph databases,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management – CIKM ‘17, Singapore, 2017.
   Google Scholar
• X. Zuo, L. Li, H. Peng, S. Luo, and Y. Yang, “Privacy-preserving subgraph matching scheme with authentication in social networks,” IEEE Trans. Cloud Comput., Vol. 10, pp. 2038–49, 2020.
   Web of Science ®Google Scholar
• Y. Zhu, H. Li, J. Cui, and Y. Ma, “Verifiable subgraph matching with cryptographic accumulators in cloud computing,” IEEE Access, Vol. 7, pp. 169636–45, 2019.
   Web of Science ®Google Scholar
• X. Han, C. Jia, L. Ding, X. Ding, and B. Song, “Dynamic top-K interesting subgraph query on large-scale labeled graphs,” Information, Vol. 10, pp. 61, 2019.
   Google Scholar
• X. Shan, G. Wang, L. Ding, B. Song, and Y. Xu, “Top-k subgraph query based on frequent structure in large-scale dynamic graphs,” IEEE. Access., Vol. 6, pp. 78471–82, 2018.
   Web of Science ®Google Scholar
• Z. Sun, H. Huo, and X. Chen, “Fast top-K graph similarity search via representative matrices,” IEEE. Access., Vol. 6, pp. 21408–17, 2018.
   Web of Science ®Google Scholar
• R. Roul, and I. Bansal, “GM-tree: An efficient frequent pattern mining technique for dynamic database,” in 2014 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior, India, 2014, pp. 1–6.
   Google Scholar
• J. Kim, and K. Jung, “Fractal tree analysis of drainage patterns,” Water Resour. Manage, Vol. 29, pp. 1217–30, 2014.
   Web of Science ®Google Scholar
• J. Tang, B. Zhang, Y. Zhou, and L. Wang, “An energy-aware spatial index tree for multi-region attribute query aggregation processing in wireless sensor networks,” IEEE. Access., Vol. 5, pp. 2080–95, 2017.
   Web of Science ®Google Scholar