Ontology-Based Backpropagation Neural Network Classification and Reasoning Strategy for NoSQL and SQL Databases
Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132613Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 631
 P. Agarwa, R. Verma, A. Mallik, “Ontology based disease diagnosis system with probabilistic inference”, India International Conference on Information Processing (IICIP), pages: 1 – 5, 2016.
 F. Ali, D. Kwak, P. Khan, S. H. A. Ei-Sappagh, S. M. R. Islam, D. Park, K. Kwak, “Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots”, IEEE Access, Vol.5, pages: 12364 – 12379, 2017.
 A. Azqueta-Alzúaz, M. Patiño-Martinez, I. Brondino, R. Jimenez-Peris, “Massive Data Load on Distributed Database Systems over HBase”, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pages: 776 - 779, 2017.
 X.X. Dou, X.P. Wang, Application of Big Data Analysis Method in Supply Chain, Advances in Networks, Vol. 4, No. 1, pp. 1-5, 2016.
 J. Guo, X. Wu, “Research on optimization of community mass data storage based on HBase”, Third International Conference on Cyberspace Technology (CCT 2015), pages: 1 – 4, 2015.
 M. Kwon, M. Ju, S. Choi, “Classification of various daily behaviors using deep learning and smart watch”, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pages: 735 – 740, 2017.
 Y. Kravchenko, E. Kuliev, I. Kursitys, “Information's semantic search, classification, structuring and integration objectives in the knowledge management context problems”, 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), pages: 1 – 5, 2017.
 C. Ramesh, K. V. C. Rao, A. Govardhan, “Ontology based web usage mining model”, 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pages: 356 – 362, 2017.
 S. Seo, J. Kim, L. Choi, “Semantic hashtag relation classification using co-occurrence word information”, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pages: 860 – 862, 2017.
 X. Tang, B. Han, H. Chen, “A hybrid index for multi-dimensional query in HBase”, 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), pages: 332 - 336, 2016.
 K. Wang, J. Zhang, D. Li, X. Zhang, T. Guo, “Adaptive Affinity Propagation Clustering”, Acta Automatica Sinica, Vol 33, No. 22, pages:1242-1246, 2007
 Z. Wei, J. M. Qu, L. Liu, C. Q. Zhu, W. J. Yin, “MDDM: A Method to Improve Multiple Dimension Data Management Performance in HBase”, 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS), pages: 102 - 109, 2015.
 J. Xu, L. LI, X. Lu, S. Hu, B. G, W. Xiao, L. Yao, “Behavior-Based Collective Classification in Sparsely Labeled Networks”, IEEE Access, Vol.5, pages: 12512 - 12525, 2017.