A Semantically-Based Big Data Processing System Using Hadoop and Map-Reduce

Abstract : In financial industry, a wide range of financial systems generate vast amount of data in different structures, which change with compliance rules change and hard to manage due to their heterogeneity. This paper introduces a semantically-based big data processing system to integrate the data from different sources, which realizes the query and computation in semantic layer. The system provides a new data management way for the financial industry. With Semantic Web, the information can be managed, integrated, and collaborated in a more fluent way than it in traditional ETL. In order to clear the complex logical relationship among data, the system uses SPARQL to query. Through Map-Reduce, this system, based on Hadoop and Hbase can improve the processing speed for big data.
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Communication dans un congrès
Maria Cecilia Calani Baranauskas; Kecheng Liu; Lily Sun; Vânia Paula de Almeida Neris; Rodrigo Bonacin; Keiichi Nakata. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-477, pp.246-247, 2016, Socially Aware Organisations and Technologies. Impact and Challenges
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Wang Wanting, Qin Zheng. A Semantically-Based Big Data Processing System Using Hadoop and Map-Reduce. Maria Cecilia Calani Baranauskas; Kecheng Liu; Lily Sun; Vânia Paula de Almeida Neris; Rodrigo Bonacin; Keiichi Nakata. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-477, pp.246-247, 2016, Socially Aware Organisations and Technologies. Impact and Challenges. 〈hal-01646637〉

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