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Conference Papers Year : 2016

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

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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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Dates and versions

hal-01646637 , version 1 (23-11-2017)

Licence

Attribution - CC BY 4.0

Identifiers

  • HAL Id : hal-01646637 , version 1

Cite

Wang Wanting, Qin Zheng. A Semantically-Based Big Data Processing System Using Hadoop and Map-Reduce. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. pp.246-247. ⟨hal-01646637⟩
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