Abstract
Large amounts of data are generated daily, according to the wide usage of social media websites and scientific data. These data need to be stored and analyzed to help decision makers but the traditional database concepts are insufficient. Data warehouse and OLAP are useful technologies in the storage and analysis of big data. Using MapReduce will help to save processing time, using cloud computing will help in saving resources and storage. In this paper, we propose a system that integrates the OLAP and MapReduce over cloud (considering workload balance) in order to enhance the performance of query processing over big data. The proposed system is applied to large amounts of data stored in cubes located in a Peer-to-peer cloud; this process is done using an allocation approach to save resources and query processing times. The proposed system achieves enhancements as time saving in query processing and in resources usage.
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Ezzat, M., Ismail, R., Badr, N., Tolba, M.F. (2016). A Peer-to-Peer Architecture for Cloud Based Data Cubes Allocation. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_35
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DOI: https://doi.org/10.1007/978-3-319-26690-9_35
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