Abstract
In the era of big data, recent developments in the field of information and communication technologies are facilitating organizations to innovate and grow. These technological developments and wide adaptation of ubiquitous computing enable numerous opportunities for government and companies to discover useful trends or patterns that are used in health-care decision making. A common problem affecting data quality is the presence of noise and irrelevant information which can lead decision makers to a wrong decision. Intelligent Decision Support System (IDSS) an automated judgment that supports decision making is composed of human and computer interaction to help in decision-making accuracy. Also, multi-agent systems (MAS) are collections of independent intelligent entities that collaborate in the joint resolution of a complex problem. Multi-agent IDSS can be used to solve large-scale convention problem. In this paper, we introduce a multiagent-MapReduce framework based dimension reduction for medical diagnosis that can filter the noise and irrelevant information and keeps only smart data, which can lead to a reduced storage space in one hand and produce a better health-care decision in the other hand.
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Sakr, S., Gaber, M. (eds.): Large Scale and Big Data: Processing and Management. Auerbach, Philadelphia (2014)
Groves, P., Kayyali, B., Knott, D., Kuiken, S.V.: The big data revolution in healthcare. McKinsey Q. 2, 3 (2013)
James, M., Michael, C., Brad, B., Jacques, B., Richard, D., Charles, R., Angela, H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. The McKinsey Global Institute, New York (2011)
Bologa, A., Bologa, R.: Business intelligence using software agent. Database Syst. J. 2(4), 31–42 (2011)
Talib, R., Hanif, M.K., Fatima, F., Ayesha, S.: A multi-agent framework for data extraction, transformation and loading in data warehouse. Int. J. Adv. Comput. Sci. Appl. 7(11), 351–354 (2016)
Belghache, E., Georg, J., Gleizes, M.: Towards an adaptive multi-agent system for dynamic big data analytics. In: Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (2016)
Twardowski, B., Ryzko, D.: Multi-agent architecture for real-time big data processing. In: International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (2014)
El Fazziki, A., Sadiq, A., Ouarzazi, J., Sadgal, M.: A multi-agent framework for a hadoop based air quality decision support system. In: Advenced Information Systems Engineering (2015)
Qayumi, K., Norta, A.: Business-intelligence mining of large decentralized multimedia datasets with a distributed multi-agent system. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(06), 1160–1169 (2016)
Lenk, A., Bonorden, L., Hellmanns, A., Roedder, N., Jaehnichen, S.: Towards a taxonomy of standards in smart data. In: 2015 IEEE International Conference on Big Data (Big Data) (2015)
Triguero, I., Maillo, J., Luengo, J., Garcia, S., Herrera, F.: From big data to smart data with the k-nearest neighbours algorithm. In: IEEE International Conference on Smart Data (Smart Data 2016) (2016)
Wu, M.: The key to insight discovery: where to look in big data to find insights (2013)
Wu, M.: The big data fallacy (2012)
Garcia-Gil, D., Luengo, J., Garcia, S., Herrera, F.: Enabling smart data: noise filtering in big data classification. Computer Science (2017). arXiv:1704.01770 [cs.DB]
Fu, Y., Jiang, H., Xiao, N.: A scalable inline cluster deduplication framework for big data protection. In: Middleware, pp. 354–373 (2012)
Xia, W., Jiang, H., Feng, D., Hua, Y.: SiLo: a similarity-locality based near-exact deduplication scheme with low RAM overhead and high throughput. In: USENIX Annual Technical Conference (2011)
Nurse, J., Rahman, S., Creese, S., Goldsmith, M., Lambert, K.: Information quality and trustworthiness: a topical state-of-the-art review. In: International Conference on Computer Applications and Network Security, pp. 492–500. IEEE (2011)
Ramirez-Gallego, S., Krawczyk, B., Garcia, S., Wozniak, M., Herrera, F.: A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239, 39–57 (2017)
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Elaggoune, Z., Maamri, R., Boussebough, I. (2018). A Multi-agent Framework for Medical Diagnosis Driven Smart Data in a Big Data Environment. In: Auer, M., Tsiatsos, T. (eds) Interactive Mobile Communication Technologies and Learning. IMCL 2017. Advances in Intelligent Systems and Computing, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-319-75175-7_71
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DOI: https://doi.org/10.1007/978-3-319-75175-7_71
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