Abstract
AbstractThe objective of this paper was to propose a multi-agents model for automating big data processing, to improve the process of decision-making and urban planning. The huge amounts of collected data from different domains, such as urban management and remote sensing, are characterized as big data with a spatial component. Smart data is the approach to deal with big data characteristics and challenges by focusing on the Value aspect. The focus on smart data on the relevant data and the mechanism of automation and collaboration of the agents, will contribute to the efficient automating for big data analytics and processing. The proposed approach is based on a collection of agents, and adopt the concept of smart data, this paradigm focus on the aspect value from the big data and help to retrieve the useful information from the large volumes of data by intelligent processing. The proposed model describes the functionalities of the agents. The agents receive data in real time, perform the operations of storing data, pre-processing, streaming processing and batch processing and finally transfer the results of analysis to the services and applications. Machine learning techniques can be used to enhance the aspect of cognition of the agents; through a case study, we used supervised learning methods to build a classification model to support the process of urban planning by predicting the type of habitat adequate for the population based on their demographic and socio-economic characteristics.KeywordsSmart dataBig dataMulti-agent systemAutomationMachine learningUrban planningBig data analyticsDecision-making
Published Version
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