Abstract
Network analysis is of great interest to web and cloud companies, largely because of the huge number of web-networks users and services. Analyzing web networks is helpful for organizations that profit from how network nodes (e.g. web users) interact and communicate with each other. Currently, network analysis methods and tools support single network analysis. One of the Web 3.0 trends, however, namely personalization, is the merging of several user accounts (social, business, and others) in one place. Therefore, the new web requires simultaneous multiple network analysis. Many attempts have been made to devise an analytical approach that works on multiple big data networks simultaneously. This chapter proposes a new model to map web multi-network graphs in a data model. The result is a multidimensional database that offers numerous analytical measures of several networks concurrently. The proposed model also supports real-time analysis and online analytical processing (OLAP) operations, including data mining and business intelligence analysis.
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This work has been supported by the University of Quebec at Chicoutimi and the Lebanese University (AZM Association).
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Karawash, A., Mcheick, H., Dbouk, M. (2014). Simultaneous Analysis of Multiple Big Data Networks: Mapping Graphs into a Data Model. In: Bessis, N., Dobre, C. (eds) Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-319-05029-4_10
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DOI: https://doi.org/10.1007/978-3-319-05029-4_10
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