Abstract
Data originating from diverse sources, including relational and NoSQL databases, web pages, texts, images, recordings, and videos, are expanding in both size and complexity. Navigating through these vast datasets poses significant challenges. As the volume of data grows, the complexity of analysis intensifies. Multidimensional data analysis requires effective organization of the data, and different ranking methods can help achieve this goal. Ranking is a way to create a linear order of the data items that reflects their similarity or importance. The essence of ranking lies in providing a systematic way to traverse the entirety of a dataset, from its inception to its conclusion. In this paper, we introduce a novel method named “Spanning Thread" (ST) for classification and ranking multidimensional data. ST aims to establish a meaningful path connecting all data points and starts with a randomly selected data. Additionally, we present OST (Ordered Spanning Thread), which commences with the minimum virtual data and concludes with the maximum virtual data. Both methods are evaluated using an open dataset, wherein we measure the frequency of class changes to assess their effectiveness.
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Hussenet, L., Boucetta, C., Herbin, M. (2024). Spanning Thread: A Multidimensional Classification Method for Efficient Data Center Management. In: Phillipson, F., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2024. Communications in Computer and Information Science, vol 2109. Springer, Cham. https://doi.org/10.1007/978-3-031-60433-1_13
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DOI: https://doi.org/10.1007/978-3-031-60433-1_13
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