Network data streams offer an abstraction of complex systems from the real-world, which can be seen as producers of unbounded sequences of complex data generated at high speed. Many complex systems evolve according to stochastic processes which remain unknown to the interested users. As a consequence, changes happen in an unpredictable manner and may involve various portions of the observed complex systems. In this scenario, an interesting problem concerns the identiffcation and characterization of the changes that may concern both the whole structure of a complex system and small parts of it. We conjecture that the former can be explained by the latter and conversely, the latter can trigger the former. This type of problem requires a quite holistic strategy that traditional approaches often do not carry out because they focus on either the whole network or on some portions only. In this discussion paper, we describe a descriptive data mining approach based on frequent pattern discovery that we designed for recent research work. It combines frequent pattern with automatic time-window setting, in order to identify and characterize macroscopic changes and microscopic changes as changes that have an impact on a substantial part of the network or on specific portions, respectively. We provide arguments of the viability to real-world applications through two case studies, more precisely, telecommunication networks and geo-sensor networks.

Mining microscopic and macroscopic changes in network data streams (discussion paper)

Loglisci C.;Impedovo A.;Ceci M.;Malerba D.
2019

Abstract

Network data streams offer an abstraction of complex systems from the real-world, which can be seen as producers of unbounded sequences of complex data generated at high speed. Many complex systems evolve according to stochastic processes which remain unknown to the interested users. As a consequence, changes happen in an unpredictable manner and may involve various portions of the observed complex systems. In this scenario, an interesting problem concerns the identiffcation and characterization of the changes that may concern both the whole structure of a complex system and small parts of it. We conjecture that the former can be explained by the latter and conversely, the latter can trigger the former. This type of problem requires a quite holistic strategy that traditional approaches often do not carry out because they focus on either the whole network or on some portions only. In this discussion paper, we describe a descriptive data mining approach based on frequent pattern discovery that we designed for recent research work. It combines frequent pattern with automatic time-window setting, in order to identify and characterize macroscopic changes and microscopic changes as changes that have an impact on a substantial part of the network or on specific portions, respectively. We provide arguments of the viability to real-world applications through two case studies, more precisely, telecommunication networks and geo-sensor networks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/259180
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