Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System - Service-Oriented and Cloud Computing Access content directly
Conference Papers Year : 2020

Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System

Rosa Di Salvo
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Antonino Galletta
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Massimo Villari
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Abstract

We exploit the feasibility of predictive modeling combined with the support given by a suitably defined IoT Cloud Infrastructure in the attempt of assessing and reporting relative performances for user-specific settings during a bike trial. The matter is addressed by introducing a suitable dynamical system whose state variables are the so-called origin-destination (OD) flow deviations obtained from prior estimates based on historical data recorded by means of mobile sensors directly installed in each bike through a fast real-time processing of big traffic data. We then use the Kalman filter theory in order to dynamically update an assignment matrix in such a context and gain information about usual routes and distances. This leads us to a dynamical ranking system for the users of the bike trial community making the award procedure more transparent.
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hal-03203278 , version 1 (20-04-2021)

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Rosa Di Salvo, Antonino Galletta, Orlando Marco Belcore, Massimo Villari. Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System. 8th European Conference on Service-Oriented and Cloud Computing (ESOCC), Sep 2020, Heraklion, Crete, Greece. pp.149-158, ⟨10.1007/978-3-030-44769-4_12⟩. ⟨hal-03203278⟩
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