izvorni znanstveni rad
<jats:p>This chapter introduces an innovative methodology for error and attenuation detection in network services by unifying the REFII time series model with Bayesian networks (BNs), and complementary data science techniques traditionally applied outside temporal analytics. The proposed framework addresses the critical challenge of identifying rare, high-impact events—such as signal degradation in mobile or fixed networks—that compromise service quality but are often obscured by complex temporal dependencies and imbalanced data distributions. This transformation facilitates non-temporal expansion, a novel interpolation process that enriches time series with contextual events and operational parameters lacking inherent temporal markers. Proposed solution unites advanced analytical techniques with decision making process for pattern mining like Bayesian networks, decision trees, FTP, K-Means clustering.</jats:p>
AI, Time series, temporal analytics, Bayesian networks
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
