Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο: https://ruomo.lib.uom.gr/handle/7000/387
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorSkaperas, Sotiris-
dc.contributor.authorMamatas, Lefteris-
dc.contributor.authorChorti, Arsenia-
dc.date.accessioned2019-10-31T07:43:14Z-
dc.date.available2019-10-31T07:43:14Z-
dc.date.issued2019-
dc.identifier10.1109/ACCESS.2019.2940816en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2019.2940816en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/387-
dc.description.abstractVideo content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. In this paper, we propose the employment of on-line change point (CP) analysis to implement real-time, autonomous and low-complexity video content popularity detection. Our proposal, denoted as real-time change point detector (RCPD) , estimates the existence, the number and the direction of changes on the average number of video visits by combining: (i) off-line and on-line CP detection algorithms; (ii) an improved time-series segmentation heuristic for the reliable detection of multiple CPs; and (iii) two algorithms for the identification of the direction of changes. The proposed detector is validated against synthetic data, as well as a large database of real YouTube video visits. It is demonstrated that the RCPD can accurately identify changes in the average content popularity and the direction of change. In particular, the success rate of the RCPD over synthetic data is shown to exceed 94% for medium and large changes in content popularity. Additionally, the dynamic time warping distance, between the actual and the estimated changes, has been found to range between 20 samples on average, over synthetic data, to 52 samples, in real data. The rapid responsiveness of the RCPD is instrumental in the deployment of real-time, lightweight load balancing solutions, as shown in a real example.en_US
dc.language.isoenen_US
dc.sourceIEEE Accessen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.titleReal-Time Video Content Popularity Detection Based on Mean Change Point Analysisen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume7en_US
local.identifier.firstpage142246en_US
local.identifier.lastpage142260en_US
Εμφανίζεται στις Συλλογές: Τμήμα Εφαρμοσμένης Πληροφορικής

Αρχεία σε αυτό το Τεκμήριο:
Αρχείο Περιγραφή ΜέγεθοςΜορφότυπος 
08835019.pdf4,13 MBAdobe PDFΠροβολή/Ανοιγμα


Τα τεκμήρια στο Αποθετήριο προστατεύονται από πνευματικά δικαιώματα, εκτός αν αναφέρεται κάτι διαφορετικό.