{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T04:10:45Z","timestamp":1685419845700},"reference-count":0,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Remote desktop connection (RDC) services offer clients the ability to access\n   remote content and services, commonly in the context of accessing their\n   working environment. With the advent of cloud-based services, an example use\n   case is that of delivering virtual PCs to users in WAN environments. In this\n   paper, we aim to detect and analyze common user behavior when accessing RDC\n   services, and use this as input for making Quality of Experience (QoE)\n   estimations and subsequently providing input for effective QoE management\n   mechanisms. We first identify different behavioral categories, and conduct\n   traffic analysis to determine a feature set to be used for classification\n   purposes. We propose a machine learning approach to be used for classifying\n   behavior, and use this approach to classify a large number of real-world\n   RDCs. We further conduct QoE evaluation studies to determine the relationship\n   between different network conditions and subjective end user QoE for all\n   identified behavioral categories. Results show an exponential relationship\n   between QoE and delay and loss degradations, and a logarithmic relationship\n   between QoE and bandwidth limitations. Obtained results may be applied in the\n   context of network resource planning, as well as in making QoE-driven\n   resource allocation decisions.<\/jats:p>","DOI":"10.2298\/csis140810018s","type":"journal-article","created":{"date-parts":[[2015,6,9]],"date-time":"2015-06-09T11:16:03Z","timestamp":1433848563000},"page":"587-605","source":"Crossref","is-referenced-by-count":1,"title":["Statistical user behavior detection and QoE evaluation for thin client services"],"prefix":"10.2298","volume":"12","author":[{"given":"Mirko","family":"Suznjevic","sequence":"first","affiliation":[{"name":"University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lea","family":"Skorin-Kapov","sequence":"additional","affiliation":[{"name":"University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iztok","family":"Humar","sequence":"additional","affiliation":[{"name":"University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:32:25Z","timestamp":1685349145000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141500018S"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2015]]}},"URL":"https:\/\/doi.org\/10.2298\/csis140810018s","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]}}}