Min Chen 0010

dblp:50/6996-10 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia systems and quality of experience · 100%
Computer networks
1 paper
Network measurement and analytics · 50% Network management and operations · 50%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia systems and quality of experience › quality of experience
qoe measurement
0.112011
Q-score: proactive service quality assessment in a large IPTV system · Internet Measurement Conference 2011
Network measurement and analytics › network performance measurement
quality of service monitoring
0.012011
Q-score: proactive service quality assessment in a large IPTV system · Internet Measurement Conference 2011

Methods — techniques the papers use, named apart from their topics

proactive monitoring · 0.2performance indicator selection · 0.2
YearPublicationVenuePosition
2011 Q-score: proactive service quality assessment in a large IPTV system
abstract
In large-scale IPTV systems, it is essential to maintain high service quality while providing a wider variety of service features than typical traditional TV. Thus service quality assessment systems are of paramount importance as they monitor the user-perceived service quality and alert when issues occurs. For IPTV systems, however, there is no simple metric to represent user-perceived service quality and Quality of Experience (QoE). Moreover, there is only limited user feedback, often in the form of noisy and delayed customer calls. Therefore, we aim to approximate the QoE through a selected set of performance indicators in a proactive (i.e., detect issues before customers reports to call centers) and scalable fashion.
Han Hee Song, Zihui Ge, Ajay Mahimkar, Jia Wang 0001, Jennifer Yates, Yin Zhang 0001, Andrea Basso 0001, Min Chen 0010
Internet Measurement Conference8