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Zhongxian Tang

dblp:153/2316 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-0729-5898ORCID · corroborated

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

Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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.

Databases, data mining, and information retrieval
1 paper
Web and social media mining · 50% Information retrieval · 25% Data mining · 25%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › clustering › online clustering
data stream clustering
0.212014
EventEye: Monitoring Evolving Events from Tweet Streams · ACM Multimedia 2014
Web and social media mining › event analysis
event evolution tracking
0.212014
EventEye: Monitoring Evolving Events from Tweet Streams · ACM Multimedia 2014
Information retrieval › document retrieval › temporal information retrieval
event retrieval
0.212014
EventEye: Monitoring Evolving Events from Tweet Streams · ACM Multimedia 2014
Web and social media mining › event detection
social event detection
0.212014
EventEye: Monitoring Evolving Events from Tweet Streams · ACM Multimedia 2014
Visualization and visual analytics › spatiotemporal visualization
event visualization
0.112014
EventEye: Monitoring Evolving Events from Tweet Streams · ACM Multimedia 2014

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

stream clustering · 0.4multi-layer indexing · 0.4
YearPublicationVenuePosition
2020 Smart VM co-scheduling with the precise prediction of performance characteristics
Yuxia Cheng, Wenzhi Chen, Zonghui Wang, Zhongxian Tang, Yang Xiang 0001
Future Gener. Comput. Syst.4
2014 EventEye: Monitoring Evolving Events from Tweet Streams
abstract
With the rapid growth in popularity of social websites, social event detection has become one of the hottest research topics. However, continuously monitoring social events has not been well studied. In this demo, we present a novel system called EventEye to effectively monitor evolving events and visualize their evolving paths, which are discovered from tweet streams. In particular, four event operations are defined for our proposed stream clustering algorithm to capture the evolutions over time and a multi-layer indexing structure is designed to support efficient event search from large-scale event databases. In our system, events are visualized in different views, including evolution graph, timeline, map view, etc.
Hongyun Cai 0001, Zhongxian Tang, Yang Yang 0002, Zi Huang
ACM Multimedia2