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Jo-Anne Tan

dblp:163/0606 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Databases, data management, data science and information retrieval · 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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 77% Computational social science and digital humanities · 23%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Smart cities and intelligent transportation
public transit		 0.212015
FTT: A System for Finding and Tracking Tourists in Public Transport Services · SIGMOD Conference 2015
Data mining › spatiotemporal data mining › trajectory data mining
trajectory pattern mining		 0.212015
FTT: A System for Finding and Tracking Tourists in Public Transport Services · SIGMOD Conference 2015

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

data mining · 0.4
YearPublicationVenuePosition
2015 FTT: A System for Finding and Tracking Tourists in Public Transport Services
abstract
The tourism industry is a key economic driver for many cities. To understand tourists' traveling patterns can help both public and private relevant sectors design and improve their services to serve tourists better and get additional values from it. The existing approaches to discover tourists' traveling pattern focus on small sets of known tourists extracted from social media or other channels. The accuracy of the mining result cannot be guaranteed due to the small and bias set of samples.
Huayu Wu 0001, Jo-Anne Tan, Wee Siong Ng, Mingqiang Xue, Wei Chen 0025
 SIGMOD Conference2