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researcher

Nan Cao

Tongji University

16 papers hereh-index 444.9k citations129 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author8

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.HC8
  • cs.SI4
  • cs.AI2
  • cs.LG2
affiliations
  • Tongji University
  • Shanghai Innovation Institute
Homepage
same name
  • Nan Cao — 10 papers, h 12
  • Nan Cao — 10 papers, h 2
  • Nan Cao — 5 papers, h 1
  • Nan Cao — 3 papers, h 2
  • Nan Cao — 2 papers, h 2
  • Nan Cao — 2 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142021
most citedCalliope: Automatic Visual Data Story Generation from a Spreadsheet

179 citations · 393 across the 16 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.HC2019

Task-Oriented Optimal Sequencing of Visualization Charts

Danqing Shi, Yang Shi, Xinyue Xu +4

A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts. It provides information details in each chart as well…

cs.AI2019

EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data

Zhuochen Jin, Nan Cao, Yang Shi +2

The rapid development of urbanization during the past decades has significantly improved people's lives but also introduced new challenges on effective functional urban planning an…

cs.HC2019★ 23 cited

Visual Anomaly Detection in Event Sequence Data

Shunan Guo, Zhuochen Jin, Qing Chen +3

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analys…

cs.HC2019★ 3 cited

Visual Analytics of Anomalous User Behaviors: A Survey

Yang Shi, Yuyin Liu, Hanghang Tong +3

The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it h…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.