Download Visualization

ACADEMIC
VISUALIZATION
AN INSIGHT INTO
ACADEMIA
CHANGFENG LIU
ACEMAP GROUP
MAY 31, 2016
OUTLINE
• Background
• Work Review
• SchoolDrag
• Future work
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Academic Visualization
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• Q&A
OUTLINE
• Background
• What is visualization?
• Why academic visualization?
• Work Review
• SchoolDrag
• Future work
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Academic Visualization
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• Q&A
WHAT IS
VISUALIZATION
• Wikipedia:
• Information visualization is the study of
(interactive) visual representations of abstract
data to reinforce human cognition.
• An easy way to understand the world!
Visualization
Academic Visualization
http://echarts.baidu.com/demo.html#heatmap-map
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http://www.air-level.com/rank
May 31, 2016
WHY ACADEMIC
VISUALIZATION
• Prof. Wang:
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• We have got massive academic data,
which is like raw food materials. What
we should do is to cook delicious
dishes!
• How to be a good chef?
WHY ACADEMIC
VISUALIZATION
Information visualization!
May 31, 2016
Academic Visualization
Data warehousing, DBMS
Data mining, knowledge discovery
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Crawling, data cleaning
WHY ACADEMIC
VISUALIZATION
• Why we need to build our own academic visualization?
• Because traditional academic search engines
provide few visualizations…
May 31, 2016
Academic Visualization
Microsoft academic
dblp
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Google Scholar
OUTLINE
• Background
• Work Review
• SchoolDrag
• Future work
May 31, 2016
Academic Visualization
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• Q&A
WORK REVIEW
• One paper accepted by WWW BigScholar workshop
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• Two patents submitted
WORK REVIEW
Local network analysis and hierarchical clustering
By clicking the nodes, networks of
the references are expanded and
collapsed.
Find multiple paths to the
center paper and highlight
them in the graph.
The cluster graph
shows fields in
computer science.
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Academic Visualization
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Zhaowei Tan, Changfeng Liu, et al., "AceMap: A Novel Approach towards Displaying Relationship among Academic
Literatures", WWW BigScholar Workshop, 2016
BEYOND PAPER + PAPER
WE CAN SEE MORE!
• Paper + University?
• Paper + Timeline?
• Paper + Location?
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OUTLINE
• Background
• Work Review
• SchoolDrag
•
•
•
•
Motivation
Visualization tool selection
Handle the data
Real-time querying
• Future work
May 31, 2016
Academic Visualization
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• Q&A
SCHOOLDRAG
• Our team implemented three visualizations this
semester
• Organization
• Geographical location
• Hierarchical structure
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• I will introduce the one about
organizations -- SchoolDrag
MOTIVATION
• Select good graduate
schools in a certain field
• Find out the developing
trends of different universities
• Which university is the
groundbreaker? Which
university is the follower?
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• No academic search engines
provide such a function.
VISUALIZATION TOOL
SELECTION
• There are many tools available
• D3 and vega
• Tableau
• Gephi
• Eigenfactor
• How to choose?
Open-source
Easy to code
Community support
Web-based
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•
•
•
•
HANDLE THE DATA -GOAL
• To know the numbers of published papers of every
organization in every year.
• To get the values of corresponding authors of
every organization in every year.
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• To demonstrate the developing trends and
academic potential of different organizations in an
interactive way.
HANDLE THE DATA -OTHER TOOLS
• MySQL to manage and retrieve data
MySQL
• C++ & Python to do the computation
Batch files
• JSON to store the data
C++
Python
JSON
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• Batch files to do the repeating tasks
HANDLE THE DATA -RESULTS
• Two fields according to CCF recommendation
list
• Network
• Artificial intelligence
• Ten L1 or L2 fields according to MAG
datasets
• Computer security
• Computer vision
• Embedded system
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• ……
• More fields are coming
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Academic Visualization
http://acemap.sjtu.edu.cn/schoolDrag/networktop100.html
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DEMO TIME
OFFLINE DEMO
1. Each circle stands for an
academic organization.
2. The more published
papers an organization
has in the specified year,
the bigger the circle is.
3. The curve shows the
developing trends of
the total number of
published papers and
authors of an
organization.
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schoolDrag in AI field
REAL-TIME QUERYING
• Real-time Querying V.S. Retrieving JSON files
• More storage efficient – 50k fields in MAG dataset
• More flexible – what if a new field joins
• Less time efficient – minute-level waiting is
unbearable
• How to deal with this trade-off?
• Rapid sampling[1]
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[1] Kim, Albert, et al. "Rapid sampling for visualizations with ordering guarantees." Proceedings of the VLDB Endowment
8.5 (2015): 521-532.
REAL-TIME QUERYING -RAPID SAMPLING
• Main ideas
• Part stands for the whole
population
• Only focusing on what
we care about
• Confidence intervals
count
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Kim, Albert, et al. "Rapid sampling for visualizations with ordering guarantees." Proceedings of the VLDB Endowment 8.5
(2015): 521-532.
REAL-TIME QUERYING -RAPID SAMPLING
• For our scenario
• Focus on ordering rather than exact paper
numbers
• Compute in back-end and returning JSON files
• Combine static JSON with rapid sampling
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Kim, Albert, et al. "Rapid sampling for visualizations with ordering guarantees." Proceedings of the VLDB Endowment 8.5
(2015): 521-532.
OUTLINE
• Background
• Work Review
• SchoolDrag
• Future work
May 31, 2016
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• Q&A
FUTURE WORK
• Building an interactive visualization for Junxian’s
paper
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• Focusing on clustering algorithms at theory group
(how to cook food)
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Q&A
THANK
YOU!
May 31, 2016
Academic Visualization