[Asis-l] The First International Workshop on Data-Driven Discovery, in conjunction with SIGKDD2017

[Ying Ding]

CALL FOR PAPER

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The First International Workshop on Data-Driven Discovery
in conjunction with SIGKDD2017 (http://www.kdd.org/kdd2017/)
http://datainnovation.soic.indiana.edu:8080/kdd2017_workshop/index.html
August 14, 2017, Halifax, Nova Scotia - Canada
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The traditional mode of conducting science involves reading related 
articles, selectively evaluating datasets and “thinking through” 
testable hypotheses. Data was the bottleneck in this traditional 
scientific method, reserved only for final testing. With the rise of 
high-throughput experiments and sensors, the associated production of 
enormous data, and publication of more papers on many topics than any 
individual or team can peruse, the same artisanal approach to hypothesis 
generation both slows and narrows the scope of science relative to its 
potential. For one common disease (i.e., diabetes), more than 500,000 
articles have been published to date. If a scientist read 20 papers per 
day, it would take 68 years, by which time millions more will have been 
published. We need computational approaches to read, reason, and design 
hypotheses that transcend the capacity of individual teams. We need to 
deploy scientific creativity not only to craft individual questions, but 
the models and algorithms that will generate the most promising 
collections of questions. In short, we need computation to generate Big 
Questions equal to Big Data.

Researchers generate hypotheses in different ways. A dominant approach 
in biology and medicine is first-hand observation and lab test results, 
mining electronic medical records, and engagement with experimental and 
gene sequence data. Generating hypothesis from literature is viewed as a 
serendipitous process with great uncertainty. With the digitization of 
the international medical corpus, and increased production of 
born-digital publications, the vast amount of published knowledge 
contains a diversity of insight to which domain experts are rarely 
exposed and about which they cannot casually reason. This situation is 
exacerbated for transdisciplinary domains. The flipside of this 
challenge reveals the promise. Generating hypothesis from literature in 
different but related disciplines can reveal potential connections never 
before realized because experts from distant domains have not mastered 
each other's knowledge. Mining literature to generate hypotheses need 
not be confined to biology or medicine. It should be extended to all 
areas of science, scholarship, engineering and the arts. Given the 
enormous resource constraints facing contemporary research, the 
acceleration of discovery is indispensable scientific and societal advance.

This workshop aims to explore this timely topic because the Web has 
become the essential infrastructure to acquire, disseminate, and create 
data, information, and knowledge. Here we call for contributions on 
computational hypothesis generation to share their insights and move 
this field forward to generate scientific, technological, and societal 
impact. Ultimately, we hope that this event will visibly bring data and 
computation up the value chain of science from answers and certainty to 
questions and creativity.

Our workshop call for contributions covering, but not limited to, the 
following topics:

Tactics of Discovery
· Text mining (NLP) for Knowledge Discovery
· Graph Mining for Knowledge Discovery
· Machine Learning for Knowledge Discovery
· Data Mining for Knowledge Discovery
· Modeling Cognition for Discovery
· Complex Systems for Discovery
· Creativity and Discovery
· Design Thinking and Discovery

Medical Discovery
· Cancer pathways
· Literature, Data and Medical Record integration
· Drug repurposing
· Personalized medicine
· Discovery in Disease

Discovering the Brain
· Learning and Memory in the Human Brain
· Making sense of Neuroscience Data
· Understanding how the brain views a complex world

Computational Social Science and Service
· Discovery in Social Science
· Social Services
· Policing
· Education
· Business
· Poverty Reduction

Digital Humanities and Arts
· Digital Humanities
· Digital Painting
· Digital Recipes
· Computational Creativity: Story, joke and poetry generation

Workshop Schedule/Important Dates
"    Workshop paper submissions: June 10, 2017
"    Workshop paper notifications: June 23, 2017
"    Final submission of workshop program and materials: June 30, 2017
"    Workshop date: August 14, 2017

All papers submitted should have a maximum length of 8 pages and must be 
prepared using the ACM camera-ready 
templatehttp://www.acm.org/sigs/pubs/proceed/template.html. Authors are 
required to submit their papers electronically in PDF format. The 
submission website is https://easychair.org/conferences/?conf=kddddd2017.

Workshop Chairs:
Ying Ding, Indianan University
James A. Evans, University of Chicago
Scott Spangler, IBM Almaden Research Center
Lav Varshney, University of Illinois at Urbana-Champaign
Dashun Wang, Northwestern University

-- 
Ying Ding
Associate Director of Data Science Online Program
School of Informatics and Computing
Indiana University
http://info.slis.indiana.edu/~dingying/