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Widespread emphasis on the importance of “big data” has fostered development of key computer infrastructures, from experimental and observational science to social networking, search and e-commerce. This in turn has seen the rise of big data applications such as large-scale optimization, machine learning, data mining, and artificial intelligence. We interpret “big data” liberally remembering that it can be big in the many ways sometimes labeled by Volume, Variety, Velocity, Veracity but can also be small high Value data. This encourages to set problems in context and discuss their full range in these dimensions. We would like to bring together IU researchers to explore interactions between software or hardware infrastructure (i.e. the "producers" of big data tools) and applications (the "consumers" of this infrastructure).
We propose a half-day meeting of short talks and discussions on how people at IU can collaborate in these areas. This would enable us to better inform one another about what is going on in our fields.

Time Speaker Topic Slides
8:45
Welcome
 
 
8:50
David Crandall
Problems in large-scale computer vision [slides]
9:10
David Leake
Large-Scale Case-Based Reasoning: Opportunity and Questions [slides]
9:30
Adam Coates
Deep Learning and HPC [slides]
9:50
Emilio Ferrara
Understanding content, user behaviors and information diffusion [slides]
10:10
Break
   
10:30
Beth Plale
It’s About Data: 50,0000 ft Overview of Data To Insight Center [slides]
10:50
Sriraam Natarajan
Practical Probabilistic Relational Learning 
[slides]
11:10
Ying Ding
Knowledge Graph: Connecting Big Data Semantics [slides]
11:30
Geoffrey Fox
Tools in SPIDAL: Scalable Parallel Interoperable Data Analytics Library [slides]
11:50
Judy Qiu
Analysis Tools for Data Enabled Science [slides]
12:30
- 13:30
Lunch/Discussions
   
Contacts: Judy Qiu (xqiu@indiana.edu), David Crandall (djcran@indiana.edu)

David Leake

David Crandall

Adam Coates

Beth Plale

Emilio Ferrara

Sriraam Natarajan

Ying Ding

Geoffrey Fox

Xiaozhou Liu

Judy Qiu
The discussion will brainstorm future steps such as broader nationwide collaboration, workshops at the national level, and prospective impacts on our academic programs at IU (like Data Science).
Specific objectives include identifying the core "big data" problems (algorithms) of interest to IU researchers, and the big data resources and tools that are available overall. Our hope is to establish beneficial collaborations between candidates from different domains. We invite you to attend this potentially groundbreaking workshop.