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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Wed, 7 Feb 2018 08:59:58 -0500
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*2018 IEEE Big Data Cup*


We invite industrial, government, and academic organizations to submit
proposals to organize a Data Challenge for  the 2018 IEEE International
Conference on Big Data (IEEE Big Data 2018, ( ) . Data Challenges
are analytics and programming contests based on datasets released by the
organizations sponsoring the competitions. These contests will run for
several months, with winners announced in the IEEE Big Data 2018  in
December 10-13, 2018.

The IEEE Big Data conference series started in 2013 has established itself
as the top tier research conference in Big Data.

·       The first conference IEEE Big Data 2013 had more than 400
registered participants from 40 countries ( and the regular paper
acceptance  rate is 17.0%.

·       The IEEE Big Data 2017 ( ,
regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14,
2017 with close to 1000 registered participants from 50 countries.

Strong proposals will release interesting and novel datasets to the
participants and propose tasks that are challenging, yet achievable within
the expected timeline. To encourage broad participation in the challenge
competition, tasks and data should be made publicly accessible where

We will consider topics from (but not limited to) the following big data
application domains:

●      Mobility

●      Health and wellness

●      Finance

●      Advanced manufacturing

●      Retail and e-commerce

●      Smart City and Community

●      Blockchain Technology

●      Transportation and autonomous driving

The Challenges involve a released dataset and a well-defined problem with
clear evaluation metrics. The challenges run on a data competition platform
(e.g., Kaggle) that maintains a leaderboard for the participants. Final
winners are selected based on their standing on the leaderboard, and a
6-page report describing their algorithms and results. There will be a
workshop in the IEEE BigData 2018 conference in which the invited teams
will be able to present their solution.

*Tentative Competition Timeline. *

Step 1 - Solicitation of big data problems/topics, March 20, 2018

Step 2 - Proposal evaluation and selection of topics/problems, April 05,

Step 3 - Data Release, Platform setup and solicitation of challenge
participants, April 20, 2018

Step 4 -  Deadline for contest teams to submit letter of intent, May 31,

Step 5 - Deadline for contest teams to submit final report and solutions,
November 10, 2018

Step 6 - Announcement of winning teams, Nov 20, 2018

*Data Challenge Proposal Format*

Proposals should answer the following questions:

●      Who are the organizers? Provide the names, affiliations, contact
information, and brief bio of the organizers.

●      Which sector does the proposed Data Challenge belong to?

●      What is the dataset to be released? If there are privacy concerns
regarding the data, we advise that the organizers first seek clearance from
the legal department of their organization regarding the dataset release.

●      What prizes will be offered? Note that cash prizes will be shared
between competitors and BigData, to defray the expenses of hosting
demonstrations of the finalists’ submissions at BigData.

●      Do you require source code with submissions?

●      * Challenge Specific Questions:*

●      What are the task and the evaluation metrics?

●      What competition infrastructure will be used? Our default hosting
platform is Kaggle, which may impose fees to be borne by Challenge
organizers. If you prefer to host it on your own infrastructure, specify
the reasons and the resources you can commit for running the competition.

*Deadline for proposal Submission: March 20, 2018. Please submit the


*2018 IEEE Big Data Cup Co-Chairs*

*Mohammad Al Hasan, Associate Professor, Indiana University Purdue
University Indianapolis (**[log in to unmask]* <[log in to unmask]>*)*

*Yicheng Tu, Associate Professor, University of South Florida (*
*[log in to unmask]* <[log in to unmask]>*) *


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