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BigDF 2017 : IEEE International Workshop on Foundations of Big Data Computing


When Dec 18, 2017 - Dec 18, 2017
Where Jaipur, India
Submission Deadline Aug 25, 2017
Notification Due Sep 20, 2017
Final Version Due Oct 3, 2017
Categories    algorithms   big data analytics   machine learning   high performance computing

Call For Papers


IEEE International Workshop on Foundations of Big Data Computing (BigDF 2017)
In conjunction with HiPC 2017 (
December 18, 2017
Le Meridien, Jaipur, India

What constitutes a “Big Data” problem? What application domains are best suited to benefit from Big Data analytics and computing? What are the traits and characteristics of an application that make it suited to exploit Big Data analytics? How can Big Data systems and frameworks be designed to allow the integration and analysis of complex data sets? How can research in Big Data Analytics benefit from the latest advances in supercomputing and High Performance Computing (HPC) architectures? The goal of this workshop is to address questions like these that are fundamental to the advancement of Big Data computing, and in the process, build a diverse research community that has a shared vision to advance the state of knowledge and discovery through Big Data computing.

Topics of interest include research contributions and innovative methods in the following areas (but not limited to):

* Scalable tools, techniques and technologies for Big Data analytics (e.g., graph and stream data analysis, machine learning and emerging deep learning methods)
* Algorithms and Programming Models for Big Data
* Big Data applications - Challenges and Solutions (e.g., life sciences, health informatics, geoinformatics, climate, socio-cultural dynamics, business analytics, cybersecurity)
* Scalable Big Data systems, platforms, services, and management
* Big Data toolkits, workflows, metrics, and provenance.

We invite paper submissions that describe original research contributions in the area of Big Data computing, and position papers that highlight the potential challenges and opportunities that arise in Big Data computing. We also invite short papers that describe work-in-progress original research.

Regular papers can be up to 8 pages long and short papers can be up to 4 pages long. All submissions will undergo rigorous peer-review by the technical program committee, and accepted manuscripts will appear in the HiPC workshop ("HIPCW") proceedings and will be indexed by IEEE digital library. Authors of the accepted manuscripts will be required to present their work at the workshop proceedings.

Paper Submission link: The paper submission opens July 1, 2017. Click on the following link for submitting your papers:

Authors can submit an abstract prior to submitting the full paper for review. The abstract is not mandatory but is recommended to help organizers plan the review phase in a timely fashion (i.e., authors can submit a full paper without having submitted an abstract). However, submissions with only full papers will be reviewed.

Organizing Committee:

* General Chairs: Dinkar Sitaram (PESIT), Ananth Kalyanaraman (Washington State University)
* Program Chairs: Madhu Govindaraju (SUNY Binghamton), Saumyadipta Pyne (IIPH, Hyderabad)
* Publicity Chair: Arindam Pal (TCS Research and Innovation)
* Proceedings Chair: Ren Chen, USC (HiPC proceedings chair)
* Industry Liaison: Vivek Yadav (FullStackNet, India)

Technical Program Committee:

Medha Atre, IIT Kanpur

Ariful Azad, Lawrence Berkeley National Laboratory

Biplab Banerjee, IIT Roorkee

Suren Byna, Lawrence Berkeley National Laboratory

Nabanita Das, Indian Statistical Institute

Oded Green, Georgia Institute of Technology

Manish Kurhekar, Visvesvaraya National Institute of Technology

Suresh Marru, Indiana University

Arindam Pal, TCS Research

Laks Raghupathi, Shell, India

Sudip Seal, Oak Ridge National Laboratory

Gokul Swamy, Amazon

Devesh Tiwari, Northeastern University

Abhinav Vishnu, Pacific Northwest National Laboratory

Yinglong Xia, Huawei Research America

Jaroslaw Zola, University of Buffalo

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