Paper Submission Guidelines

Published papers will appear in the conference proceedings. Selected papers will be invited to publish on a Special Issue of the journal: Concurrency and Computing: Practice and Experience (Wiley).

Submissions may not exceed 4 pages in PDF format including figures and references, and must be formatted in the 2-column IEEE format. Submitted papers must be original work that has not appeared in and is not under consideration for another conference or journal. Work in progress is welcome, but preliminary results should be made available as a proof of concept.


A one-page abstract is required for poster submissions.

Templates for papers are available on the IEEE website:

http://www.ieee.org/conferences_events/conferences/publishing/templates.html

Paper and Poster Submissions are handled by EasyChair:

http://easychair.org/conferences/?conf=mcbda2016

Student poster competition will be carried out during the workshop and cash prizes will be awarded to first prize winner ($300), second prize winner ($200), and two third prize winners ($100 each).




Important dates

  • Paper submission: April 7, 2016 (extended)
  • Notification of acceptance: April 24th, 2016
  • Camera-ready paper: April 30th, 2016
  • Workshop: May 16th-17th, 2016

Topics of Interest

Topics of the workshop include, but are not limited to, the following.

1. Architectures
  • Big Data Analytics Platforms
  • Cloud Computing for Big Data Analytics
  • Heterogeneous Cloud Platform with GPU, APU, FPGA
  • Dynamic resource provisioning
  • HPC and Big Data
  • Big data storage architecture
2. Programming Models
  • New scalable programming models for big data analytics
  • Spark / MapReduce Performance characterization and optimization
  • Spark / MapReduce on Heterogeneous computing systems
  • Extensions of Spark/MapReduce
  • Debugging and Performance Tools
3. Algorithms
  • Machine Learning algorithms
  • Data Mining algorithms
  • Statistical methods
  • Graph algorithms
  • Big data querying and search
4. Applications
  • Mission-critical big data analytics applications
  • Stream processing/analytics applications
  • Data-intensive applications using Spark/MapReduce
  • Real-time applications
  • Image and video analytics
5. Big Data Collection and Aggregation
  • Big data collection in IoT/sensor networks
  • Big data processing schemes in IoT/sensor networks
  • Mobile and cloud support for IoT/sensor networks
  • Security and privacy in IoT/sensor networks
  • Energy efficiency of IoT/sensor networks
6. HPC and Big Data
  • Convergence of HPC and Big Data Frameworks
  • HPC programming models for Big Data Applications
  • Performance optimizations for Big Data Systems and Applications
  • Performance Modeling for Big Data Computing
  • Scientific Computing with Big Data
  • HPC and Big Data Education