[hpc-announce] Call for Poster and Demo: IEEE DSS 2016 (Data Science and Systems)

Jesson Butt jesson.butt at gmail.com
Mon Sep 12 21:14:15 CDT 2016

Call for Poster and Demo:

The 2016 IEEE International Conference on Data Science and Systems (DSS
2016), 12-14 Dec. 2016, Sydney, Australia.

Website: http://www.swinflow.org/confs/2016/dss/poster.htm

Key dates:
Deadline for proceedings published posters/demos: 1 October 2016 (11:59pm
Notification of Acceptance: 7 October 2016
Final versions of proceeding published posters/demos: 15 October 2016

Please email your posters/demos to confs.aus at gmail.com with the email
subject as "DSS 2016 poster demo submission".

Two types of posters and demos:

1. Proceedings published posters and demos: Submission is a 2-page short
paper describing the post/demo content, research, relevance and importance
to Data Science and Systems or related topics. If accepted, the 2-page
short paper will be published in the main conference proceedings together
with regular research papers. Each accepted poster or demo must register to
the main conference with full registration.

2. Web published posters and demos: Submission is a 1-page
extended abstract. Such posters/demos will not be included in the
conference proceedings, but will be published on the conference website.

Both types of posters/demos will be displayed during the conference.


Participants are invited to submit posters and research demos to DSS 2016.
DSS 2016 (Data Science and Systems) is created to provide a prime
international forum for both researchers, industry practitioners and
environment experts to exchange the latest fundamental advances in the
state of the art and practice of Data Science and Systems as well as
joint-venture and synergic research and development across various related
areas. Topics of interest for posters and demos include, but not limited to:

Scope and Topics

A. Data Science

• Data sensing, fusion and mining
• Data representation, dimensionality reduction, processing and proactive
service layers
• Stream data processing and integration
• Data analytics and new machine learning theories and models
• Knowledge discovery from multiple information sources
• Statistical, mathematical and probabilistic modeling and theories
• Information visualization and visual data analytics
• Information retrieval and personalized recommendation
• Data provenance and graph analytics
• Parallel and distributed data storage and processing infrastructure
• MapReduce, Hadoop, Spark, scalable computing and storage platforms
• Security, privacy and data integrity in data sharing, publishing and
• Big Data, data science and cloud computing
• Innovative applications in business, finance, industry and government

B. Data Systems

• Data-intensive applications and their challenges
• Scalable computing platform such as Hadoop and Spark
• Storage and file systems
• High performance data access toolkits
• Fault tolerance, reliability, and availability
• Meta-data management
• Remote data access
• Programming models, abstractions for data intensive computing
• Compiler and runtime support
• Data capturing, management, and scheduling techniques
• Future research challenges of data intensive systems
• Performance optimization techniques
• Replication, archiving, preservation strategies
• Real-time data intensive systems
• Network support for data intensive systems
• Challenges and solutions in the era of multi/many-core platforms
• Stream data computing
• Green (Power efficient) data intensive systems
• Security, Privacy and Trust in Data
• Data intensive computing on accelerators and GPUs
• HPC system architecture, programming models and run-time systems for data
intensive applications
• Productivity tools, performance measuring and benchmark for data
intensive systems
• Big Data, cloud computing and data intensive systems
• Innovative data intensive applications such as Health, Energy,
Cybersecurity, Transport, Food, Soil and Water, Resources, Advanced
Manufacturing, Environmental Change, and etc.

Mianxiong Dong, Muroran Institute of Technology, Japan
William Liu, Auckland University of Technology, New Zealand
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