[hpc-announce] Final Call for Poster and Demo: IEEE DSDIS 2015 (Data Science and Data Intensive Systems)

Andrew Clashe andrew.clashe at gmail.com
Tue Sep 29 04:51:03 CDT 2015

Final Call for Poster and Demo:

The 2015 IEEE International Conference on Data Science and Data Intensive
Systems (DSDIS 2015), 11-13 Dec. 2015, Sydney, Australia.

Website: http://www.swinflow.org/confs/dsdis2015/demo.htm

Key dates:
*Deadline for proceedings published posters/demos: 3 October 2015*
Notification of Acceptance: 7 October 2015
Final versions of proceeding published posters/demos: 15 October 2015

Please email your posters/demos to confs.aus at gmail.com with the
email subject as "DSDIS 2015 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 Internet of Things 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 the
conference. DSDIS 2015 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 Internet of Things 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
Topics of particular interest include, but are not limited to:
• 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 Intensive Systems
Topics of particular interest include, but are not limited to:
• 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 and protection of sensitive data in collaborative environments
• 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 big
sensing/surveillance/transport data, big document/accounting data, big
online transaction data analysis and etc.

Deepak Puthal, University of Technology Sydney, Australia
Rajiv Ranjan, CSIRO, Australia
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