[hpc-announce] Final Call for Demo and Poster - BDSE2013 (Big Data Science and Engineering), 3-5 Dec. 2013, Sydney, Australia

Chang Liu changliu.aus at gmail.com
Tue Oct 1 10:43:03 CDT 2013

Final Call for Poster and Demo:

The 2nd IEEE International Conference on Big Data Science and Engineering
(BDSE2013), 3-5 December 2013, Sydney, Australia.

Website: http://www.swinflow.org/confs/bdds2013/demo.htm

Important Dates:
Deadline for proceedings published posters/demos with display at
conference: 5 October 2013
Notification of Acceptance: 7 October 2013
Final versions of proceeding published posters/demos: 15 October 2013
Deadline for web published posters/demos with display at conference: 30
October 2013

Please email your posters/demos to confs.aus at gmail.com with the email
subject as "BDSE2013 demo-poster submission".

Two types of posters and demos are welcome. Both of them will be displayed
during the conference.

1. Proceedings published posters and demos: Submission is a 2-page short
paper describing the post/demo content, research, relevance and importance
to Big Data Science and Engineering community. If accepted, the 2-page
short paper will be published in the main conference proceedings.

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.

Participants are invited to submit posters and research demos to BDSE2013.
BDSE2013 (Big Data Science and Engineering) 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 Big Data 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:

· Big Data theory, applications and challenges
· Recent development in Big Data and MapReduce
· Big Data mining and analytics
· Big Data Infrastructure and Cloud Computing
· Big Data visualization
· Large data stream processing on cloud
· Large incremental datasets on cloud
· Distributed and federated datasets
· NoSQL data stores and DB scalability
· Big Data sharing and privacy preserving
· Security, trust and risk in Big Data
· Big Data placement, scheduling, and optimization
· Extension of the MapReduce programming model
· Distributed file systems for Big Data
· MapReduce for Big Data processing, resource scheduling and SLA
· MapReduce on heterogeneous distributed environments
· Performance characterization, evaluation and optimization
· Simulation and debugging of MapReduce and Big Data systems and tools
· Volume, Velocity, Variety, Value and Veracity of Big Data
· Multiple source data processing and integration with MapReduce
· Storage and computation management of Big Data
· Large-scale scientific workflow in support of Big Data processing
· Algorithms and theory for distributed systems
· Data management and distributed data systems
· Security, privacy, fault tolerance and reliability in distributed systems
· Distributed ad hoc, ubiquitous and pervasive systems
· Mobile systems and development for handheld devices such as mobile phones
· Distributed system architectures and software such as runtime systems,
multicore programming languages, performance modelling and evaluation,
programming environments and tools, and etc.
· Distributed computing applications such as management of big data,
scientific applications, social media applications, web applications and
mobile computing

Xuyun Zhang, University of Technology Sydney, Australia
Chang Liu, University of Technology Sydney, Australia
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