Final 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 HST) Notification of Acceptance: 7 October 2016 Final versions of proceeding published posters/demos: 15 October 2016 Submission Please email your posters/demos to confs.aus@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. ====== Introduction 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 analysis • Big Data, data science and cloud computing • Innovative applications in business, finance, industry and government cases 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. Chairs: Mianxiong Dong, Muroran Institute of Technology, Japan William Liu, Auckland University of Technology, New Zealand