As enquired by some authors, the Call for Poster and Demo for BDSE2013 is now ready. 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: 30 September 2013 Notification of Acceptance: 2 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 Submissions: Please email your posters/demos to confs.aus@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 Chairs: Xuyun Zhang, University of Technology Sydney, Australia Chang Liu, University of Technology Sydney, Australia