3rd IEEE/ACM International Conference on Big Data Science, Engineering and Applications, Shanghai, China

Abbrevation

IEEE/ACM BDSEA

Link

http://computing.derby.ac.uk/bdsea2016/

City

Shanghai

Country

China

Deadline Paper

2016-07-30

Start Date

2016-12-06

End Date

2016-12-09

Abstract

Rapid advances in digital sensors, networks, storage, and computation along with their availability at low cost is leading to the creation of huge collections of data — dubbed as Big Data. This data has the potential for enabling new insights that can change the way business, science, and governments deliver services to their consumers and can impact society as a whole. This has led to the emergence of the Big Data Computing paradigm focusing on sensing, collection, storage, management and analysis of data from variety of sources to enable new value and insights. To realize the full potential of Big Data, we need to address several challenges and develop suitable conceptual and technological solutions for dealing them. These include life–cycle management of data, large–scale storage, flexible processing infrastructure, data modelling, scalable machine learning and data analysis algorithms, techniques for sampling and making trade–off between data processing time and accuracy, and dealing with privacy and ethical issues involved in data sensing, storage, processing, and actions. The IEEE/ACM International Conference on Big Data Science, Engineering, and Applications (BDSEA) is an annual international conference series. The first two events were held in London (BDC 2014) and Cyprus (BDC 2015) respectively. In 2016, the conference has been expanded to explicitly include application and renamed as BDSEA 2016. The conference series aims to provide a platform for researchers to present their new discoveries, developments, results, as well as the latest trends in big data computing and applications. BDSEA 2016 will be held in conjunction with the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016) at Tongji University, Shanghai, China. Authors are invited to submit original unpublished manuscripts on a broad range of topics related to big data science, computing paradigms, platforms and applications. Topics Topics of interest include, but are not limited to: I. Big Data Science Big Data Analytics Innovative Data Science Models and Approaches Data Science Practice and Experience Algorithms for Big Data Novel Big Data Search Techniques Innovative data and Knowledge Engineering approaches Data Mining and Knowledge Discovery Approaches for Big Data Big Data Acquisition, Integration, Cleaning, and Best Practices Experience reports in Solving Large Scale Data Science Problems II. Big Data Infrastructures and Platforms Scalable computing models, theories, and algorithms In–Memory Systems and platforms for Big Data Analytics Programming Systems for Big Data Cyber–Infrastructures for Big Data Performance evaluation reports for Big Data Systems Fault tolerance and reliability of Big Data Systems I/O and Data management Approaches for Big Data Energy–efficient Algorithms Storage Systems (including file systems, NoSQL, and RDBMS) Resource management Approaches for Big Data Systems Many–Task Computing Many–core computing and accelerators III. Big Data Security and Policy Big Data Archival and Preservation Big Data Management Policies Data Privacy Data Security Big Data Provenance Ethical and Anonymization Issues for Big Data Big Data Compliance and Governance Models IV. Big Data Applications Experience Papers with Big Data Application Deployments Big Data Applications for Internet of things Scientific application cases studies on Cloud infrastructure Big Data Applications at Scale Data streaming applications Mobile Applications of Big Data Big Data in Social Networks Healthcare Applications such as Genome processing and analytics Enterprise Applications V. Visualization of Big Data Visual Analytics Algorithms and Foundations Graph and Context Models for Visualization Analytical Reasoning and Sense–making on Big Data Visual Representation and Interaction Big Data Transformation, and Presentation