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 Computing, 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 International Symposium on Big Data Computing (BDC) 2015 -- held in conjunction with 8th IEEE/ACM International Conference on Utility and Cloud Computing (UCC) 2015, December 7-10, 2015, St. Raphael Resort, Limassol, Cyprus, aims at bringing together international researchers, developers, policy makers, and users and to provide an international forum to present leading research activities, technical solutions, and results on a broad range of topics related to Big Data Computing paradigms, platforms and their applications. The conference features keynotes, technical presentations, posters, and workshops. 

Topics

Topics of interest include, but are not limited to:

I. Big Data Science
• Analytics
• Algorithms for Big Data
• Energy-efficient Algorithms
• Big Data Search
• Big Data Acquisition, Integration, Cleaning, and Best Practices
• Visualization of Big Data

II. Big Data Infrastructures and Platforms
• Programming Systems
• Cyber-Infrastructure
• Performance evaluation
• Fault tolerance and reliability
• I/O and Data management
• Storage Systems (including file systems, NoSQL, and RDBMS)
• Resource management
• Many-Task Computing
• Many-core computing and accelerators

III. Big Data Security and Policy
• Management Policies
• Data Privacy
• Data Security
• Big Data Archival and Preservation
• Big Data Provenance

IV. Big Data Applications
• Scientific application cases studies on Cloud infrastructure
• Big Data Applications at Scale
• Experience Papers with Big Data Application Deployments
• Data streaming applications
• Big Data in Social Networks
• Healthcare Applications
• Enterprise Applications