BDAA 2016 - 3rd International Symposium on Big Data Principles, Architectures & Applications (BDAA 2016)

The challenge of “Big Data” continues to grow and is an active area of significant research. This track focuses on techniques, experiences, applications, and lessons learned for large-scale/big data science, data intensive, and data analytics, particularly in the realms of high performance computing that lead organizations through major transformations.
The symposium is to address, explore and exchange information on the state-of-the-art and practice in the broad multidisciplinary field of Big Data Science. Participation is extended to researchers, designers, educators and interested parties in all disciplines and specialties
The symposium aims at providing a forum to bring together researchers and scientists to share and exchange big data related research, technologies, experiences, and lessons for building various types large-scale data intensive and data analytics, with interoperability and coordination capabilities in a high performance and high availability setting.
This symposium solicits contributions that address contemporary and future challenges in big data Science, data analytics, and Data Intensive, particularly in collaboration systems, social networks and media, and information technologies.
BDAA topics include (but are not limited to) the following:
Theories and Methodologies for Big Data processing
Architectures and Design of Big Data processing systems
Distributed data-intensive computing systems
Managing large-scale big data platforms
Use of big data technologies for science (Hadoop, NoSQL/NewSQL, etc.)
Big data simulation, visualization, modeling tools, and algorithms
Big data intelligence and predictive analysis
(Analysis of dynamic data, such as those collected through sensors, etc.)
Discovery, Collection, and Extraction of information in Big Data sources
Processing of Big Data Streaming
Big Data Mining and Knowledge Discovery
Applications using big data (WEB, Bio-Data, Industrial Data, etc.)
Big data business implications ? Data culture
Experiences, Case Studies and Lessons Learned