SC-BDA 2012 - 2012 IEEE International Workshop on Scalable Computing for Big Data Analytics (SC-BDA)

Datasets are growing bigger and bigger. The research community and enterprises can easily produce voluminous amount of data. As the amount of data available keep increasing, the ability to compute large data sets and to scale up/down dynamically becomes increasingly important. Scalable Computing is to address the large scale computing to handle high-throughput and data-intensive computing. It requires advanced parallel and distributed computing technologies such as GPGPU, in-memory, in-database, Hadoop, cloud computing to provide highly scalable and efficient solutions for many scientific and engineering problems.
The IEEE International Workshop on Scale Computing for big data analytics aims to investigate the possible solutions and advance enabling technologies to solve complex data analytic problems. The theme of this year is data value chain as services. Data value chain involves a variety of the processes including data collection, management, storage, share, integration, processing, analyzing, and visualization. We cordially invite high quality publication to discuss the research issues not limited to parallel and distributed computing systems, supercomputing, high-throughput data processing, but also multidisciplinary research on data processing, scientific analytics, engineering simulations, as well as economic-related issues to enforce the eco-system as a whole value chain for data analytics and services.
Topic of Interests includes:
? Cloud systems for data analytics
? Multi-core and accelerator-based computing
? Autonomic computing and cyberinfrastructure
? Peer-to-peer computing
? Workflow management and optimization
? MapReduce programming and Hadoop
? High-throughput data-intensive computing and communication
? Scientific data analytic applications and experience
? Scalable data mining algorithms
? Programming models
? Scheduling, resource management, and fault tolerance
? Performance modeling and evaluation
? Economic-based models and approaches
? Data privacy and integrity