WPBA 2014 - Workshop on Parallel and Distributed Computing for Big Data Applications

Nowadays, the abundance of data is changing from the way companies make business to the way governments take many decisions, from the way science is made in several knowledge areas to the way many individuals take daily decisions such as where to go or how to buy. During the last decade, tools and techniques emerged to support massive offline analysis of web scale datasets on many thousands of computers working as a single facility. However, the total amount of digital data being produced, stored, and transmitted around the world is growing exponentially. The wide diversity of data sources and formats (data variety) cannot be handled by traditional systems and techniques, raising new data management challenges. In many areas, applications need to collect data and produce answers with high frequency or low latency, e.g. to raise some alarm or take a decision within a few milliseconds. Furthermore, in scalable environments with hundreds or thousands of components, surviving to frequent failures is mandatory. Analytic processing and knowledge discovery in such scenarios demand scalable and efficient algorithms, able to handle the complexity and variety of data even under specific constraints (e.g., energy consumption, available memory, computational power, and networking capacity). Furthermore, sensor networks and the Internet-of-Things open new perspectives in terms of the amount and complexity of data to be managed. Learn more.
Topics of interests include, but are not limited to novel techniques, algorithms, and tools for collecting, storage, processing, mining and analysis of low latency big data in reliable and scalable computing environments:
Scalability and elasticity in big data environments
Fault-tolerance in big data environments
Security and privacy in big data environments
Reliability in big data environments
Data streams processing techniques and systems
Complex event processing
Big data applications
Energy efficiency and big data
Scientific workflows for big data
Programming models, including MapReduce, extensions, and new models
Algorithms for big data analytics and data mining
Management of big data on the cloud
Big data tools, services, and infrastructures on clouds
HPC clouds for big data
Performance analysis of big data environments and applications
Big data benchmarks
Challenges in big data storage and processing
Scheduling and resource management in big data environments
Large data stream processing systems and infrastructures
Data-intensive computing on hybrid infrastructures (e.g., clusters, clouds, grids, P2P)
Implementation and optimizations for heterogeneous architectures
Implementation and optimizations for specialized architectures
Performance evaluation and optimization
Accepted papers will be published by the ACM International Conference Proceedings Series - ICPS and disseminated through electronic channels, specifically, the ACM Digital Library (approval pending).
Best papers published and presented at the workshop may be invited to submit an extended version for publication in an good quality International Journal (approval pending).