Workshop on Parallel and Distributed Computing for Big Data Applications, Paris, Frankreich

Abbrevation

WPBA

Link

http://wpba2014.dc.ufscar.br/datas.html

City

Paris

Country

France

Deadline Paper

2014-09-01

Start Date

2014-10-22

End Date

2014-10-24

Abstract

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. 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