HPDA 2014 - International Workshop on High Performance Data Analysis

Prof. Fuad Aleskerov, alesk-AT-hse.ru (HSE, Moscow, Russia)
Prof. Vassil Alexandrov, vassil.alexandrov-AT-bsc.es (ICREA Research Professor in Computational Science at Barcelona Supercomputing Centre, Spain)
Associate Prof. Ying Liu, yingliu-AT-ucas.ac.cn (University of Chinese Academy of Sciences, China)
Big data is an emerging and active research topic in recent years. There is a clear need to analyze huge amounts of unstructured and structured complex data, historic data as well as data coming from real time feeds (e.g. Business data, meteorological ones from sensors, etc.). This is beyond the capability of traditional data processing techniques and tools. The challenges include data capture, storage, search, sharing, transfer, analysis, and visualization. In order to meet the requirement of big data analysis, Computational science and high performance computing methods and algorithms are in real demand to solve the above challenges, including scalable mathematical methods and algorithms, parallel and distributed computing, cloud computing, etc. This workshop will focus on the issues of high performance data analysis. Theoretical advances, mathematical methods, algorithms and systems, as well as diverse application areas will be in the focus of the workshop.
This year the workshop aims at organizing a special theme session exploring emerging trends in high performance data analysis. We welcome papers on all aspects of high performance data analysis, including, but not limited to:
1) Data processing exploiting hybrid architectures and accelerators (multi/many-core, CUDA-enabled GPUs, FPGAs)
2) Data processing exploiting dedicated HPC machines and clusters
3) Data processing exploiting cloud
4) High performance data-stream mining and management
5) Efficient, scalable, parallel/distributed data mining methods and algorithms for diverse applications
6) Advanced methods and algorithms for big data visualization
7) Parallel and distributed KDD frameworks and systems
8) Theoretical foundations and mathematical methods for mining data streams in parallel/distributed environments
9) Applications of parallel and distributed data mining in diverse application areas such as business, science, engineering, medicine, and other disciplines