HPPD-DM 2016 - Special Session on High Performance Parallel and Distributed Data Mining (HPPD-DM 2016)

The HPPD-DM special session focuses on the issues of high performance, distributed and parallel computation in the process of knowledge discovery from large databases. Theoretical advances, algorithm and systems, as well as application cases are welcomed contributions.
Over the years the definition of high performance computing evolved according to the opportunities provided by new technologies and to the needs of the emerging industrial and scientific applications. High performance computing research topics range from traditional parallel and distributed algorithms, to modern multi-core CPU architectures, streaming GPUs, cloud computing, etc. Nowadays, high performance computing is a necessary support for Big Data and the analysis of very large volumes of possibly distributed data, such as those generated by scientific applications, Web x.0 services and IoT based.
The HPPD-DM special session aims at presenting new and original contributions focusing on the application of high performance parallel and distributed data mining, including distributed, parallel, P2P and data intensive algorithms and systems. We invite papers tackling the performance issues of data mining algorithm at all levels of the system architecture: I/O, memory bottlenecks, processor-level parallelism as well as distributed/parallel computation. All application areas are welcomed.
The HPPD-DM Session topics include (but are not limited to) the following:
Energy-efficient distributed data mining algorithms and systems
Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining and pre-processing and post-processing tasks
Data mining exploiting multi-core CPUs or GPUs
Scalable algorithms & architectures for Machine learning over structured, semi-structured, spatio-temporal, graph, and streaming data
Grid-based and cluster-based data mining algorithms and systems
Data mining on Clouds
Data Mining for energy-aware Cloud management
Data Mining exploiting Map-Reduce paradigm
Distributed techniques for incremental, exploratory and interactive mining
Distributed techniques for security, privacy preserving data mining
Peer-to-Peer Data Mining
Data Mining in Social Networks and Social Media
High performance data stream mining and management
Resource- and location-aware mining algorithms
Data mining in mobile environments
Theoretical foundations for resource-aware mining in a mobile, streaming and/or distributed environments
Advances in data mining over multimedia data
Parallel or distributed frameworks for stream management, KDD systems, and parallel or distributed mining
Web Mining, Information and Knowledge Extraction
Applications of parallel and distributed data mining in business, science, engineering, medicine, and other disciplines.