PDDM 2019 - 7th Special Session on Parallel and Distributed Data Mining (PDDM 2019)

The PDDM special session focuses on the issues of high performance, distributed and parallel computation in the process of knowledge discovery from large databases and big data systems. Theoretical advances, algorithms 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 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 infrastructures.
The PDDM 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 algorithms 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 PDDM Special Session topics include (but are not limited to) the following:
Parallel and distributed data mining algorithms on GPUs, many-cores, and accelerators
Grid-based and cluster-based data mining algorithms and systems
Data mining on Clouds
Data mining and blockchain
Data mining exploiting the Map-Reduce paradigm
Data mining exploiting approximate computing and information granulation
Message passing parallel data mining algorithms using MPI
Scalable shared-memory parallel data mining algorithms using OpenMP
FPGA for parallel data mining applications
Middleware for high-performance data mining
Peer-to-peer data mining
Programming paradigms to support high-performance computing for data mining
Programming models, tools, and environments for high-performance data mining
Benchmarking and performance studies of high-performance data mining solutions and applications
Performance models for high-performance data mining applications and infrastructures
Optimization techniques for data management in high-performance computing for data mining
Energy-efficient distributed data mining algorithms and systems
Scalable algorithms for deep learning and machine learning
Parallel and distributed data mining exploiting simulations
Distributed mining of complex and massive data streams
Distributed techniques for security and privacy preserving data mining
Data mining in social networks and social media
Data mining in mobile and multimedia environments
Data mining aspects of scalable artificial intelligence applications
Web mining and scalable information retrieval
Applications of parallel data mining in business, science, engineering, medicine, and other disciplines.