BD2M 2016 - 2nd International Workshop on Big Data and Data Mining Challenges on IoT and Pervasive Systems

Data analytics and data mining have become strategic components for today's businesses. Manipulating large volumes of data, integrating information from different sources and formats and extracting useful insights from it has only been possible with the development of new techniques and tools, nowadays known under the Big Data appellation. The upcoming of the Internet of Things (IoT) not only represents an additional volume of data to explore and the need for more efficient and scalable data analysis methods, but also raises additional challenges on data management, distribution, storage and their integration into the enterprises’ information systems. Such information systems have to evolve and become Pervasive Information System thanks to this new ubiquitous environment made available by IoT. Hence, IoT opens new perspectives for the enterprises that will be able to manage and extract information from these pervasive systems.
This workshop aims at gathering researchers interested in the big data and data mining challenges for pervasive systems and IoT environments. We welcome original unpublished research papers (as well as practical experience reports) related to topics of the workshop, which include but are not limited to the following:
Data-intensive computing on hybrid infrastructures (e.g., clusters, clouds, grids, P2P)
Data streams mining techniques
Data streams processing systems and real-time decision support
Data analytics on pervasive grids
Heterogeneous Source Mining
Algorithms for big data analytics and data mining on pervasive environments
Mining and recommendation techniques for pervasive environments
Sampling for Big Data
Big data analytics and mining applications for pervasive systems
Big data analytics applied to Smart Cities
Data mining techniques for sensor data
Programming models, including MapReduce, extensions, and new models applied to pervasive environments
Scalability and elasticity in big data environments
Fault-tolerance and reliability in big data environments
Performance analysis of big data tools and applications in pervasive environments
Software-defined networks for big data analytics and data mining
Scheduling and resource management in big data environments
Challenges in big data storage and processing
Big data tools, services, and infrastructures on clouds