In the last few years, the use of Information and Communication Technologies has made available a huge amount of heterogeneous data in various real application domains. For example, in the urban scenario Internet of Things (IoT) systems capture massive data collections describing the overall urban environment as well citizen exploitation and perception of available services. In health care systems, electronic health records allow storing a variety of information about patients as adopted treatments and monitored physiological conditions, while Internet of Medical Things (IoMT) ensures the availability and processing of healthcare data through smart medical devices and web. Moreover, in most domains, individual plays a crucial role generating data on the one side, but also driving a user and context aware analysis process, and finally demanding an easily accessible and understandable knowledge at the end of the process.

Digging deep these data collections can unearth a rich spectrum of knowledge in the targeted domain valuable to characterize user behaviors, identify weaknesses and strengths, improve the quality of provided services or even devise new ones. However, data analytics on these data collections is still a daunting task, because they are generally too big and heterogeneous to be processed through data analysis techniques currently available. Consequently, various challenges about data science arise dealing with creation, storage, search, sharing, modeling, analysis, and visualization of data, information, and knowledge.

Suitable data fusion techniques and data representation paradigms should be devised to integrate the heterogeneous collected data into a unified representation describing all facets of the targeted domain. Moreover, huge volume of data demands the definition of novel data analytics strategies also exploiting recent analysis paradigms and cloud based platforms as Hadoop and Spark. Proper strategies can also be devised for data and knowledge visualization, possibly also involving user interactive interfaces.

The aim of the workshop is to allow academics and practitioners from various research areas to share their experiences on designing cutting-edge analytics solutions for real-life applications. Researchers are encouraged to submit their work-in-progress research activity describing innovative methodologies, algorithms, platforms addressing all facets of a data analytics process providing interesting and useful services.

Industrial implementations of data analytics applications, design and deployment experience reports on various issues raising data analytics projects are particularly welcome. We call for research and experience papers as well as demonstration proposals covering any aspect of data analytics solutions for real-life applications.