The society, technologies, and sciences undergo a rapid and revolutionary transformation towards incorporating Artificial Intelligence in every system humans use in everyday life for creating Smart Environments (SmE) through Ambient Intelligence (AmI) in highly interconnected and collaborative scenarios. The main source and asset for making smart systems is data, produced today in extraordinary large quantities thanks to the recent advances in sensors and sensor networks, pervasive and embedded computing enhancing the capability of everyday objects and easing collaboration among people. Data from all areas of daily life that are increasingly accessible to a broad public enable the conception, creation, validation, and calibration of process and systems. However, this requires international standards for data quality and access. Mobile systems could enhance the possibilities available for designers and practitioners. Effective analysis, quality assessment, and utilization of big data is a key factor for success in many business and service domains, including the domain of smart systems. Major industrial domains are on the way to perform this tectonic shift based on Big Data, Artificial Intelligence, Collaborative Technologies, Smart Environments (SmE) supporting Virtual and Mixed Reality Applications, Multimodal Interaction and Reliable Visual and Cognitive Analytics. However, a number of requirements must be fulfilled and complexities resolved before we can effectively and efficiently turn the huge amount of generated data into information and knowledge. The first one is to ensure data quality, which includes accuracy and integrity of the obtained data, timely delivery, suitable quantity, etc. Privacy and security requirements and thorough end-to-end rights complement realization and deployment of modern design, implementation and evaluation tools. The second one is to develop models, which can turn data into valuable information and then into knowledge. Two important characteristics are desirable for regression and classification models: accuracy and interpretability. While accuracy deals with the ability of the model to predict a certain outcome, interpretability deals with the ability of the model to explain the reasons for producing a certain outcome. The aim of this workshop is to bring together researchers and practitioners working on theoretical as well as practical aspects of data generation, data processing and knowledge creation, including social issues which arise when using AI-powered systems in collaborative scenarios and smart city applications. In this fourth edition, we invite students, researchers, and users from several countries will present their work and discuss ways and opportunities for cooperation.

The organizers expect to attract submissions in categories (with duration of presentation):

Poster Session with individual presentations: 1-2 pages (10 min)

Short papers: 4-8 pages (15 + 5 min)

Full papers: 9-16 pages (25 + 5 min)