Pervasive Data Science and AI

Recent years have seen an explosion in the use of data science and AI as a central tenant in numerous computing applications, products, research, and innovation. Examples of the success of data science abound—applying new machine-learning techniques to problems such as vision and speech recognition and translation has achieved commonplace levels of performance that would have seemed impossible a few years ago. In parallel, developments in pervasive computing increasingly enable us to instrument our physical environment with complex sensors and actuators and create an interconnected world that generates huge volumes of data. The importance of these trends can be seen in the growing momentum of exemplars such as the Internet of Things (IoT), smart environments, and augmented cognition. Pervasive data science is characterized by a focus on the collection, analysis (inference), and use of data (actuation) in pursuit of the vision of ubiquitous computing1 and raises multiple new challenges, demanding new approaches to how we capture, process, and use data in pervasive environments. Beyond the hype, it is clear that our world is becoming increasingly data centric, in which both physical and electronic services depend on the collection, analysis, and application of large volumes of heterogeneous data. In this Special Issue, we present a series of articles that cover different aspects of the exciting work that is currently carried out in this area.