Editorial special issue on data analytics for engineering, science and society

This special issue introduces original contributions aiming to make data analytics models and technique accessible to people and useful to address engineering, scientific and societal challenges. Each contribution included in the special issue describes interesting research activities developed in the field of applied data science innovation. These contributions have a wide variety of perspectives including methodological aspects, theoretical and technological views. The special issue covers a wide range of interesting topics categorised into: (1) solutions addressing issues related to exploit/deploy data science technologies such as load scheduling in distributed environments (Bhatia et al.), quality of service (Haytamy et al.), data retrieval strategy (Andrejev et al.), reduction of data transmissions in sensor networks (Kniess and Oliviera), privacy in the data analytics workflow (Belhajjame et al.); (2) innovative analytics algorithms towards interpretable models proposed to add more transparency in some application scenarios such as Healthcare (Fawagreh and Gaber), electric distribution networks (Renga et al.), quantitative stock trading (Cagliero et al.); (3) characterizing user behavior and the surrounding environments as discussed by Chen and Golab in the context of micro-journal mining to understand environment stimuli. Boratto et al. present a strategy to model user behavior in journey planning, while Castells-Graells et al. discuss how to address cyclings’ urban safety. Finally, Jabbour et al. present a methodology to easily detect overlapping community in networks.