The focus on work processes in process science is increasingly shifting from a case-centric, pure control-flow perspective, to a data-aware setting where (possibly multiple interrelated) objects and their properties are updated by and influence the process. Data may range from simple attributes (such as strings or numbers) to complex data structures like relational databases. Modelling, analysis, and mining of such data-aware processes call for formalisms and techniques that simultaneously tackle time/dynamics and the interplay with these different forms of data. In this short paper, we focus on data-aware work processes whose underlying control-flow backbone is described as a Petri net. We overview the main modelling requirements and constructs emerging in different proposals, ranging from case- to object-centric processes. We then summarize how artificial intelligence techniques from automated reasoning have been employed and further developed to obtain foundational and practical results in data-aware process analysis and mining.