A great deal of research efforts has been invested in temporal aspects of big data management during last years, with alternate fortune. This line of research aims at capturing, formally modeling and successfully exploiting all the time-dependent characteristics of the fundamental big data model ranging from state model to query model. Temporal big data management thus poses novel research challenges and exciting directions to be followed, and a first critical result is represented by recognizing that traditional time-focused models, techniques and algorithms developed in previous years are not suitable to deal with novel characteristics of big data, mainly due to volume, heterogeneity and scalability issues. Inspired by these considerations, in this paper we provide a comprehensive overview of state-of-the-art temporal big data management proposals, and criticisms on benefits and limitations of these initiatives. We complement our contributions with a deep discussion on future research directions in this area.
