Data aggregation processes: a survey, a taxonomy, and design guidelines

Abstract

Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things systems, many with timing constraints. Understanding the common and variable features of data aggregation processes, especially their implications to the time-related properties, is key to improving the quality of the designed system and reduce design effort. In this paper, we present a survey of data aggregation processes in a variety of application domains from literature. We investigate their common and variable features, which serves as the basis of our previously proposed taxonomy called DAGGTAX. By studying the implications of the DAGGTAX features, we formulate a set of constraints to be satisfied during design, which helps to check the correctness of the specifications and reduce the design space. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also serves as the foundation of a design tool which facilitates the model-driven design of data aggregation processes.