With the extensive use of information technology in AAL communication systems, a data management model has recently embodied the 3-V characteristics of big data: volume, velocity, and variety. A lot of work has been done on volume and velocity, but not as much has been reported on variety. To handle the variety of data, universal solutions with acceptable performance are usually much more cost effective than customized solutions. To achieve universality, a basic idea is to first define a universal abstraction that covers a wide range of data types, and then build a universal system for universal abstraction.
Traditional database management systems commonly use a multidimensional data type, or feature vectors, as a universal abstraction. However, many new data types in AAL systems cannot be abstracted into multidimensional space. To find a more universal data abstraction and build more universal systems, we propose the concept of big data abstraction, with metric space as a universal abstraction for AAL data types. Furthermore, to demonstrate how metric-space data abstraction works, we survey the state of the art in metric space indexing, a fundamental task in data management. Finally, open research issues are discussed.