Segmentation techniques for the summarization of individual mobility data

Abstract

Segmentation techniques partition a sequence of data points into a series of disjoint subsequences—segments—based on some criteria. Depending on the context and the nature of data themselves, segments return an approximate representation. The final result is a summarized representation of the sequence. This intuitive mechanism has been extensively studied, for example, for the summarization of time series in order to preserve the ‘shape’ of the sequence while omitting irrelevant details. This survey focuses on the use of segmentation methods for extracting behavioral information from individual mobility data, in particular from spatial trajectories. Such information can then be given a compact representation in the form of summarized trajectories, e.g., semantic trajectories and symbolic trajectories. Two major streams of research are discussed, spanning computational geometry and data mining respectively, that are emblematic of the multiplicity of views. WIREs Data Mining Knowl Discov 2017, 7:e1214. doi: 10.1002/widm.1214This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Structure Discovery and Clustering