A case study of applying data mining to sensor data for contextual requirements analysis

Abstract

Determining the context situations specific to contextual requirements is challenging, particularly for environments that are largely unobservable by system designers (e.g., dangerous system contexts of use and mobile applications). In this paper, we describe the application of data mining techniques in a case study of identifying contextual requirements for a context-aware mobile application to be used by a team of four long-distance rowers. The context of use for this application was dangerous and isolated, making it unobservable by the developers. The context situations for five mobile application requirements were defined by using a data mining algorithm applied to historical sensor data passively collected by the users while they crossed the Atlantic Ocean in a rowboat. The performance of the resulting classifiers is analyzed over time with promising results demonstrating that the data mining approach is feasible with implications for requirements engineering, context-aware mobile applications, and group-context-aware mobile applications.