A lazy evaluation approach for mobile reasoning in DSSs

Recently, a new mobile generation of decision support systems (DSSs) is appearing to face a set of new challenging scenarios, where information must be used anywhere for supporting the decision-making tasks seamlessly and ubiquitously. In this respect, this paper presents a lazy evaluation approach for reasoning in mobile knowledge-based DSSs in order to grant an efficient handling of memory and computational resources. The approach relies on knowledge representation and reasoning facilities to face and efficiently reason on the continuous and real-time flow of data. The core of the approach is a lazy pattern matching algorithm, specifically designed and implemented as a light-weight solution suitable for resource-limited mobile devices with the final aim of improving performance in real-time and intensive applications.