Improving Context-Awareness in Situation-Aware Smart Software and Self-Adaptive Systems

Start Date: Feb-2012   End Date: Feb-2013
Members: Norha M. Villegas (project leader), Gabriel Tamura (co-researcher)

Main Objective

This project focuses on the investigation of models and mechanisms to represent, acquire, provide, and reason about context information to improve context-awareness in user-centric situation-aware smart software (SASS) systems.


Situation-aware Smart software (SASS) systems are software systems whose requirements may change continuously and are highly affected by dynamic context information. That is, the satisfaction of their requirements depends on context situations that change at runtime, and generally cannot be anticipated at design time. Situation-awareness refers to the capability of a system to gather and process information from its environment to understand the situation of external and internal entities that can affect the system in the accomplishment of its goals.
We have explored the application of dynamic context management techniques to the improvement of accuracy in recommender systems. Particularly, we applied our solution to the improvement of the relevance of product and service offers delivered to users by daily-deal applications such as Groupon in North America ( Our results are promising. A preliminary exploration demonstrated that for many deal categories the accuracy is between 3% and 8% better than the approaches we used as baselines. For some categories, and in terms of multiplicative relative performance, deal recommendations based on context information outperforms related approaches by as much as 173.4%, and 37.5% on average [1]. This can obviously have a tremendous impact on the generation of value and revenue in e-commerce business models. We want to explore also the possibility of creating a spin off in the future from the results of this research.
With respect to self-adaptive systems the project focuses on the investigation of the application of dynamic context management techniques to the improvement of adaptation mechanisms. In particular, we are interested in modeling and managing adaptive systems’ viability zones as a set of relevant context attributes and corresponding desired values that must be monitored at runtime. Viability zones in self-adaptive systems correspond to the set of states where the system’s requirements and desired properties (i.e., adaptation goals) are satisfied [2].

[1] S. Ebrahimi, N. M. Villegas, H. A. Müller, and A. Thomo. SmarterDeals: A Context -aware Deal Recommendation
System based on the SmarterContext Engine. In Proceedings of CASCON 2012, Markham, ON, Canada, 2012. IBM Corp.
To appear.
[2] G. Tamura, N. M. Villegas, H. A. Müller, J. P. Sousa, B. Becker, M. Pezzè, G. Karsai, S. Mankovskii, W. Sch äfer, L.
Tahvildari, and K. Wong. Towards Practical Runtime Veri_cation and Validation of Self-Adaptive Software Systems,
volume 7475 of LNCS, pages 108-132. Springer, 2012.


Icesi University