A Human-Centric Approach to Group-Based Context-Awareness

The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems

Optimizing the Performance and Robustness of Type-2 Fuzzy Group Nearest-Neighbor Queries

In Group Nearest-Neighbor (GNN) queries, the goal is to find one or more points of interest with minimum sum of distance to the current location of mobile users. The classic forms of GNN use Euclidean distance measure which is not sufficient to capture other essential distance perceptions of human and the inherent uncertainty of it. To overcome this problem, an improved distance model can be used which is based on a richer, closer to real-world type-2 fuzzy logic distance model. However, large search spaces as well as the need for higher-order uncertainty management will increase the response times of such GNN queries. In this paper two fuzzy clustering methods combined with spatial tessellation are exploited to reduce the search space. Extensive evaluation of the proposed method shows improved response times compared to naïve method while maintaining a high quality of approximation. The proposed uncertainty management method also provides robustness to movement of mobile users, eliminating the need for full re-computation of candidate clusters when the locations of group members are changed

GREST : a type-2 fuzzy distance model for group nearest-neighbor queries

Collaborative and group-based queries aim to find one or more points in a search space which have the minimum aggregate distance to all members of a group, situated at a set of query points. Current approaches like Group Nearest-Neighbor (GNN) queries are based on single-measure models of distance, like Euclidean distance. In reality, human has a multi-measure perception of distance so that spatial, temporal and economical aspects are important to people with possibly different individual preferences. Current approaches to GNN are unable to handle such distance measures, since it depends on the perceptions and preferences of the users. In this study, we focus on the role of users, as members of a group, situated at GNN query points. An enriched model of distance is introduced which takes the advantage of interval type-2 fuzzy sets to cope with high-order distance uncertainties, emerged from different perceptions of distance by users, and their different preferences. The flexibility of this aggregate model in handling uncertainty enables every member of the group to use a set of group-defined words to express his/her perception of multiple distance types, and to use words instead of numeric values to set the weights for each distance type according to his/her preferences. Our experimental evaluations show that the query results are closer to group preferences by providing higher quality of consensus, while keeping the spatial dispersion of the top-k results at a small level, and improved performance with reasonable response time. The proposed distance model also provides more robustness to changes of mobile member locations, eliminating unnecessary repeated computations