Algorithms to Resolve Conflict in Multiuser Context Aware Ubiquitous Environment

Algorithms to Resolve Conflict in Multiuser Context Aware Ubiquitous Environment

Thyagaraju G.S., U.P. Kulkarni
DOI: 10.4018/japuc.2012070103
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Conflict resolution in context-aware computing is getting more significant attention from researchers as pervasive/ubiquitous computing environments take into account multiple users and multiple applications. In multi-user ubiquitous computing environments, conflicts among user’s contexts need to be detected and resolved. Conflicts arise when multiple users try to access or try to have a control on an application. In this paper, the authors propose a series of algorithms to resolve conflict which can be embedded in different context aware applications like context aware devices (say TV, Mobile, AC, and Fan) and Context Aware Ambient (like Meeting Room, Living Room, Restaurant, Coffee Shop, etc.). The algorithms discussed in this paper make use of different tools like Probability, Fuzzy Logic, Bayesian Network and Rough set theory. In addition the algorithms utilize various factors like social, personal and environmental. The motto of this paper is to enable context aware applications to offer socialized and personalized services to multiple users by resolving service conflicts among users.
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Most of the conflict resolving systems use Collaborative Filtering or Content-based methods or hybrid conflict resolving methods to predict new items of interest for a user (Herlocker, Konstan, & Ried, 2002; Feng, Xian, & Feng, 2004; Rong & Bin, 2007). The system called Tapestry is often associated with the genesis of computer-based conflict resolving, recommendation, and collaborative filtering systems. In Tapestry (Goldberg et al., 1992), users were able to annotate documents with arbitrary text comments and other users could then query based on the comments of other users. The key attribute of this system is that it allowed recommendations to be generated based on a synthesis of the input from many other users. Making recommendations based on the opinions of like minded users rather than filtering items based on content has become known as collaborative filtering. The collaborative filtering paradigm which began with Tapestry was later automated in a number of projects (Resnick & Varian, 1997; Balabanovic & Shoham, 1997; Karypis, 2001; Herlocker et al., 2004; Wang et al., 2007).

The main advantage of collaborative filtering is the ability to make serendipitous recommendations (Herlocker et al., 2004). Most systems use a notion of inter –user distance and thus can define “neighbors” for a user. If an item of a particular genre is highly preferred by user’s neighbor, then that item could be recommended even if the recommendee has no previous experience with items of that genre.

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