Nikos Kalatzis
ABSTRACT
Pervasive computing systems need to be strongly proactive. Context-awareness contributes to this, thus minimizing human-machine interaction. Context-aware systems are greatly enhanced by the utilization of recorded history of the users' situations and interactions. In this paper, an approach is proposed for modelling, storing and exploiting history-of-context, in order to predict or estimate context information. The proposed framework is context-type-independent, requires minimal processing and storage resources, and can be used for data compression. It is based on multiple context prediction rule generation models, demonstrates high prediction success ratio, and has been empirically evaluated via extensive experiments.
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Index Terms
- User-centric inference based on history of context data in pervasive environments
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Reviews
Amos O Olagunju
Intelligent pervasive computing systems (IPCS) are useful for forecasting the situational information of users, such as their daily routine, and activities at any time or location. The literature discusses the architecture of an IPCS equipped with situation-aware agents for predicting and alerting the flow of debris disasters [1]. Yet, the design and implementation of proactive IPCS for user-centered information inference by means of historical context data (HCD) pose difficult questions. How should an IPCS dynamically detect and adapt to the environments, resources, and preferences of users__?__ How should the principal context parameter be targeted for defining the prediction rules of context estimation__?__ Kalatzis et al. propose a framework for storing and modeling the HCD used to forecast the context and location information of users. The framework includes an algorithm for periodically caching context information into an HCD database (HCDDB), and procedures for creating and maintaining the HCDDB based on adjustable threshold time frames and the relevance of prediction rules to the activities and locations of users. The incidences of each context data combination in a time frame are assigned a daily summative attenuation score, and stored as a single entry in the HCDDB. The maximum score, the minimum threshold score, the minimum score difference of entries, and the cumulative scores of selected entries in a time frame are alternative models available in context estimation. The probabilities of a user performing specific activities in time frames are computed from the entries in the HCDDB. These probabilities are used in context prediction. The prediction rules are responsive to the constraints of the resources and the requirements of the context estimation. An experiment was performed to evaluate the effectiveness of the HCD framework in predicting user-centered information. A random sample of volunteers carried cell phones equipped with software designed to identify their movement locations over 13 weeks. The initial 12 weeks yielded the training dataset for a context inference engine. The experimental context information was collected in the last week. The evaluation results revealed significant user-location successes, and an excellent storage reduction in the volume of data in the HCDDB. Consequently, the advocated methodology for user-centered information deduction with HCD is useful for predicting subjective types of context information. Online Computing Reviews Service
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