عملکرد پیش بینی متن توسط شبکه های بیزی دینامیکی: تاکید بر پیش بینی موقعیت مکانی در پشتیبانی تصمیم گیری محیط زیست همه جا

Ubiquitous decision support systems require more intelligent mechanism in which more timely and accurate decision support is available. However, conventional context-aware systems, which have been popular in the ubiquitous decision support systems field, cannot provide such agile and proactive decision support. To fill this research void, this paper proposes a new concept of context prediction mechanism by which the ubiquitous decision support devices are able to predict users’ future contexts in advance, and provide more timely and proactive decision support that users would be satisfied much more. Especially, location prediction is useful because ubiquitous decision support systems could dynamically adapt their decision support contents for a user based on a user’s future location. In this sense, as an alternative for the inference engine mechanism to be used in the ubiquitous decision support systems capable of context-prediction, we propose an inductive approach to recognizing a user’s location by learning a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power in the location prediction. Besides, we found that the dynamic Bayesian network model has a great potential for the future types of ubiquitous decision support systems.

As the ubiquity becomes perceived by users more easily in their daily lives, context-aware systems have received a lot of attention from both practitioners and researchers. World-wild excitement about smart phones is also representing people’s passion about the context-aware devices. In this respect, context-awareness has been the subject of the growing attention in the area of ubiquitous computing over the years due to its usefulness for several application domains (Hong, Suh, & Kim, 2008). When computer systems are aware of the context in which they are used and are able to adapt to changes in context, they can engage in more efficient interaction with users. Context awareness is concerned with enabling ubiquitous computing devices to be aware of changes in the environment, and to intelligently adapt themselves to provide more meaningful and timely decision support for decision-makers (Feng, Teng, & Tan, 2009). However, context-aware systems are limited by the fact that their target is the current context, and that the future context is not predicted by context-aware systems. Therefore, the quality of services provided by the context-aware systems is seriously restricted when future contexts change drastically. To this end, we need to consider the task of context prediction in order to proactively offer high-quality services for users in ubiquitous computing environments. Context prediction opens a wide variety of possibilities of context-aware computing applications. A context-prediction application may infer the future location of an office owner and redirect incoming calls to the future location. A context-prediction application may also be useful for enhancing the quality of transportation systems. Based on the information about the current location and the future location of a particular user, transportation systems equipped with context prediction technology may be able to assist drivers more effectively by inferring possible preferred routes and by providing customized route suggestions for drivers, as well as warning the drivers about possible dangers by predicting their future context. Knowing the current location and current time, together with the user’s calendar, could also allow application to have a good idea of the user’s current social situation, such as if the user is in a meeting, in class, waiting in the airport, and so on. The list of applications listed here is limited and we believe that there is a great potential for context prediction to be used in a variety of ubiquitous computing applications. Especially, it becomes clear how much users would benefit from the ubiquitous decision support systems equipped with the context-prediction mechanism. As an alternative for the inference engine to be used in the ubiquitous decision support systems capable of providing context-prediction function, this paper proposes a dynamic Bayesian network (DBN) approach to location prediction for ubiquitous computing environments. DBN is an important technique because of its ability to represent the temporal properties of user context information. In fact, it is obvious that a user’s current locations are influenced by their previous locations, and particular locations afford particular types of actions. Therefore, we adopted a DBN approach for recognizing the locations of users. This paper is structured as follows. Section 2 discusses context prediction and various context prediction techniques in ubiquitous computing environments. The modeling techniques used to predict a user’ locations are described in Section 3. The results of the experiment are presented and discussed in Section 4, followed by concluding remarks and directions for future work in Section 5.

Context prediction is an important problem in ubiquitous computing environments. Accurately predicting user contexts could greatly improve the quality of user satisfaction in every aspect of daily life, particularly in the use of ubiquitous decision support systems. By drawing inferences about user locations, ubiquitous decision support systems should not only automatically detect a user’s current situation, but also forecast the user’s likely future location. Such location prediction systems will help users make decisions quickly and efficiently by providing the most suitable services. By utilizing the DBN, this paper has shown that the DBN has potentials to be used as a context-prediction mechanism for the ubiquitous decision support systems. The findings reported here contribute to the growing body of work on context prediction for ubiquitous computing environments. In the future, it will be useful to investigate much richer context information such as a user’s intention, or emotional states to predict a user’s future context. Secondly, evaluating the resulting models as a runtime component in ubiquitous environments will be an important next step in the development of context-prediction applications. Thirdly, it will be important to investigate location prediction models that can make “early” predictions of user location. Early prediction would allow ubiquitous systems adequate time to prepare for services for a particular future location of the user or to suggest alternative locations before users move to dangerous locations.