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With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user's needs. Adaptive control of thought-rational (ACT-R) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this paper, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route evaluation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and then recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to evaluate the suitability of the routes inspired by cognition. [ABSTRACT FROM AUTHOR]

Although topic detection and tracking techniques have made great progress, most of the researchers seldom pay more attention to the following two aspects. First, the construction of a topic model does not take the characteristics of different topics into consideration. Second, the factors that determine the formation and development of hot topics are not further analyzed. In order to correctly extract news blog hot topics, the paper views the above problems in a new perspective based on the W2T (Wisdom Web of Things) methodology, in which the characteristics of blog users, context of topic propagation and information granularity are investigated in a unified way. The motivations and features of blog users are first analyzed to understand the characteristics of news blog topics. Then the context of topic propagation is decomposed into the blog community, topic network and opinion network, respectively. Some important factors such as the user behavior pattern, opinion leader and network opinion are identified to track the development trends of news blog topics. Moreover, a blog hot topic detection algorithm is proposed, in which news blog hot topics are identified by measuring the duration, topic novelty, attention degree of users and topic growth. Experimental results show that the proposed method is feasible and effective. These results are also useful for further studying the formation mechanism of opinion leaders in blogspace. [ABSTRACT FROM AUTHOR]