In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and the non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: Predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user's location changes. We have developed sequential pattern mining based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under a small prediction sets.
Like middle-men in physical commerce, middleagents support the flow of information in electronic commerce, assisting in locating and connecting the ultimate information provider with the ultimate information requester. Many different types of middleagents will be useful in realistic, large, distributed, open multi-agent problem solving systems. These include matchmakers or yellow page agents that process advertisements, blackboard agents that collect requests, and brokers that process both. The behaviors of each type of middle-agent have certain performance characteristics-privacy, robustness, and adaptiveness qualities-that are related to characteristics of the external environment and of the agents themselves. For example, while brokered systems are more vulnerable to certain failures, they are also able to cope more quickly with a rapidly fluctuating agent workforce and meet certain privacy considerations. This paper identifies a spectrum of middle-agents, characterizes the behavior of three different types, and reports on initial experiments that focus on evaluating performance tradeoffs between matchmaking and brokering middle-agents, according to criteria such as load balancing, robustness, dynamic preferences or capabilities, and privacy.
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