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Atmospheric pollution is getting more and more attention because of the serious status. Finding the pollutant source is one of the key steps to solve this problem. This paper introduces a new method to trace the pollution diffusion path according to the relevance of different areas where they were polluted (the relatively small areas regard as points or lattices in this paper). We present the single...
With huge amount of observed air quality and components data, it is of great challenge to analyze and trace the pollutant diffusion path. Partitioning the air pollution sources (air quality observation stations) into subnetworks will help a lot in tracing the air pollution diffusion path. Conventional air pollution sources clustering methods, which are based on geography or pollutant levels, present...
Learning temporal causal structures between time series is one of key tools for analyzing time series data. Most previous works focuse on learning with static temporal causal relationships. However, in many real world applications, such as climate environment and transportation system, the causal structures vary dramatically over time. In this paper, we propose a probabilistic dynamic causal (PDC)...
Deep learning has attracted a lot of attention in research and industry in recent years. Behind the success of deep learning, there is much space for improvement. It is difficult to identify if a testing sample can be represented by the deep network effectively before we examining the final result. In this paper, we proposed a dynamic boosting strategy according to reconstruction error in deep networks...
Understanding and predicting destination of a trip is a crucial component of location based services. Traditional destination prediction work mostly focus on mining mobility patterns from frequently been locations. However, location transition patterns are not regular enough to provide favorable predicting results. Meanwhile, it could only be used when a user has enough movements in a location. In...
GPS-based activity recognition is extremely important for high-level analysis and location based services. Trajectories of people are highly imbalanced from spatial and temporal perspectives. Many existing researches achieve good results on recognizing activities with lots of GPS logs, such as working and staying at home. However, these approaches usually fail at activities with few trajectory records...
Location information is becoming much more important than ever before, especially in mobile services. Being widespread, less cost of energy and almost free for collecting data make mobile phone a perfect location sensor probe. Meaningful location name rather than digital coordinates could provide much more valuable information. In this paper, we develop a location semantic predicting method referred...
Hadoop has shown great power in processing vast data in parallel. Hive, the database on Hadoop, enables more experts to process relational data by providing sql-like interface. However, Hive does not provide an efficient approach for join, a common but expensive operator in relational database. Due to the importance of join, this paper proposes a novel hybrid algorithm, HJA, which can help to automatically...
Map Reduce cluster is emerging as a solution of data-intensive scalable computing system. The open source implementation Hadoop has already been adopted for building clusters containing thousands of nodes. Such cloud infrastructure was used to processing many different jobs depending on different hardware resources, such as memory, CPU, Disk I/O and Network I/O, simultaneously. If the schedule policy...
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