Owing to the increase and the complexity of data caused by the uncertain environment, the water environment monitoring system in Three Gorges Reservoir Area faces much pressure in data handling. In order to identify the water quality quickly and effectively, this paper presents a new big data processing algorithm for water quality analysis. The algorithm has adopted a fast fuzzy C-means clustering algorithm to analyze water environment monitoring data. The fast clustering algorithm is based on fuzzy C-means clustering algorithm and hard C-means clustering algorithm. And the result of hard clustering is utilized to guide the initial value of fuzzy clustering. The new clustering algorithm can speed up the rate of convergence. With the analysis of fast clustering, we can identify the quality of water samples. Both the theoretical and simulated results show that the algorithm can quickly and efficiently analyze the water quality in the Three Gorges Reservoir Area, which significantly improves the efficiency of big data processing. What is more, our proposed processing algorithm provides a reliable scientific basis for water pollution control in the Three Gorges Reservoir Area.
With the increasing popularity of mobile devices and applications, emerging mobile network traffic exhibits special characteristics in temporal scale e.g., there is a scale variance between the network traffic on weekdays and on weekends. Although most existing methods have been applied to data traffic prediction, few of them take such characteristic into consideration. In this paper, by using real data in mobile networks, we adopt the entropy theory to reveal that the duration of time-series given for prediction doesn't always have a positive impact and that the uncorrelated preceding time-series also deteriorates the prediction accuracy. In view of this, partitioning the network traffic prediction into weekdays' and weekends' perspective, we propose a method to predict the data traffic. Finally, we evaluate the proposed method through predicting the data traffic for a future time according to the historical data traffic in a real mobile network. In comparison with the work based on ARMA (Auto Regressive Moving Average) method, our proposed method can reduce the Mean Absolute Percentage Error (MAPE) by 35.7% and 43.8% on weekdays' and weekends' prediction, respectively. Index Terms-Mobile networks, data traffic prediction, entropy theory, time-series, real data I.
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