The temporal-spatial distribution of mid-small earthquakes in Italy and its surroundings from January 1 to April 5,2009 shows that there were significant foreshocks before the moderate L'Aquila earthquake of April...The temporal-spatial distribution of mid-small earthquakes in Italy and its surroundings from January 1 to April 5,2009 shows that there were significant foreshocks before the moderate L'Aquila earthquake of April 6,2009.The enhancement of frequency and intensity of small earthquakes and their concentrating tendency to the future main shock have provided a comprehensive case for digging methods of earthquake forecasting with foreshocks.展开更多
Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, qua...Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.展开更多
基金supported by the State Key Program of"Exploring Radar Tomography with Seismic Waves:4D-Mapping the Regional Continental Crustal Structureswith Artificial Seismic Sources"from the National Natural Science Foundation (40730318),China
文摘The temporal-spatial distribution of mid-small earthquakes in Italy and its surroundings from January 1 to April 5,2009 shows that there were significant foreshocks before the moderate L'Aquila earthquake of April 6,2009.The enhancement of frequency and intensity of small earthquakes and their concentrating tendency to the future main shock have provided a comprehensive case for digging methods of earthquake forecasting with foreshocks.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.
