Nowadays movement patterns and people＇s be- havioral models are needed for traffic engineers and city plan- ners. These observations could be used to reason about mobil- ity and its sustainability and to support deci...Nowadays movement patterns and people＇s be- havioral models are needed for traffic engineers and city plan- ners. These observations could be used to reason about mobil- ity and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valu- able to the urban planner, so as to localize new services, orga- nize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to inves- tigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made avail- able by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform how many peo- ple are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by an- alyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications be- tween mobile phone users during their daily lives.In this work, we propose a comprehensive visual analysissystem named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population＇s mobility patterns from millions of phone call records. Our system consists of three major components： 1） visual analysis of user groups in a base station; 2） visual anal- ysis of the mobility patterns on different user groups mak- ing phone calls in certain base stations; 3） visual analysis of handoff phone call records. Some well-established visu- alization techniques such as parallel coordinates and pixel- based representations have been integrated into our system. We also develop a novel visualization schemes, Voronoi- diagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people＇s mobility patterns.展开更多
In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore＇s law. This exp...In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore＇s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidi- mensional characteristics. The huge amount of data brings many challenges on the management, transportation, and pro- cessing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. How- ever, how to build a good data center remains an open chal- lenge. Concurrently, the architecture design, which signifi- cantly affects the total performance, is of great research inter- est. This paper surveys advances in data center network de- sign. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today＇s data center network architectures. In the third part, we present some up-to-date data center frame- works and make a comprehensive comparison of them. Dur- ing the comparison, we observe that there is no so-called op- timal data center and the design should be different referring to the data placement, replication, processing, and query pro- cessing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.展开更多
Since the early 21st century, big data has drawn much attention from both academia and industry. In the last decade of collecting massive data, seeking innovative applications, and developing powerful technologies, th...Since the early 21st century, big data has drawn much attention from both academia and industry. In the last decade of collecting massive data, seeking innovative applications, and developing powerful technologies, the major focus of the research and engineering in big data has shifted substantially in many aspects. It is time to rethink about this fuzzy term to make clear the upcoming opportunities and the most urgent challenges.展开更多
文摘Nowadays movement patterns and people＇s be- havioral models are needed for traffic engineers and city plan- ners. These observations could be used to reason about mobil- ity and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valu- able to the urban planner, so as to localize new services, orga- nize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to inves- tigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made avail- able by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform how many peo- ple are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by an- alyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications be- tween mobile phone users during their daily lives.In this work, we propose a comprehensive visual analysissystem named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population＇s mobility patterns from millions of phone call records. Our system consists of three major components： 1） visual analysis of user groups in a base station; 2） visual anal- ysis of the mobility patterns on different user groups mak- ing phone calls in certain base stations; 3） visual analysis of handoff phone call records. Some well-established visu- alization techniques such as parallel coordinates and pixel- based representations have been integrated into our system. We also develop a novel visualization schemes, Voronoi- diagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people＇s mobility patterns.
文摘In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore＇s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidi- mensional characteristics. The huge amount of data brings many challenges on the management, transportation, and pro- cessing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. How- ever, how to build a good data center remains an open chal- lenge. Concurrently, the architecture design, which signifi- cantly affects the total performance, is of great research inter- est. This paper surveys advances in data center network de- sign. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today＇s data center network architectures. In the third part, we present some up-to-date data center frame- works and make a comprehensive comparison of them. Dur- ing the comparison, we observe that there is no so-called op- timal data center and the design should be different referring to the data placement, replication, processing, and query pro- cessing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.
基金This work was supported by the National Science Foundation of China （Grant No. 61300030）, and the University of Macan, China （SRG2015-00050-FST）.
文摘Since the early 21st century, big data has drawn much attention from both academia and industry. In the last decade of collecting massive data, seeking innovative applications, and developing powerful technologies, the major focus of the research and engineering in big data has shifted substantially in many aspects. It is time to rethink about this fuzzy term to make clear the upcoming opportunities and the most urgent challenges.
