For technical and other reasons there is a dilemma that data providers cannot find an appropriate way to redistribute spatial forest data and data users who need spatial data cannot access and integrate available fore...For technical and other reasons there is a dilemma that data providers cannot find an appropriate way to redistribute spatial forest data and data users who need spatial data cannot access and integrate available forest resources information. To overcome this dilemma, this paper proposed a spatial forest information system based on Web service using an open source software approach. With Web service based architecture, the system can enable interoperability, integrate Web services from other application servers, reuse codes, and shorten the development time and cost. At the same time, it is possible to extend the local system to a regional or national spatial forest information system. The growth of Open Source Software （OSS） provides an alternative choice to proprietary software for operating systems, web servers, Web-based GIS applications and database management systems. Using open source software to develop spatial forest information systems can greatly reduce the cost while providing high performance and sharing spatial forest information. We chose open source software to build a prototype system for Xixia County, Henan Province, China. By integrating OSS packages Deegree and UMN MapServer which are compliant to the OGC open specifications, the prototype system enables users to access spatial forest information and travelling information of Xixia County which come from two different data servers via a standard Web browser and promotes spatial forest information sharing.展开更多
In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning ha...In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.展开更多
In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 ...In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.展开更多
基金the National 863 program (2003AA131020-06)the programme Young scientists from extra-European countries to Lower Saxony.
文摘For technical and other reasons there is a dilemma that data providers cannot find an appropriate way to redistribute spatial forest data and data users who need spatial data cannot access and integrate available forest resources information. To overcome this dilemma, this paper proposed a spatial forest information system based on Web service using an open source software approach. With Web service based architecture, the system can enable interoperability, integrate Web services from other application servers, reuse codes, and shorten the development time and cost. At the same time, it is possible to extend the local system to a regional or national spatial forest information system. The growth of Open Source Software （OSS） provides an alternative choice to proprietary software for operating systems, web servers, Web-based GIS applications and database management systems. Using open source software to develop spatial forest information systems can greatly reduce the cost while providing high performance and sharing spatial forest information. We chose open source software to build a prototype system for Xixia County, Henan Province, China. By integrating OSS packages Deegree and UMN MapServer which are compliant to the OGC open specifications, the prototype system enables users to access spatial forest information and travelling information of Xixia County which come from two different data servers via a standard Web browser and promotes spatial forest information sharing.
基金supported by the grants from Natural Science Foundation of China(Project No.61375045)the joint astronomic fund of the national natural science foundation of China and Chinese Academic Sinica(Project No.U1531242)Beijing Natural Science Foundation(4142030)
文摘In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.
基金Scientific Research Program Funded by Shaanxi Provincial Education Department,China(No.2013JK0749)
文摘In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.
