Teaching evaluation on a WebGIS course is a multi-objective nonlinear high-dimensional NP-hard problem. The index system for the teaching evaluation of a WebGIS course, including teacher- and student-oriented sub-syst...Teaching evaluation on a WebGIS course is a multi-objective nonlinear high-dimensional NP-hard problem. The index system for the teaching evaluation of a WebGIS course, including teacher- and student-oriented sub-systems, is first established and used for questionnaires from 2013 to 2017. The multi-objective nonlinear high-dimensional evaluation model is constructed and then solved via dynamic self-adaptive teaching–learning-based optimization (DSATLBO). DSATLBO is based on teaching–learning-based optimization with five improvements: dynamic nonlinear self-adaptive teaching factor, extracurricular tutorship factor, dynamic self-adaptive learning factor, multi-way learning factor, and non-dominated sorting factor. WebGIS teaching performance is fully evaluated based on questionnaires and DSATLBO. Optimal weights and weighted scores from DSATLBO are compared with those from the non-dominated sorting genetic algorithm-II using the Pareto front, coverage to two sets, and spacing of the non-dominated solution sets to validate the performance of DSATLBO. The results show that DSATLBO can be uniformly distributed along the Pareto front. Therefore, DSATLBO can efficiently and feasibly solve the multi-objective nonlinear high-dimensional teaching evaluation model of a WebGIS course. The proposed teaching evaluation method can help reflecting the quality of all aspects of classroom teaching and guide the professional development of students.展开更多
Building a cloud geodatabase for a sponge city is crucial to integrate the geospatial information dispersed in various departments for multi-user high concurrent access and retrieval,high scalability and availability,...Building a cloud geodatabase for a sponge city is crucial to integrate the geospatial information dispersed in various departments for multi-user high concurrent access and retrieval,high scalability and availability,efficient storage and management.In this study,Hadoop distributed computing framework,including Hadoop distributed file system and MapReduce(mapper and reducer),is firstly designed with a parallel computing framework to process massive spatial data.Then,access control with a series of standard application programming interfaces for different functions is designed,including spatial data storage layer,cloud geodatabase access layer,spatial data access layer and spatial data analysis layer.Subsequently,a retrieval model is designed,including direct addressing via file name,three-level concurrent retrieval and block data retrieval strategies.Main functions are realised,including real-time concurrent access,high-performance computing,communication,massive data storage,efficient retrieval and scheduling decisions on the multi-scale,multi-source and massive spatial data.Finally,the performance of Hadoop cloud geodatabases is validated and compared with that of the Oracle database.The cloud geodatabase for the sponge city can avoid redundant configuration of personnel,hardware and software,support the data transfer,model debugging and application development,and provide accurate,real-time,virtual,intelligent,reliable,elastically scalable,dynamic and on-demand cloud services of the basic and thematic geographic information for the construction and management of the sponge city.展开更多
基金Project(41661026)supported by the National Natural Science Foundation of ChinaProject supported by the Fund for the Construction of Western-China First-class Specialty of Ningxia University,China
文摘Teaching evaluation on a WebGIS course is a multi-objective nonlinear high-dimensional NP-hard problem. The index system for the teaching evaluation of a WebGIS course, including teacher- and student-oriented sub-systems, is first established and used for questionnaires from 2013 to 2017. The multi-objective nonlinear high-dimensional evaluation model is constructed and then solved via dynamic self-adaptive teaching–learning-based optimization (DSATLBO). DSATLBO is based on teaching–learning-based optimization with five improvements: dynamic nonlinear self-adaptive teaching factor, extracurricular tutorship factor, dynamic self-adaptive learning factor, multi-way learning factor, and non-dominated sorting factor. WebGIS teaching performance is fully evaluated based on questionnaires and DSATLBO. Optimal weights and weighted scores from DSATLBO are compared with those from the non-dominated sorting genetic algorithm-II using the Pareto front, coverage to two sets, and spacing of the non-dominated solution sets to validate the performance of DSATLBO. The results show that DSATLBO can be uniformly distributed along the Pareto front. Therefore, DSATLBO can efficiently and feasibly solve the multi-objective nonlinear high-dimensional teaching evaluation model of a WebGIS course. The proposed teaching evaluation method can help reflecting the quality of all aspects of classroom teaching and guide the professional development of students.
基金Project(NZ1628)supported by the Natural Science Foundation of Ningxia,China
文摘Building a cloud geodatabase for a sponge city is crucial to integrate the geospatial information dispersed in various departments for multi-user high concurrent access and retrieval,high scalability and availability,efficient storage and management.In this study,Hadoop distributed computing framework,including Hadoop distributed file system and MapReduce(mapper and reducer),is firstly designed with a parallel computing framework to process massive spatial data.Then,access control with a series of standard application programming interfaces for different functions is designed,including spatial data storage layer,cloud geodatabase access layer,spatial data access layer and spatial data analysis layer.Subsequently,a retrieval model is designed,including direct addressing via file name,three-level concurrent retrieval and block data retrieval strategies.Main functions are realised,including real-time concurrent access,high-performance computing,communication,massive data storage,efficient retrieval and scheduling decisions on the multi-scale,multi-source and massive spatial data.Finally,the performance of Hadoop cloud geodatabases is validated and compared with that of the Oracle database.The cloud geodatabase for the sponge city can avoid redundant configuration of personnel,hardware and software,support the data transfer,model debugging and application development,and provide accurate,real-time,virtual,intelligent,reliable,elastically scalable,dynamic and on-demand cloud services of the basic and thematic geographic information for the construction and management of the sponge city.
