This document describes the creation of an informative Web GIS aimed at mitigating the impacts of flooding in the municipality of Ouagadougou, in Burkina Faso, a region that is highly sensitive to climate change. Burk...This document describes the creation of an informative Web GIS aimed at mitigating the impacts of flooding in the municipality of Ouagadougou, in Burkina Faso, a region that is highly sensitive to climate change. Burkina Faso, which is undergoing rapid urbanization, faces major natural threats, particularly flooding, as demonstrated by the severe floods of 2009 that caused loss of life, injury, structural damage and economic losses in Ouagadougou. The aim of this research is to develop a web map highlighting the municipality’s flood-prone areas, with a view to informing and raising awareness of flood risk reduction. Using the Leaflet JavaScript mapping library, the study uses HTML, CSS and JavaScript to implement web mapping technology. Data on Ouagadougou’s flood zones is generated by a multi-criteria analysis combining Saaty’s AHP method and GIS in QGIS, integrating seven (7) parameters including hydrography, altitude, slope, rainfall, soil types, land use and soil moisture index. QGIS processes and maps the themes, PostgreSQL with PostGIS serves as the DBMS and GeoServer functions as the map server. The Web GIS platform allows users to visualize the different flood risks, from very low to very high, or the high-risk areas specific to Ouagadougou. The AHP calculations classify the municipality into five flood vulnerability zones: very low (24.48%), low (27.93%), medium (23.01%), high (17.11%) and very high (7.47%). Effective risk management requires communication and awareness-raising. This online mapping application serves as a tool for communication, management and flood prevention in Ouagadougou, helping to mitigate flood-related natural disasters.展开更多
The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of ...The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.展开更多
Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved perform...Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.展开更多
Web service （WS） is an emerging software technology, especially acting an important role in cloud computing. The WS choreography description language （WS-CDL） is the standard for modeling the observable behavior o...Web service （WS） is an emerging software technology, especially acting an important role in cloud computing. The WS choreography description language （WS-CDL） is the standard for modeling the observable behavior of WS composition across multiple participants from a global point of view. However, it lacks of a formal semantics and could easily lead to misunderstanding and different implementations. In this paper, the WS-CDL based specifications are formally extracted in a communicating sequential process supporting a formal approach to checking WS models. In addition, formalisms and model checking are explicitly illustrated through a simple but non-trivial example with the help of model checker process analysis toolkit （PAT）.展开更多
文摘This document describes the creation of an informative Web GIS aimed at mitigating the impacts of flooding in the municipality of Ouagadougou, in Burkina Faso, a region that is highly sensitive to climate change. Burkina Faso, which is undergoing rapid urbanization, faces major natural threats, particularly flooding, as demonstrated by the severe floods of 2009 that caused loss of life, injury, structural damage and economic losses in Ouagadougou. The aim of this research is to develop a web map highlighting the municipality’s flood-prone areas, with a view to informing and raising awareness of flood risk reduction. Using the Leaflet JavaScript mapping library, the study uses HTML, CSS and JavaScript to implement web mapping technology. Data on Ouagadougou’s flood zones is generated by a multi-criteria analysis combining Saaty’s AHP method and GIS in QGIS, integrating seven (7) parameters including hydrography, altitude, slope, rainfall, soil types, land use and soil moisture index. QGIS processes and maps the themes, PostgreSQL with PostGIS serves as the DBMS and GeoServer functions as the map server. The Web GIS platform allows users to visualize the different flood risks, from very low to very high, or the high-risk areas specific to Ouagadougou. The AHP calculations classify the municipality into five flood vulnerability zones: very low (24.48%), low (27.93%), medium (23.01%), high (17.11%) and very high (7.47%). Effective risk management requires communication and awareness-raising. This online mapping application serves as a tool for communication, management and flood prevention in Ouagadougou, helping to mitigate flood-related natural disasters.
基金supported by the National Science Foundation of China(No.51575264)the Jiangsu Province Science Foundation for Excellent Youths under Grant BK20121011the Fundamental Research Funds for the Central Universities(No.NS2015050)
文摘The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.
基金Supporting this study through Taif University Researchers Supporting Project number(TURSP-2020/115),Taif University,Taif,Saudi Arabia.
文摘Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.
基金supported by the Shanghai Leading Academic Discipline Project (Grant No.J50103)
文摘Web service （WS） is an emerging software technology, especially acting an important role in cloud computing. The WS choreography description language （WS-CDL） is the standard for modeling the observable behavior of WS composition across multiple participants from a global point of view. However, it lacks of a formal semantics and could easily lead to misunderstanding and different implementations. In this paper, the WS-CDL based specifications are formally extracted in a communicating sequential process supporting a formal approach to checking WS models. In addition, formalisms and model checking are explicitly illustrated through a simple but non-trivial example with the help of model checker process analysis toolkit （PAT）.
