Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2016.10005792
Raja Bellakhal, K. Ghédira
The web service selection is the process of finding matches between the service descriptions and the client's specific needs including the QoS parameters. Actually, these parameters are dynamic and considering them static affects negatively the system's reliability. To make dynamic the QoS parameters, negotiation appears a relevant tool as it positively influences the success rate of the web service selection process. However, existing works do not consider important characteristics of the real humans' interactions including dependencies existing between the concurrent negotiation processes, and the hybrid as well as the dynamic negotiation aspects. In this paper, we show that omitting these aspects while implementing a selection system incurs degradation on its performance. We propose a web service selection agent system based on a hybrid negotiation that solves these drawbacks. The experimental results show that the proposed approach outperforms the existing approaches in terms of the outputs quality and the CPU time.
{"title":"A multi-agent-based negotiation system for web service selection","authors":"Raja Bellakhal, K. Ghédira","doi":"10.1504/IJKWI.2016.10005792","DOIUrl":"https://doi.org/10.1504/IJKWI.2016.10005792","url":null,"abstract":"The web service selection is the process of finding matches between the service descriptions and the client's specific needs including the QoS parameters. Actually, these parameters are dynamic and considering them static affects negatively the system's reliability. To make dynamic the QoS parameters, negotiation appears a relevant tool as it positively influences the success rate of the web service selection process. However, existing works do not consider important characteristics of the real humans' interactions including dependencies existing between the concurrent negotiation processes, and the hybrid as well as the dynamic negotiation aspects. In this paper, we show that omitting these aspects while implementing a selection system incurs degradation on its performance. We propose a web service selection agent system based on a hybrid negotiation that solves these drawbacks. The experimental results show that the proposed approach outperforms the existing approaches in terms of the outputs quality and the CPU time.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133401973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2012.051318
G. Batista, Mayu Urata, T. Yasuda
This paper discusses the development of the dynamic teaching materials system and its evaluation tests. The system was developed in a research project realised jointly at Nagoya University in Japan and Brasilia University in Brazil. The main purpose of creating this system was to find a way to make teaching materials dynamic, so they could be easily adapted to the necessities encountered by the teacher during classes. Another important point was to find a way to allow teachers to create, update and share multimedia interactive teaching materials themselves. The actual features of the system allow the teaching materials to evolve, but there are still many possibilities for improving the system.
{"title":"The dynamic teaching materials system: a way to make teaching materials evolve","authors":"G. Batista, Mayu Urata, T. Yasuda","doi":"10.1504/IJKWI.2012.051318","DOIUrl":"https://doi.org/10.1504/IJKWI.2012.051318","url":null,"abstract":"This paper discusses the development of the dynamic teaching materials system and its evaluation tests. The system was developed in a research project realised jointly at Nagoya University in Japan and Brasilia University in Brazil. The main purpose of creating this system was to find a way to make teaching materials dynamic, so they could be easily adapted to the necessities encountered by the teacher during classes. Another important point was to find a way to allow teachers to create, update and share multimedia interactive teaching materials themselves. The actual features of the system allow the teaching materials to evolve, but there are still many possibilities for improving the system.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131446215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2016.078718
Wedad Hussein, Tarek F. Gharib, R. Ismail, M. Mostafa
Web personalisation is the process of customising a website's content to users' specific needs. Next page prediction is one of the basic techniques needed for personalisation. In this paper, we present a framework for next page prediction that uses user-concept matrix clustering to integrate semantic information into web usage mining process for the purpose of improving prediction quality. We use clustering to group users based on common interests expressed as concept vectors and search only the set of frequent patterns matched to a user's cluster to make a prediction. The proposed framework was tested over two different datasets and compared to usage mining techniques that search the whole set of frequent patterns. The results showed a 33% and 2.1% improvement in the average system accuracy as well as 6.6% and 7.3% improvement in the average system precision and a 6.5% and 1.7% in coverage for the two datasets respectively, within the same computation timeframe.
{"title":"A user-concept matrix clustering algorithm for efficient next page prediction","authors":"Wedad Hussein, Tarek F. Gharib, R. Ismail, M. Mostafa","doi":"10.1504/IJKWI.2016.078718","DOIUrl":"https://doi.org/10.1504/IJKWI.2016.078718","url":null,"abstract":"Web personalisation is the process of customising a website's content to users' specific needs. Next page prediction is one of the basic techniques needed for personalisation. In this paper, we present a framework for next page prediction that uses user-concept matrix clustering to integrate semantic information into web usage mining process for the purpose of improving prediction quality. We use clustering to group users based on common interests expressed as concept vectors and search only the set of frequent patterns matched to a user's cluster to make a prediction. The proposed framework was tested over two different datasets and compared to usage mining techniques that search the whole set of frequent patterns. The results showed a 33% and 2.1% improvement in the average system accuracy as well as 6.6% and 7.3% improvement in the average system precision and a 6.5% and 1.7% in coverage for the two datasets respectively, within the same computation timeframe.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126084289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2012.051317
K. Asakura, Masayoshi Takeuchi, Toyohide Watanabe
In this paper, we propose a pedestrian-oriented map matching algorithm for tracking moving trajectories of pedestrians. Our algorithm is designed for a map information sharing system among refugees in disaster areas. In this situation, refugees have mobile terminals with GPS devices and move to shelters at walking speed. Thus, our algorithm has to be suitable for battery-driven mobile terminals. In order to reduce battery consumption, our algorithm is based on a geometric curve-to-curve matching approach in which computation resources are less required in comparison with other complicated approaches such as probabilistic map matching, statistical map matching and so on. Furthermore, in order to deal with matching errors, our algorithm has following features: initial matching point selection, candidate road segments selection and incremental matching method. We conduct experiments with real time sequence data captured by GPS. Experimental results shows that our proposed algorithm can achieve the best result in comparison with the other conventional geometric map matching methods.
{"title":"A pedestrian-oriented map matching algorithm for map information sharing systems in disaster areas","authors":"K. Asakura, Masayoshi Takeuchi, Toyohide Watanabe","doi":"10.1504/IJKWI.2012.051317","DOIUrl":"https://doi.org/10.1504/IJKWI.2012.051317","url":null,"abstract":"In this paper, we propose a pedestrian-oriented map matching algorithm for tracking moving trajectories of pedestrians. Our algorithm is designed for a map information sharing system among refugees in disaster areas. In this situation, refugees have mobile terminals with GPS devices and move to shelters at walking speed. Thus, our algorithm has to be suitable for battery-driven mobile terminals. In order to reduce battery consumption, our algorithm is based on a geometric curve-to-curve matching approach in which computation resources are less required in comparison with other complicated approaches such as probabilistic map matching, statistical map matching and so on. Furthermore, in order to deal with matching errors, our algorithm has following features: initial matching point selection, candidate road segments selection and incremental matching method. We conduct experiments with real time sequence data captured by GPS. Experimental results shows that our proposed algorithm can achieve the best result in comparison with the other conventional geometric map matching methods.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115340029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, news distribution through the internet has increased significantly and so does our growing dependency on online news sources. As vast numbers of web documents from different news websites are readily available, it is possible to extract information that can be used for various applications. One possible application is breaking news detection through text and property analysis of these web documents. In this paper, we presented an approach to detect breaking news from web documents by using keywords extraction through Brill's tagger and HTML tag attributes. Once the keywords are extracted, seasonality for each of the keywords are calculated by the ratio of the linear weighted moving averages LWMA at each point of the time series. Our approach has been validated and performance metrics have been evaluated with two online newspapers.
{"title":"Breaking news detection from the web documents through text mining and seasonality","authors":"Syed Tanveer Jishan, Md. Nuruddin Monsur, Hafiz Abdur Rahman","doi":"10.1504/IJKWI.2016.078714","DOIUrl":"https://doi.org/10.1504/IJKWI.2016.078714","url":null,"abstract":"In recent years, news distribution through the internet has increased significantly and so does our growing dependency on online news sources. As vast numbers of web documents from different news websites are readily available, it is possible to extract information that can be used for various applications. One possible application is breaking news detection through text and property analysis of these web documents. In this paper, we presented an approach to detect breaking news from web documents by using keywords extraction through Brill's tagger and HTML tag attributes. Once the keywords are extracted, seasonality for each of the keywords are calculated by the ratio of the linear weighted moving averages LWMA at each point of the time series. Our approach has been validated and performance metrics have been evaluated with two online newspapers.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116730039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2012.051319
Kenichi Sugihara
3D urban models are important in several fields, such as urban planning and gaming industries. However, enormous time and labour has to be consumed to create these 3D models. In order to automate laborious steps, a GIS and CG integrated system was proposed for automatically generating 3D building models, based on building polygons (building footprints) on digital maps. For either orthogonal or non-orthogonal building polygons, the knowledge-based system is proposed for automatically generating 3D building models with general shaped roofs by straight skeleton computation. In this paper, the algorithm for 'split event' is clarified and the new methodology is presented for constructing roof models by assuming the third event: 'simultaneous event' and, at the end of the shrinking process, some polygons are converged to 'a line of convergence'.
{"title":"Knowledge-based automatic generation of 3D building models from building footprint by straight skeleton computation","authors":"Kenichi Sugihara","doi":"10.1504/IJKWI.2012.051319","DOIUrl":"https://doi.org/10.1504/IJKWI.2012.051319","url":null,"abstract":"3D urban models are important in several fields, such as urban planning and gaming industries. However, enormous time and labour has to be consumed to create these 3D models. In order to automate laborious steps, a GIS and CG integrated system was proposed for automatically generating 3D building models, based on building polygons (building footprints) on digital maps. For either orthogonal or non-orthogonal building polygons, the knowledge-based system is proposed for automatically generating 3D building models with general shaped roofs by straight skeleton computation. In this paper, the algorithm for 'split event' is clarified and the new methodology is presented for constructing roof models by assuming the third event: 'simultaneous event' and, at the end of the shrinking process, some polygons are converged to 'a line of convergence'.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133601324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2011.045162
A. M. Rinaldi
The use of ontologies for knowledge representation has had a fast increase in the last years and they are used in several application context. One of these challenging applications is the web. Managing large amount of information on internet needs more efficient and effective methods and techniques for mining and representing information. In this article, we present a methodology for automatic topic annotation of web pages. We describe an algorithm for words disambiguation using an apposite metric for measuring the semantic relatedness and we show a technique which allows to detect the topic of the analysed document using ontologies extracted from a knowledge base. The strategy is implemented in a system where these information are used to build a topic hierarchy automatically created and not a priori defined for classifying web pages. Experimental results are presented and discussed in order to measure the effectiveness of our approach.
{"title":"Automatic web pages hierarchical classification using dynamic domain ontologies","authors":"A. M. Rinaldi","doi":"10.1504/IJKWI.2011.045162","DOIUrl":"https://doi.org/10.1504/IJKWI.2011.045162","url":null,"abstract":"The use of ontologies for knowledge representation has had a fast increase in the last years and they are used in several application context. One of these challenging applications is the web. Managing large amount of information on internet needs more efficient and effective methods and techniques for mining and representing information. In this article, we present a methodology for automatic topic annotation of web pages. We describe an algorithm for words disambiguation using an apposite metric for measuring the semantic relatedness and we show a technique which allows to detect the topic of the analysed document using ontologies extracted from a knowledge base. The strategy is implemented in a system where these information are used to build a topic hierarchy automatically created and not a priori defined for classifying web pages. Experimental results are presented and discussed in order to measure the effectiveness of our approach.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122578981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2016.078733
D. Sejal, T. Kamalakant, Dinesh Anvekar, K. Venugopal, S. S. Iyengar, L. Patnaik
Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for webpage recommendation, reduction of latency and online advertising. These applications compromise with prediction accuracy and modelling complexity. We propose a web navigation prediction framework for webpage recommendation WNPWR which creates and generates a classifier based on sessions as training examples. As sessions are used as training examples, they are created by calculating the average time on visiting webpages rather than traditional method which uses 30 minutes as default timeout. This paper uses standard benchmark datasets to analyse and compare our framework with two-tier prediction framework. Simulation results show that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.
{"title":"Webpage recommendation with web navigation prediction framework","authors":"D. Sejal, T. Kamalakant, Dinesh Anvekar, K. Venugopal, S. S. Iyengar, L. Patnaik","doi":"10.1504/IJKWI.2016.078733","DOIUrl":"https://doi.org/10.1504/IJKWI.2016.078733","url":null,"abstract":"Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for webpage recommendation, reduction of latency and online advertising. These applications compromise with prediction accuracy and modelling complexity. We propose a web navigation prediction framework for webpage recommendation WNPWR which creates and generates a classifier based on sessions as training examples. As sessions are used as training examples, they are created by calculating the average time on visiting webpages rather than traditional method which uses 30 minutes as default timeout. This paper uses standard benchmark datasets to analyse and compare our framework with two-tier prediction framework. Simulation results show that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121842460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2016.10005796
Rachna Miglani
In this world of specialisation where everything is getting specialised, data warehouses and web mining techniques are also getting specialised. Web usage mining, web content mining, and web structure mining are various categories of web mining techniques depending upon the data to be mined. Apriori algorithm, FP growth algorithm, and average linear time algorithm are available to analyse the general access patterns in web server logs whereas WCOND-mine and signed with weight technique are web content outlier mining algorithms. However, no such algorithm is available to check the authenticity and availability of hyperlinks in the resultant web pages given by web search engines. The present research work aims at detection of outliers from the results of queries over web pages through web search engines.
在这个一切都变得专业化的世界里，数据仓库和网络挖掘技术也变得专业化。Web使用挖掘、Web内容挖掘和Web结构挖掘是Web挖掘技术的不同类别，这取决于要挖掘的数据。Apriori算法、FP增长算法和平均线性时间算法可用于分析web服务器日志中的一般访问模式，而WCOND-mine和signed with weight技术是web内容离群值挖掘算法。然而，没有这样的算法是可用的，以检查的真实性和可用性的结果网页上的超链接由网络搜索引擎给出。目前的研究工作旨在通过网络搜索引擎从网页查询结果中检测异常值。
{"title":"WSOLINK: web structure outlier detection algorithm","authors":"Rachna Miglani","doi":"10.1504/IJKWI.2016.10005796","DOIUrl":"https://doi.org/10.1504/IJKWI.2016.10005796","url":null,"abstract":"In this world of specialisation where everything is getting specialised, data warehouses and web mining techniques are also getting specialised. Web usage mining, web content mining, and web structure mining are various categories of web mining techniques depending upon the data to be mined. Apriori algorithm, FP growth algorithm, and average linear time algorithm are available to analyse the general access patterns in web server logs whereas WCOND-mine and signed with weight technique are web content outlier mining algorithms. However, no such algorithm is available to check the authenticity and availability of hyperlinks in the resultant web pages given by web search engines. The present research work aims at detection of outliers from the results of queries over web pages through web search engines.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"378 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121764286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1504/IJKWI.2016.078712
Asha S. Manek, P. D. Shenoy, M. Mohan, K. Venugopal
Recently, the web has become a crucial worldwide platform for online shopping. People go online to sell and buy products, use online banking facilities and even give opinions about their online shopping experience. People with malicious intent may be involved in any online transaction with a fraudulent e-business give fake positive reviews that actually does not exist to promote or degrade the product. User reviews are extremely essential for decision making and at the same time cannot be reliable. In this paper, we propose a novel method Bayesian logistic regression classifier BLRFier that detects fraudulent and malicious websites by analysing user reviews for online shopping websites. We have built our own dataset by crawling reviews of benign and malicious e-shopping websites to apply supervised learning techniques. Experimental evaluation of BLRFier model achieved 100% accuracy signifying the effectiveness of this approach for real-life deployment.
{"title":"Detection of fraudulent and malicious websites by analysing user reviews for online shopping websites","authors":"Asha S. Manek, P. D. Shenoy, M. Mohan, K. Venugopal","doi":"10.1504/IJKWI.2016.078712","DOIUrl":"https://doi.org/10.1504/IJKWI.2016.078712","url":null,"abstract":"Recently, the web has become a crucial worldwide platform for online shopping. People go online to sell and buy products, use online banking facilities and even give opinions about their online shopping experience. People with malicious intent may be involved in any online transaction with a fraudulent e-business give fake positive reviews that actually does not exist to promote or degrade the product. User reviews are extremely essential for decision making and at the same time cannot be reliable. In this paper, we propose a novel method Bayesian logistic regression classifier BLRFier that detects fraudulent and malicious websites by analysing user reviews for online shopping websites. We have built our own dataset by crawling reviews of benign and malicious e-shopping websites to apply supervised learning techniques. Experimental evaluation of BLRFier model achieved 100% accuracy signifying the effectiveness of this approach for real-life deployment.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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