Web Intell.最新文献_第2页

A survey of semantic web (Web 3.0), its applications, challenges, future and its relation with Internet of things (IoT) 语义网(web 3.0)及其应用、挑战、未来及其与物联网(IoT)的关系

Web Intell. Pub Date : 2022-09-22 DOI: 10.3233/web-210491

Adeem Ali Anwar

{"title":"A survey of semantic web (Web 3.0), its applications, challenges, future and its relation with Internet of things (IoT)","authors":"Adeem Ali Anwar","doi":"10.3233/web-210491","DOIUrl":"https://doi.org/10.3233/web-210491","url":null,"abstract":"The Semantic Web (Web 3.0) is an advancement of the existing web in which knowledge is given well-defined importance, allowing people and machines to operate better. The Semantic Web is the next step in the evolution of the Web. The semantic web improves online technologies in need of generating, distributing, and linking material. In literature, multiple surveys have been done on the semantic web (Web 3.0), but those surveys are limited to some specific topics. According to the best of our understanding, none of the surveys provides a comprehensive study about the applications, challenges, and future of the semantic web along with its relationship with the Internet of things (IoT). The previous surveys focused on the Web 3.0 without touching on applications or challenges or focused on only the application prospect of the web 3.0, focused on the just the challenges, or focused on web 3.0 relationship with either internet of things or knowledge graphs but failed to touch the other important factors i.e., failed to provide comprehensive web 3.0 survey. This survey paper covers the gaps created from the previous survey papers in the same field and provides a comprehensive survey about web 3.0, a comparison between web 1.0, 2.0, and 3.0, the study of application and challenges in web 3.0, the relationship between web 3.0 with IoT and knowledge graph. Moreover, it focuses on the evolution of the web, and semantic web along with an explanation of the various layers, ontology tools, and semantic web tools with their comparison and semantic web service search. Despite all the shortcomings and challenges, the semantic web is moving in the right direction, and it is the future of the web.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126683411","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}

引用次数: 3

A detection method of wormhole attack in power communication sensor networks based on hops 基于跳数的电力通信传感器网络虫洞攻击检测方法

Web Intell. Pub Date : 2022-09-09 DOI: 10.3233/web-220036

Jie Yuan, Binyuan Yan

引用次数: 0

A fast classification method of mass data in Internet of things based on fuzzy clustering maximum tree algorithm 基于模糊聚类最大树算法的物联网海量数据快速分类方法

Web Intell. Pub Date : 2022-09-07 DOI: 10.3233/web-220045

Zhixia Duan, Shuai Tang

引用次数: 0

Research on data fusion of power wireless sensor networks based on Kalman filter 基于卡尔曼滤波的电力无线传感器网络数据融合研究

Web Intell. Pub Date : 2022-09-07 DOI: 10.3233/web-220035

Hongya Wang

引用次数: 0

The mobile edge computing task offloading in wireless networks based on improved genetic algorithm 基于改进遗传算法的无线网络移动边缘计算任务卸载

Web Intell. Pub Date : 2022-08-16 DOI: 10.3233/web-220019

Zhanlei Shang, Chenxu Zhao

引用次数: 0

An automatic generation of software test data based on improved Markov model 基于改进马尔可夫模型的软件测试数据自动生成

Web Intell. Pub Date : 2022-08-16 DOI: 10.3233/web-220028

Jiali Chen, Xiaojie Chen, Tao Zan, Mengjia Lian

引用次数: 0

An accurate fault location method for wireless sensor network based on random matrix theory 基于随机矩阵理论的无线传感器网络故障精确定位方法

Web Intell. Pub Date : 2022-08-16 DOI: 10.3233/web-220026

Qi Wang

引用次数: 0

A multi-agent approach for on-demand transportation problem in cities 城市按需交通问题的多智能体方法

Web Intell. Pub Date : 2022-08-09 DOI: 10.3233/web-220004

A. Malas, S. E. Falou, Mohamad El Falou, Mohammad Hussein

{"title":"A multi-agent approach for on-demand transportation problem in cities","authors":"A. Malas, S. E. Falou, Mohamad El Falou, Mohammad Hussein","doi":"10.3233/web-220004","DOIUrl":"https://doi.org/10.3233/web-220004","url":null,"abstract":"On-demand transportation (ODT) systems have proliferated in diverse cities worldwide due to their social, economic and environmental advantages. Despite those advantages, it is vital to get public approval. The approval key is the system’s reactivity in supplying speedy and reliable solutions that consider clients’ and vehicles’ constraints. Those solutions have to reflect actual life conditions to optimize the quality of service. The most regarded challenge in studying the ODT problem in cities is the stochastic time-dependent travel speed that varies due to traffic fluctuations. To deal with an actual ODT problem, a system has to represent the traffic on its scale. Hence, estimating the travel speed at a specific time and affording a solution based on reliable traffic data. Accordingly, the passengers are served better. This work contributes to the study by solving the ODT problem in cities with a massive multi-agent system that considers historical traffic data and unpredictable events disrupting the typical traffic. We evaluate the proposed approach by experiments with instances based on actual data for a city in the north of Lebanon. The results reveal that the quality of service increases when the stochastic time-dependent travel speed is considered. 50% to 100% of affected clients by an unpredictable event are satisfied when this event is considered by the system.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123077734","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}

引用次数: 0

A data cube modeling method for longitudinal cohort study 纵向队列研究的数据立方体建模方法

Web Intell. Pub Date : 2022-07-22 DOI: 10.3233/web-220018

Xin Li, Huadong Liang, Lin Li

{"title":"A data cube modeling method for longitudinal cohort study","authors":"Xin Li, Huadong Liang, Lin Li","doi":"10.3233/web-220018","DOIUrl":"https://doi.org/10.3233/web-220018","url":null,"abstract":"Longitudinal cohort study is an effective way to probe into the risk factors of disease and evaluate intervention measures. It has gradually become the mainstream research method in precision medicine, chronic disease management and evidence-based education, and has been deployed in many National Science and Technology Major Projects, which established its specialized cohorts for natural populations, chronic diseases, specialized diseases. The quality of data is a make-or-break factor of longitudinal cohort study. The subjects and test tasks in longitudinal cohort study have dynamic changes over time, and the data generated involves multiple modalities and scales. Therefore, exploring how to model business-oriented longitudinal cohort data will contribute to a unified understanding and governance of longitudinal cohort data, and ultimately improve data quality. On the one hand, because different modal data in longitudinal cohort study have different dimensional indicators, it is difficult to carry out data modeling based on unified dimensional indicators through simple dimensional splicing; on the other hand, the needs of the longitudinal cohort management scenario determine the calculations should be focused on the granularity of individual subjects and data modal types. Considering the above, the traditional multi-dimensional data modeling method based on data dimension indicators and their measurements as basic elements couldn’t be fully adapted to the counting and statistical requirements under the longitudinal cohort scenarios. This paper proposes a data cube model based on MOLAP named SubTaP, which take multimodal data objects as basic granularity. This model constructs a cube structure with three dimensions of Subject, Task and Phase. It can be applied to meet the visualization requirements of longitudinal cohort management scenario and guide the construction of a data information platform for cohort study. At the same time, it helps to build a unified understanding of longitudinal cohort study data among data generators, cohort maintainers, and data users.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128243738","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}

引用次数: 0

Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework 基于分数帆鱼稀疏模糊c均值和粒子鲸优化的MapReduce框架的大数据聚类

Web Intell. Pub Date : 2022-07-20 DOI: 10.3233/web-210490

Omkaresh Kulkarni, Ravi Sankar Vadali

{"title":"Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework","authors":"Omkaresh Kulkarni, Ravi Sankar Vadali","doi":"10.3233/web-210490","DOIUrl":"https://doi.org/10.3233/web-210490","url":null,"abstract":"The process of retrieving essential information from the dataset is a significant data mining approach, which is specifically termed as data clustering. However, nature-inspired optimizations are designed in recent decades to solve optimization problems, particularly for data clustering complexities. However, the existing methods are not feasible to process with a large amount of data, as the execution time taken by the traditional approaches is larger. Hence, an efficient and optimal data clustering scheme is designed using the devised Fractional Sail Fish-Sparse Fuzzy C-Means + Particle Whale optimization (FSF-Sparse FCM + PWO) based MapReduce Framework (MRF) to process high dimensional data. Theproposed FSF-Sparse FCM is designed by the integration of Sail Fish Optimization (SFO) with fractional concept and Sparse FCM. The proposed MRF poses two functions, such as the mapper function and reducer function to perform the process of data clustering. Moreover, the proposed FSF-Sparse FCM is employed in the mapper phase to compute the cluster centroids, and thereby the intermediate data is generated. The intermediate data is tuned in the reducer phase using Particle Whale Optimization (PWO), which is the integration of Particle Swarm Optimization (PSO) and Whale optimization algorithm (WOA). Accordingly, the optimal cluster centroid is computed at the reducer phase using the objective function based on DB-Index. The proposed FSF-Sparse FM + PWO obtained the highest accuracy of 0.903 and lowest DB-Index of 39.07.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128921376","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}

引用次数: 0