| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 152 | 209 | 293 | 31 |
The journal covers a variety of subjects, including World Wide Web, Information retrieval, Artificial intelligence, Data mining and Machine learning. Web modeling, Web service, Web page, Web development and Web navigation are among the concentrations of World Wide Web that garnered much attention in it. While Web modeling is the focus of the journal, it also provided insights into the studies of Web standards, Web design, Web application security and Data Web.
Web service research is concerned with Web mapping in particular. Some problems in Information retrieval that were presented in the journal overlapped with concepts under Ranking and XML. It facilitated presentations on XML research, particularly Efficient XML Interchange, XML validation, Document Structure Description, Streaming XML and XML schema.
The research on Artificial intelligence tackled can also make contributions to studies in the areas of Natural language processing, Recommender system, Graph (abstract data type) and Pattern recognition. The studies in Data mining featured incorporate elements of Set (abstract data type) and Cluster analysis. The concepts on Web query classification presented in the journal can also apply to other research fields, including Query expansion and Query optimization.
World Wide Web, Information retrieval, Web service, Data mining and Artificial intelligence are the main subjects of interest in the most cited articles. The most cited papers tackle studies in Web page and the interrelated subject of Web server to gain insights into Web service. The studies on Artificial intelligence discussed at the published articles can also contribute to research in the domains of Machine learning and Pattern recognition.
The journal investigates studies in Artificial intelligence, Theoretical computer science, Information retrieval, Machine learning and Graph (abstract data type). Topics in Artificial intelligence explored in World Wide Web were investigated in conjunction with research in Context (language use) and Natural language processing. Pruning (decision trees), Matching (graph theory), Structure (mathematical logic) and Vertex (geometry), Graph are some topics wherein Theoretical computer science research discussed in it have an impact.
It centers on topics in Information retrieval, with a focus on Recommender system. Machine learning research featured in the journal incorporates concerns from various other topics such as Entropy (information theory), Encoder, Component (UML) and Factor (programming language). Issues in Graph (abstract data type) were discussed, taking into consideration concepts from other disciplines like Search tree, Task (project management) and Data mining.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in World Wide Web (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in World Wide Web (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 0.90% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 44.55% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.91% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.09% of all publications and 25.45% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
An additional valuable area for exploration is how this impactful research can be translated into education and professional development, specifically within the field of web and tech education. For example, educators aspiring to teach students about web development, AI, machine learning, and data mining could use these research findings to enrich their curricula. Similarly, individuals looking to pursue a career in the tech industry ranging from data scientists to web developers could also find this research beneficial. Knowing the nuances of the most cited papers and understand the evolving trends in web-related subjects like AI and data mining can always help in career progression. Moreover, individuals seeking to bring these learnings into the practical world, specifically in the educational sector, could leverage insights from this research. An example could be those wanting to become elementary school teachers in technology-focused states like California. For more insights and guidance on this, you can refer to our detailed guide on how to become an elementary teacher in California. This guide will explore how advancements in the understanding of web technologies can be incorporated into teaching methodologies, enriching the education system right from the grassroots level.
Lianyong Qi;Yi Chen;Yuan Yuan;Shucun Fu
(2020)Zongda Wu;Guiling Li;Shigen Shen;Xinze Lian
(2021)Yiteng Pan;Fazhi He;Haiping Yu
(2020)Yahya Al-Hadhrami;Farookh Khadeer Hussain
(2021)Lingzhen Kong;Lina Wang;Wenwen Gong;Chao Yan
(2021)Jinyuan He;Jia Rong;Le Sun;Hua Wang
(2020)Rui Hu;Zheng Yan;Zheng Yan;Wenxiu Ding;Laurence T. Yang
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