| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 104 | 205 | 350 | 42 |
ACM Transactions on Information Systems covers a variety of subjects, including Information retrieval, Artificial intelligence, Data mining, World Wide Web and Machine learning. As a part of ACM Transactions on Information Systems, discussions in Information retrieval involve topics like Ranking (information retrieval), Relevance (information retrieval), Query expansion, Search engine and Web query classification. The concepts on Query expansion presented in the journal can also apply to other research fields, including Query language and Web search query.
ACM Transactions on Information Systems explores topics in Query language which can be helpful for research in disciplines like RDF query language and Query optimization. The work tackled in ACM Transactions on Information Systems goes beyond the discipline of Query optimization as it also encompasses Sargable. The studies on Artificial intelligence discussed can also contribute to research in the domains of Context (language use), Recommender system and Natural language processing.
Specifically, studies on Collaborative filtering are prevalent in the Recommender system works discussed. In ACM Transactions on Information Systems, Set (abstract data type) and Search engine indexing are investigated in conjunction with one another to address concerns in Data mining research.
The published articles primarily focus on research topics in Information retrieval, Artificial intelligence, World Wide Web, Data mining and Human–computer interaction. While work presented in the published articles provide substantial information on Artificial intelligence, it also covers topics in Machine learning, Computer vision and Natural language processing. The published papers address concerns in Data mining which are intertwined with other disciplines, such as Collaborative filtering, Recommender system and Cluster analysis.
ACM Transactions on Information Systems explores disciplines such as Information retrieval, Human–computer interaction, Data science, Recommender system and Feature learning. The journal holds forums on Information retrieval that merges themes from other disciplines such as Latency (engineering) and Pruning (decision trees). The research on Human–computer interaction featured in the journal combines topics in other fields like Multi-task learning, Task (project management), User modeling and Knowledge graph.
The studies in Data science featured incorporate elements of End-to-end principle and Open domain. Collaborative filtering is a focus of the presented Recommender system works and it dives deep in Collaborative filtering. Issues in Feature learning were discussed, taking into consideration concepts from other disciplines like Topic model, Embedding and Group (mathematics).
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 ACM Transactions on Information Systems (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 ACM Transactions on Information Systems (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 2022 edition, 5.56% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.65% were posted by at least one author from the top 10 institutions publishing in the journal. Another 41.18% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 5.88% of all publications and 35.29% 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 important aspect that was not thoroughly addressed in the article is how the findings discussed in ACM Transactions on Information Systems can be applied in practical settings. For instance, is it possible for these research topics to contribute to the improvement of the current pedagogical methods? It’s interesting to observe how the advancements in Artificial Intelligence and Machine Learning can have an impact on various areas including the education sector. Taking the domain of education as an example, understandings from the field of Machine Learning and Artificial Intelligence can facilitate the development of methods for personalized learning - a teaching model that tailors instruction, content, pace, and assessments to individual students’ needs. Through topics such as query expansion, data mining and natural language processing which have been discussed in ACM Transactions on Information Systems, developers can potentially create technologically advanced tools to facilitate student learning. In fact, several establishments have already begun to leverage these advancements to produce intelligent tutoring systems (ITS), adaptive learning platforms, learning analytics methodologies, and more. For those interested in a direct application in the educational sector, particularly in elementary school education, these learnings may inform your decision about {anchor} Exploring the future of these potentials and addressing the ethical implications evolving around this technology in an educational context is an important angle to consider. As researchers continue to review relevant papers and topics, it is key to keep in mind the ways in which their findings could have a transformative effect on real-world settings. Emphasizing the application of these topics and how they affect sectors such as education, health, finance etc., could add significant depth to the analysis and discussion presented in the journal.
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(2021)Minlie Huang;Xiaoyan Zhu;Jianfeng Gao
(2020)Chong Chen;Min Zhang;Yongfeng Zhang;Yiqun Liu
(2020)Unknown
(2022)Tianchi Yang;Linmei Hu;Chuan Shi;Houye Ji
(2021)Ruihong Qiu;Zi Huang;Jingjing Li;Hongzhi Yin
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