1548-3924
Published by: IGI Global Publishing
https://www.igi-global.com/journal/international-journal-data-warehousing-mining/1085
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 841 | 12 | 13 | 5 |
The journal was organized to reinforce research efforts on Data mining, Data warehouse, Artificial intelligence, Online analytical processing and Machine learning. Data mining research presented in the journal encompasses a variety of subjects, including Set (abstract data type) and Cluster analysis. Many of the studies tackled connect Set (abstract data type) with a similar field of study like Algorithm.
Cluster analysis studies presented include Correlation clustering, Fuzzy clustering and Data stream clustering. Correlation clustering research is the primary subject tackled in it with a focus on CURE data clustering algorithm. International Journal of Data Warehousing and Mining explores issues in Data warehouse which can be linked to other research areas like Information retrieval, Business intelligence, Data science and Materialized view.
Some problems in Artificial intelligence that were presented in the journal overlapped with concepts under Natural language processing and Pattern recognition. In addition to Online analytical processing research, the journal aims to explore topics under Theoretical computer science, Decision support system and Data cube.
The most cited articles are organized to reinforce research efforts on Data warehouse, Data mining, Online analytical processing, Data science and Information retrieval. The published papers with studies in Data warehouse featured incorporate elements of Software, Metadata, Set (abstract data type) and Business intelligence. The study of Data mining in the most cited articles encompasses disciplines such as Artificial intelligence, as well as fields such as TOPSIS, all of which overlap with one another.
Data warehouse, Artificial intelligence, Set (abstract data type), Database and Filter (video) are the subjects of interest in International Journal of Data Warehousing and Mining. The journal explores topics in Data warehouse which can be helpful for research in disciplines like Information retrieval and Data science. The Artificial intelligence study featured in the journal draws parallels with the field of Natural language processing.
International Journal of Data Warehousing and Mining focuses on Set (abstract data type) but sometimes tackles the closely related topic of Algorithm which is concerned with Scalability, Biclustering, Logical matrix and Binary number. While Database is the focus of International Journal of Data Warehousing and Mining, it also provided insights into the studies of Data quality and Subset and superset. Topics in Process (computing) were tackled in line with various other fields like Data mining and Big data.
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 International Journal of Data Warehousing and Mining (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 International Journal of Data Warehousing and Mining (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, 15.79% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.50% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 31.25% of all publications and 56.25% 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.
While the domain of data warehousing and mining continues to expand in research, it also opens up a broad range of career opportunities. These extend from academic roles in universities and research institutes to industrial roles in tech companies and business consultancies. One such promising career avenue lies in teaching at the secondary or tertiary education levels. Private schools, in particular, may offer specialized courses related to data mining, artificial intelligence or machine learning. For example, becoming a private school teacher in Delaware specializing in teaching subjects such as data mining, data warehouse, and AI could be an enriching and rewarding journey. In Delaware, becoming a private school teacher involves satisfying certain educational and certification criteria before one can engage in the teaching of these specialized subjects. This includes possessing a related degree, undergoing a period of practical teaching experience, and sometimes completing a course in pedagogical techniques. Knowledge in related disciplines such as data science, business intelligence or natural language processing can also add depth to one's teaching. To understand more about these requirements and steps to start a teaching career in Delaware's private schools, you can explore this private school teacher requirements delaware. Thus, a career in data warehousing and mining is not only intellectually fulfilling but also offers various avenues for individuals with different interests and strengths. Whether in the halls of academia or the offices of tech firms, opportunities abound for those versed in this important and expanding field.
Venkatesan Rajinikanth;Seifedine Nimer Kadry
(2021)Deden Witarsyah;Mohd Farhan Md Fudzee;Mohamad Aizi Salamat;Iwan Tri Riyadi Yanto
(2020)Zi Hung You;Ya Han Hu;Chih Fong Tsai;Yen Ming Kuo
(2020)Nguyen Long Giang;Le Hoang Son;Nguyen Anh Tuan;Tran Thi Ngan
(2021)Latha Banda;Karan Singh;Le Hoang Son;Mohamed Abdel-Basset
(2020)Cu Nguyen Giap;Nguyen Nhu Son;Nguyen Long Giang;Hoang Thi Minh Chau
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