2178-7107
Published by: Sociedade Brasileira de Computacao
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 975 | 11 | 14 | 3 |
The journal investigates areas of study like Data mining, Information retrieval, Artificial intelligence, Machine learning and Database. The journal addresses concerns in Data mining which are intertwined with other disciplines, such as Theoretical computer science, Set (abstract data type), Search engine indexing and Cluster analysis. The studies in Information retrieval featured incorporate elements of XML validation and World Wide Web.
The journal focused on XML validation research conducted under the discipline of XML. In addition to World Wide Web research, the journal aims to explore topics under Data management and Data science. Identification (information), Pattern recognition and Natural language processing are some topics wherein Artificial intelligence research discussed in Journal of Information and Data Management have an impact.
Relevance (information retrieval) is a major topic of Machine learning research presented in the journal. Process ontology, Ontology-based data integration and Upper ontology are Ontology (information science) topics of special interest in Journal of Information and Data Management. Process ontology research is concerned with Ontology alignment in particular.
The published articles primarily tackle Information retrieval, Ontology-based data integration, Upper ontology, Process ontology and Database. The most cited papers aim to form a more comprehensive understanding of the field by integrating disciplines like Information retrieval and Digital library. While work presented in the most cited papers provide substantial information on Database, it also covers topics in Java, Multiprocessing, Document Structure Description and Streaming XML.
The discussions in the journal mainly cover the fields of Information retrieval, Data science, Data mining, Machine learning and Artificial intelligence. While Journal of Information and Data Management focused on Information retrieval, it was also able to explore topics like Content-based image retrieval, Fake news and Chatbot. The journal facilitates the exploration of Data science in relation to the fields of Scientific experiment, Democracy, Presidential election, Presidential system and Ranked voting system.
Data mining research presented in the journal encompasses a variety of subjects, including Representation (mathematics), Similarity (psychology) and Ranking (information retrieval). While work presented in Journal of Information and Data Management provided substantial information on Machine learning, it also covered topics in Temporal database and Big data. Topics in Artificial intelligence explored in it were investigated in conjunction with research in Bivariate analysis and Reliability (statistics).
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 Journal of Information and Data Management (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 Journal of Information and Data Management (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, 45.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 81.82% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.09% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 9.09% of all publications and 0.00% 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.
The development and exploration of topics discussed in the Journal of Information and Data Management require a high level of knowledge and expertise in areas such as data mining, information retrieval, AI, machine learning, and database management. For those interested in contributing to this field of study, several educational pathways can be considered. It often starts with an undergraduate degree in computer science, information systems or a related field. This provides a foundational understanding of programming languages, algorithms, and data structures.
Furthering one's education with a graduate degree, such as a Master's or Doctorate, typically opens more research-heavy or senior-level roles. With this expertise, one could contribute to the research topics discussed in the journal and play a role in advancing these areas of study.
In addition to academics, real-world experience is crucial for understanding practical applications, challenges, and solutions in these domains. It could be beneficial to seek internships, part-time roles, or full-time positions within organizations that concentrate on these areas. Gaining experiences in teaching or tutoring can also offer deeper insights and a different perspective, especially in computer science or data management.
An example of such a career pathway is becoming a middle school math teacher in Texas. This role can contribute to early education in mathematical concepts that are fundamental in fields such as AI, machine learning, and data management. To find out more about how you can start this journey, follow this link.
To summarize, individuals who aim to contribute to the Journal of Information and Data Management encompass a combination of strong educational background and relevant hands-on experience.
Hermano L. S. Lustosa;Anderson C. Silva;Daniel N. R. da Silva;Patrick Valduriez
(2020)Cláudio Gustavo S. Capanema;Fabrício A. Silva;Thais R. M. Braga Silva;Antonio A. F. Loureiro
(2021)João A. O. Pedrosa;Derick M. de Oliveira;Wagner Meira;Antônio Luiz P. Ribeiro
(2021)L. H. M. Jacintho;T. P. da Silva;A. R. S. Parmezan;G. E. A. P. A. Batista
(2021)Liliane N. O. Kunstmann;Débora B. Pina;Filipe Silva;Aline Paes
(2021)Guilherme Dytz dos Santos;Ana L. C. Bazzan;Arthur Prochnow Baumgardt
(2021)Raphael Marins;Rafael Pereira de Oliveira;Edward Hermann Haeusler;Sérgio Lifschitz
(2021)For those interested in studying Computer Science but seeking flexibility, exploring self paced online colleges can be a great option. These programs allow students to learn at their own speed, making it easier to balance studies with work or personal commitments.
Cost is another important factor. Many prospective students look for the least expensive online masters programs to advance their education without breaking the bank. Affordable online master’s degrees in Computer Science often provide quality education with the flexibility needed for today's busy learners.
If starting with a shorter, less intensive program appeals to you, consider looking into the what is the easiest associate's degree to get for foundational knowledge. Associate degrees serve as a stepping stone to higher-level study or entry-level positions in tech-related fields.
Choosing the right institution is crucial. Prioritize attending online schools with national accreditation to ensure your degree holds value for future career opportunities and further education.
French Institute for Research in Computer Science and Automation - INRIA
Publications: 2
