Knowledge and Information Systems

Top Research Topics at Knowledge and Information Systems?

Knowledge and Information Systems investigates studies in Data mining, Artificial intelligence, Machine learning, Cluster analysis and Information retrieval. Knowledge and Information Systems facilitates discussions on Data mining that incorporate concepts from other fields like Algorithm, Scalability and Set (abstract data type). It explores issues in Artificial intelligence which can be linked to other research areas like Natural language processing and Pattern recognition.

It primarily discusses Cluster analysis topics, particularly Correlation clustering, Fuzzy clustering, CURE data clustering algorithm and Data stream clustering.

• Data mining (36.10%)
• Artificial intelligence (34.20%)
• Machine learning (19.54%)

What are the most cited papers published in the journal?

• Top 10 algorithms in data mining (3497 citations)
• Data Preparation for Mining World Wide Web Browsing Patterns (1453 citations)
• Exact indexing of dynamic time warping (1397 citations)

Research areas of the most cited articles at Knowledge and Information Systems:

The journal publications primarily focus on research topics in Data mining, Artificial intelligence, Machine learning, Cluster analysis and Pattern recognition. The studies on Data mining discussed at the most cited publications can also contribute to research in the domains of Algorithm, Information extraction and Set (abstract data type). The journal articles connects research in Artificial intelligence with the related topics of Natural language processing.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The objective of Knowledge and Information Systems is to combine knowledge in the areas of Artificial intelligence, Machine learning, Data mining, Pattern recognition and Set (abstract data type). Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Domain (software engineering) and Natural language processing. Machine learning research presented in the journal encompasses a variety of subjects, including Class (computer programming) and Adaptation (computer science).

The study of Data mining encompasses disciplines such as Feature selection, as well as fields such as Feature (computer vision), all of which overlap with one another. In addition to Pattern recognition research, it aims to explore topics under Subspace topology, Constraint (information theory) and Embedding. The journal focuses on Set (abstract data type) but the discussions also offer insight into other areas such as Tree traversal and Data point.

The most cited articles from the last journal are:

• RELINE: point-of-interest recommendations using multiple network embeddings (6 citations)
• BestNeighbor: efficient evaluation of kNN queries on large time series databases (4 citations)
• CANE: community-aware network embedding via adversarial training (3 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Knowledge and Information Systems (based on the number of publications) are:

• Philip S. Yu (29 papers) absent at the last edition,
• Jian Pei (18 papers) published 1 paper at the last edition the same number as at the previous edition,
• Jianzhong Li (14 papers) absent at the last edition,
• Svetha Venkatesh (13 papers) absent at the last edition,
• Hong Gao (13 papers) published 1 paper at the last edition, 1 less than at the previous edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Knowledge and Information Systems (based on the number of publications) are:

• IBM (55 papers) published 1 paper at the last edition,
• Chinese Academy of Sciences (27 papers) published 4 papers at the last edition, 3 more than at the previous edition,
• Harbin Institute of Technology (26 papers) published 2 papers at the last edition, 1 less than at the previous edition,
• Deakin University (25 papers) absent at the last edition,
• Simon Fraser University (24 papers) published 1 paper at the last edition the same number as at the previous edition.

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.

Publication chance based on affiliation

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, 6.60% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 11.11% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.06% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.16% of all publications and 66.67% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Educational Opportunities and Career Choices in Knowledge and Information Systems Field

If you are interested in these research topics, and the inter-disciplinary approach of knowledge and information systems appeals to you, you can consider building a career in the said areas. The path to such a career primarily initiates from an educational background in computer science, artificial intelligence or data sciences. Universities across the globe offer specialized degree programs and courses, training individuals in specific areas of data mining, Artificial intelligence, machine learning or cluster analysis.

Besides the academic courses, real-world experience is equally crucial. Internships, co-op programs, and part-time roles provide hands-on experience and offer practical exposure to the theory learned in classrooms. After acquiring a bachelor's or master's, individuals can venture into a wide range of positions such as data scientists, machine learning engineer, AI-specialist, and many others.

Teaching is another popular career choice among graduates of these programs. Educational institutions, including high schools and universities, are frequently in search of teachers and professors who specialize in these areas. If you are specifically interested in teaching in middle school and located in Alaska, here's a guide on how you can become a middle school math teacher in Alaska.

Remember, evolving technologies continuously introduce new areas worth exploring. Therefore, continuous learning and skills enhancement is key to a successful career in the Knowledge and Information Systems field.