Abstract In 2025, Knowledge and Information Systems (KAIS) celebrates its 25th anniversary. To mark this occasion, this study presents a comprehensive bibliometric analysis covering the period 1999–2024, examining publication and citation patterns, influential authors, institutions, countries, and evolving research themes. Drawing on data from the Web of Science (WoS) Core Collection, the analysis integrates classical bibliometric indicators with science mapping techniques, including co-citation, bibliographic coupling, and keyword co-occurrence, supported by VOSviewer and Bibliometrix tools. The findings reveal that KAIS has published 2,385 articles, which have collectively received 54,319 citations, demonstrating sustained global influence. The journal’s intellectual core remains grounded in data mining, classification, clustering, and machine learning, while emerging themes—such as deep learning, graph neural networks, natural language processing, and concept drift—reflect its adaptation to contemporary developments in artificial intelligence. China and the United States lead in both productivity and impact, illustrating the globalization of the KAIS research community. This study offers insights into the evolution of knowledge and information systems as an interdisciplinary field, emphasizing KAIS’s dual role in preserving methodological rigor and promoting innovation. Overall, the journal stands as a benchmark for studying the dynamics of data-driven, intelligent, and responsible knowledge discovery.

This article develops a new penalty‐based aggregation operator known as the penalty‐based induced ordered weighted averaging (P‐IOWA) operator which is an extension of penalty‐based ordered weighted averaging (P‐OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P‐OWA and P‐IOWA operators with the different means such as generalized mean and quasi‐arithmetic mean. This article also includes different families of P‐OWA and P‐IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty‐based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.

Established in 1963 under the title Information Storage and Retrieval, the journal adopted its current name, Information Processing & Management (IPM), in 1975, reflecting a broadening scope aligned with computational and cognitive developments in information science. This study uses data from Web of Science and Scopus databases to deliver a longitudinal, multi-perspective bibliometric and science mapping analysis of IPM’s evolution from 1963 to 2023. Employing co-citation analysis, bibliographic coupling, keyword co-occurrence, and thematic mapping via VOSviewer and Bibliometrix, the analysis delineates the structural, conceptual, and topical transformation of the journal content. Co-citation networks uncover foundational cores in information retrieval, relevance theory, and evaluation methodologies, while also revealing temporal shifts toward natural language processing, deep learning, and social media analytics. Bibliographic coupling identifies coherent intellectual clusters centered on GNN-based recommendation systems, blockchain-secured infrastructures, and sentiment-aware retrieval frameworks. Keyword co-occurrence and topic evolution trajectories illustrate the journal’s recent pivot toward transformer models, misinformation detection, ethical AI, and interdisciplinary convergence across cognitive science, machine learning, and computational linguistics. Regional co-word analysis underscores epistemological diversity and geographic differentiation across North America, Europe, and East Asia. Productivity and influence metrics highlight the ascent of East Asian institutions and the emergence of globally distributed citation impact. Finally, SciVal-based topic and topic cluster analyses reveal the journal’s role in advancing highly cited research (as measured by FWCI) in areas such as ABSA, multi-view clustering, and health informatics. This work not only charts IPM’s conceptual landscape and disciplinary diffusion but also provides actionable intelligence on the journal’s strategic positioning within the broader information and computational sciences.

Traditional time series prediction methods are unable to handle the complex nonlinear relationship of a large data set. Most of the existing techniques are unable to manage multiple dimensions of a data set, due to which the computational complexity escalates with the increasing size of a data set. Many machine learning (ML) methods are unable to handle known unknown predictions. This paper presents a new forecasting method in the neural network structure based on the induced ordered weighted average (IOWA) weighted average (WA) and fuzzy time series. The proposed model is more efficient than existing complexity handling fuzzy time series prediction methods and other traditional time series prediction methods. The proposed model can accommodate the IOWA operator, weighted average, and relevance degree of each concept in a particular problem for a fuzzy nonlinear prediction. The contribution of this paper is twofold. First, it contributes to theory by proposing a new IOWAWA layer in the neural network to handle complex nonlinear prediction for a large data set. The second contribution is the application of the approach to predict nonlinear stock market data. The robustness of the approach is tested using Australian Securities Exchange (ASX) stock data by considering a case study of the housing and property sector. We further compare the prediction accuracy of the approach with sixteen existing methods. The experimental results demonstrate that the proposed model outperforms existing methods.

The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.

The variance is a statistical measure frequently used for analysis of dispersion in the data. This paper presents new types of variances that use Bonferroni means and ordered weighted averages in the aggregation process of the variance. The main advantage of this approach is that we can underestimate or overestimate the variance according to the attitudinal character of the decision-maker. The work considers several particular cases including the minimum and the maximum variance and presents some numerical examples. The article also develops some extensions and generalizations by using induced aggregation operators and generalized and quasi-arithmetic means. These approaches provide a more general framework that can consider a lot of other particular cases and a complex attitudinal character that could be affected by a wide range of variables. The study ends with an application of the new approach in a business decision-making problem regarding strategic analysis in enterprise risk management.

This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.

The variance is a statistical measure widely used in many real-life application areas. This article makes an extensive investigation on variance measure in the case when the uncertainty is not of a probabilistic nature. It generalizes the notion of variance as well as the theory of ordered weighted aggregation operators. First, we extend the idea of representative value/expected value of a decision maker and develop some new deviation measures based on ordered weighted geometric (OWG) average and ordered weighted harmonic average (OWHA) operators. These measures are developed with the consideration that decision maker can represent his/her attitudinal expected value by using any one of the ordered weighted aggregation (OWA) operators. Further, this study proposes some deviation measures by using the generalized-OWA (GOWA) and Quasi-OWA as an expected value of decision maker and discusses their particular cases. Second, a number of generalized deviation measures are introduced by taking the generalized arithmetic mean and quasi-arithmetic means for aggregation of the individual dispersion. This approach provides an ability to the user for considering the deviation under different realistic-scenario. These measures lead to many interesting particular and limiting cases and enrich the use of ordered weighted aggregation operators in the variance.

In this paper, we develop a new and flexible method for Pythagorean fuzzy decision-making using some trigonometric similarity measures. We first introduce two new generalized similarity measures between Pythagorean fuzzy sets based on cosine and cotangent functions and prove their validity. These similarity measures include some well-known Pythagorean fuzzy similarity measures as their particular and limiting cases. The measures are demonstrated to satisfy some very elegant properties which prepare the ground for applications in different areas. Further, the work defines a generalized hybrid trigonometric Pythagorean fuzzy similarity measure and discuss its properties with particular cases. Then, based on the generalized hybrid trigonometric Pythagorean fuzzy similarity measure, a method for dealing with multiple attribute decision-making problems under Pythagorean fuzzy environment is developed. Finally, a numerical example is given to demonstrate the flexibility and effectiveness of the developed approach in solving real-life problems.

This paper investigates a novel induced ordered weighted averaging (IOWA) distance operator and its application in Pythagorean fuzzy (PF) multiattribute group decision making (MAGDM). First, a new induced aggregated distance operator named the weighted IOWA distance (WIOWAD) operator is developed, which differs from the existing methods in that it considers the dual roles of the order-inducing variables at the same time. In other words, in addition to inducing the order of the arguments, the order-inducing variables of the WIOWAD operator also plays an important role in moderating the associated weight vector. Some useful properties and different families of the WIOWAD are also discussed. Then, an extension of the WIOWAD within the PF situation is presented, thus obtaining the PFWIOWAD operator. Furthermore, a MAGDM method based on the PFWIOWAD is introduced. Finally, the practicality and effectiveness of proposed approach are illustrated in a research and development project selection problem.

The Hamming distance is a well-known measure that is designed to provide insights into the similarity between two strings of information. In this study, we use the Hamming distance, the optimal deviation model, and the generalized ordered weighted logarithmic averaging (GOWLA) operator to develop the ordered weighted logarithmic averaging distance (OWLAD) operator and the generalized ordered weighted logarithmic averaging distance (GOWLAD) operator. The main advantage of these operators is the possibility of modeling a wider range of complex representations of problems under the assumption of an ideal possibility. We study the main properties, alternative formulations, and families of the proposed operators. We analyze multiple classical measures to characterize the weighting vector and propose alternatives to deal with the logarithmic properties of the operators. Furthermore, we present generalizations of the operators, which are obtained by studying their weighting vectors and the lambda parameter. Finally, an illustrative example regarding innovation project management measurement is proposed, in which a multi-expert analysis and several of the newly introduced operators are utilized.

This paper presents the induced heavy ordered weighted moving average (IHOWMA) operator. It is an aggregation operator that uses the main characteristics of three well-known techniques: the moving average, induced operator, and heavy aggregation operator. This operator provides a parameterized family of aggregation operators that include the minimum, the maximum, and total operator as special cases. It can be used in a selection process, considering that not all decision makers have the same knowledge and expectations of the future. The main properties of this operator are studied including a wide range of families of IHOWMA operators, such as the heavy ordered weighted moving average operator and uncertain induced heavy ordered weighted moving average operator. The IHOWMA operator is also extended using generalized and quasi-arithmetic means. An example in an investment selection process is also presented.

This paper focuses on the aggregation operations in the group decision-making model based on the concept of majority opinion. The weighted-selective aggregated majority-OWA (WSAM-OWA) operator is proposed as an extension of the SAM-OWA operator, where the reliability of information sources is considered in the formulation. The WSAM-OWA operator is generalized to the quantified WSAM-OWA operator by including the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM-IOWA operator, with an ordering step, is introduced to the individual fusion strategy. The proposed aggregation operators are then implemented for the case of alternative scheme of heterogeneous group decision analysis. The heterogeneous group includes the consensus of experts with respect to each specific criterion. The exhaustive multicriteria group decision-making model under the linguistic domain, which consists of two-stage aggregation processes, is developed in order to fuse the experts’ judgments and to aggregate the criteria. The model provides greater flexibility when analyzing the decision alternatives with a tolerance that considers the majority of experts and the attitudinal character of experts. A selection of investment problem is given to demonstrate the applicability of the developed model.

Ordered weighted averaging (OWA) operator has been received increasingly widespread interest since its appearance in 1988. Recently, a topic search with the keywords “ordered weighted averaging operator” or “OWA operator” on Web of Science (WOS) found 1231 documents. As the publications about OWA operator increase rapidly, thus a scientometric analysis of this research field and discovery of its knowledge domain becomes very important and necessary. This paper studies the publications about OWA operator between 1988 and 2015, and it is based on 1213 bibliographic records obtained by using topic search from WOS. The disciplinary distribution, most cited papers, influential journals, as well as influential authors are analyzed through citation and cocitation analysis. The emerging trends in OWA operator research are explored by keywords and references burst detection analysis. The research methods and results in this paper are meaningful for researchers associated with OWA operator field to understand the knowledge domain and establish their own future research direction.

The International Journal of Intelligent Systems was created in 1986. Today, the journal is 30 years old. To celebrate this anniversary, this study develops a bibliometric review of all of the papers published in the journal between 1986 and 2015. The results are largely based on the Web of Science Core Collection, which classifies leading bibliographic material by using several indicators including total number of publications and citations, the h-index, cites per paper, and citing articles. The work also uses the VOS viewer software for visualizing the main results through bibliographic coupling and co-citation. The results show a general overview of leading trends that have influenced the journal in terms of highly cited papers, authors, journals, universities and countries.

Step-like or Hurwicz-like ordered weighted averaging (OWA) (S-H OWA) operators connect two fundamental OWA operators, step OWA operators and Hurwicz OWA operators. S-H OWA operators also generalize them and some other well-know OWA operators such as median and centered OWA operators. Generally, there are two types of determination methods for S-H OWA operators: One is from the motivation of some existed mathematical results; the other is by a set of “nonstrict” definitions and often via some intermediate elements. For the second type, in this study we define two sets of strict definitions for Hurwitz/step degree, which are more effective and necessary for theoretical studies and practical usages. Both sets of definitions are useful in different situations. In addition, they are based on the same concept moment of OWA operators proposed in this study, and therefore they become identical in limit forms. However, the Hurwicz/step degree (HD/SD) puts more concerns on its numerical measure and physical meaning, whereas the relative Hurwicz/step degree (rHD/rSD), still being accurate numerically, sometimes is more reasonable intuitively and has larger potential in further studies and practical applications.

The demand is a fundamental variable in economic analysis that measures the needs of goods and services of the consumers. This paper presents a new approach for representing the demand by using aggregation systems that consider the attitudinal character of the consumers and their beliefs regarding the degree of importance of the different variables that may affect them. Several developments are introduced by using multiperson systems, different criterion, attributes, and states of nature. These developments are focused on the use of the weighted average, the ordered weighted average, and the ordered weighted averaging weighted average. Further aggregation systems are suggested by using the concept of the demand growth. The paper ends with an application of the new approach in a forecasting process that considers the attitude of the decision maker and its subjective beliefs.

Network information system security has become a global issue since it is related to the economic development and national security. Information system security assessment plays an important role in the development of security solutions. Aiming at this issue, a dual hesitant fuzzy (DHF) group decision-making (GDM) method was proposed in this paper to assist the assessment of network information system security. A systemic index containing four aspects was established including organization security, management security, technical security, and personnel management security. The DHF group evaluation matrix was constructed based on the individual evaluation information from each expert. Some power average operator–based DHF information aggregation operators are proposed and used to fusion the performance of each criterion for information systems. The advantage of these operators is that they can describe the relationship between the indexes quantitatively. Finally, a case study about information systems security assessment was presented to verify the effectiveness of proposed GDM methods.

The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper analyzes the use of the OWA in the variance and the covariance. It presents several extensions by using a unified framework between the weighted average and the OWA. Furthermore, it also develops other generalizations with induced aggregation operators and by using quasi-arithmetic means. Several measures of correlation by using the OWA are introduced including a new type of Pearson coefficient. The paper ends with some numerical examples focused on the construction of interval and fuzzy numbers with the variance and the covariance.

A new method for decision making based on generalized aggregation operators is presented. We use a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index uses distance measures and other techniques that are very useful for decision making. In this paper, it is suggested a generalization by using generalized and quasi-arithmetic means. As a result, it is obtained the generalized and quasi-arithmetic weighted IMAM (GWIMAM and quasi-WIMAM) and the generalized ordered weighted averaging IMAM (GOWAIMAM) and the quasi-OWAIMAM operator. The main advantage is that it provides a parameterized family of aggregation operators that includes a wide range of special cases such as the generalized IMAM and the OWAIMAM. Thus, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. We also develop an application of the new approach in a decision-making problem regarding product selection.

We present the probabilistic weighted average (PWA). It is an aggregation operator that unifies the probability and the weighted average in the same formulation. Its main advantage is that it provides a formulation that it is able to deal with probabilities and weighted averages in the same formulation, considering the degree of importance that each concept has in the analysis. We extend this approach by using moving averages. We study some of its main properties, and we see that the applicability of the PWA is very broad because we can apply it in a wide range of fields including statistics, economics, and engineering. We focus on a multiperson decision-making application regarding the selection of fiscal policies. We see that with this approach we can unify decision-making problems under risk environment with subjective and objective information. © 2012 Wiley Periodicals, Inc.

We present the uncertain induced quasi-arithmetic OWA (Quasi-UIOWA) operator. It is an extension of the OWA operator that uses the main characteristics of the induced OWA (IOWA), the quasi-arithmetic OWA (Quasi-OWA) and the uncertain OWA (UOWA) operator. Thus, this generalization uses quasi-arithmetic means, order inducing variables in the reordering process and uncertain information represented by interval numbers. A key feature of the Quasi-UIOWA operator is that it generalizes a wide range of aggregation operators such as the uncertain quasi-arithmetic mean, the uncertain weighted quasi-arithmetic mean, the UOWA, the uncertain weighted generalized mean, the uncertain induced generalized OWA (UIGOWA), the Quasi-UOWA, the uncertain IOWA, the uncertain induced ordered weighted geometric (UIOWG), and the uncertain induced ordered weighted quadratic averaging (UIOWQA) operator. We study some of the main properties of this approach including how to obtain a wide range of particular cases. We further generalize the Quasi-UIOWA operator by using discrete Choquet integrals. We end the article with an application of the new approach in a decision making problem about investment selection. © 2010 Wiley Periodicals, Inc.

In this study, we analyze the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging operator and the induced ordered weighted geometric operator. We then analyze the problem of decision making with Dempster-Shafer (D-S) theory of evidence. We suggest the use of induced aggregation operators in decision making with the D-S theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained with different types of aggregation operators can be seen. © 2009 Wiley Periodicals, Inc.