Domain Name System (DNS) logs capture detailed records of the queries and responses exchanged between DNS servers and clients, playing a crucial role in applications such as cybersecurity monitoring and regulatory compliance, which often require long-term data retention. With the rapid growth of Internet traffic, the volume of DNS logs has surged, presenting significant storage challenges. Although many DNS operators use general-purpose compression algorithms to reduce storage costs, these solutions fail to fully exploit the unique characteristics of DNS data, leading to inefficiencies and rising storage demands.

While the development of e-commerce brings convenience to consumers, a large quantity of products and information increase the difficulty of making purchase decisions. This study constructs a classification-based product selection method driven by online reviews to assist consumers in making purchase decisions. First, the multifaceted attribute evaluations of products are extracted from textual reviews that contain more abundant and useful information than those provided by vendors. The evaluations are modeled by probabilistic linguistic term sets such that sentiment words in texts are described at different frequencies. Then, a classification-based product selection method is developed to rank products considering multifaceted attributes in which alternative products are divided into the acceptance class, rejection class, and uncertainty class through a classification strategy. Each class of products is compared based on the performance scores calculated by a probabilistic linguistic aggregation operator. A case study of selecting laptops based on real data from Amazon.com is given to illustrate the method. Comparative analysis with existing ranking methods shows the advantages of the proposed method in matching consumers’ risk aversion behavior and preserving uncertain information.

By harnessing evaluation information like attribute types and attribute values hidden in online reviews, managers can acquire consumer preference and satisfaction over products. Most existing studies only considered how to extract attribute types or attribute values of products from textual reviews, but ignored the role of attribute-level ratings in reflecting consumer preference and satisfaction. Based on the techniques of sentiment analysis and preference disaggregation, this paper unifies the quantitative and qualitative information extracted from textual reviews and attribute-level ratings to obtain attribute types and values of products. A real case on TripAdvisor.com is given to verify the proposed method.

The probabilistic linguistic term set (PLTS), composed by linguistic terms and their probabilities, is effective to represent uncertain evaluations. Considering that interval probability is more powerful than the precise form in describing uncertainty, this study introduces the PLTS with interval probabilities. Based on belief and plausibility measures, in this article, we discuss how to translate complex qualitative evaluations, which may be composed by both interval probabilities and interval linguistic terms, to the PLTS with interval probabilities. Utility-based translation approaches are proposed, which further shows the ability of the PLTS with interval probabilities in representing quantitative information. In addition, a probabilistic linguistic dominance method is developed to compare PLTSs. Integrating optimization models with the Dempster–Shafer theory, we present an aggregation method to estimate the maximum and minimum PLTSs obtained from the combination. Furthermore, a multicriteria decision-making method is introduced considering both the comprehensive evaluations of alternatives and the ability to achieve the tolerance and expectation values of criteria. The applicability of the proposed approach is illustrated by a case study of shelter selection.

The probabilistic linguistic term set is a flexible and efficient tool to represent the cognitive complex information of experts. It has attracted many scholars’ attention since it was proposed. Information fusion over the cognitive complex information is a significant issue for decision-making problems. Over the past years, more than 40 aggregation operators have been proposed to fuse the probabilistic linguistic term sets. The aim of this paper is to survey the existing probabilistic linguistic aggregation operators from the perspectives of principles, definitions, classifications, and applications. To do so, first, we summarize the present normalization techniques and operations of probabilistic linguistic term sets. Afterward, this study classifies the existing probabilistic linguistic aggregation operators into 12 kinds. Then, the application areas of these probabilistic linguistic aggregation operators are outlined. Future research directions with interests are proposed to tackle present challenges.

To reflect the personalized cognition and expression in qualitative multiple criteria decision-making, this paper proposes unified operations for hybrid linguistic representations. To do so, the expected values of ten linguistic representation models are respectively defined based on the characteristic values of linguistic terms calculated by linguistic scale functions. Then, methods to compute the uncertainty degrees for ten models are developed to reflect the inherent hesitation and uncertainty of linguistic evaluations. Based on the expectation values and uncertainty degrees, hybrid linguistic operations are unified. An expectation value and uncertainty degree-based (EVUDB) method is developed for multiple criteria decision-making problems based on the proposed operations on hybrid linguistic representations. The advantages of the hybrid linguistic representations are highlighted by comparing with single linguistic representation model in solving a practical problem.

Abstract Innovative ability plays a critical role in the sustainable development of universities. Although the assessment of universities' innovative ability is a significant undertaking, it is difficult work. This challenge can be addressed as a typical multiple attribute decision making (MADM) problem, in which multiple attributes should be considered with different levels of importance. This paper aims to propose an integrated MADM method to solve this issue. To do so, we first introduce the least square method with the hesitant fuzzy linguistic term set to determine the subjective attribute weights. Considering that the selected attributes are not always in conflict with each other due to the complexity of objective things, we further present a correlation coefficient‐based method to calculate another kind of attribute weight. The final weights are the combined form of these two types of attribute weights. In addition, we enhance the robust ranking method, MULTIMOORA, with the Borda rule to calculate the utility values of universities and derive their rankings. Finally, after establishing an index system, the assessment of the innovative ability of 26 world‐class construction universities in China is conducted by using the proposed method. The advantages and disadvantages of the assessed universities are analysed.

The hesitant fuzzy linguistic term set (HFLTS) is an effective tool to express the experts' subjective evaluations in the processes of decision making. To solve the problem with both qualitative and quantitative criteria in the context of HFLTSs and the crisp weights of criteria being unknown, this paper proposes a new multicriteria decision making method. First, formulas are developed to convert the quantitative data into the hesitant fuzzy linguistic elements. Then, motivated by the ORESTE method, we develop a new global preference score function to aggregate the criterion weights and criterion values, both of which are expressed as hesitant fuzzy linguistic elements. To get the real relation between alternatives, three preference intensity formulas are proposed and a hesitant fuzzy linguistic indifference threshold is introduced. We establish a conflict test framework after detailed research on the threshold values. On these bases, a new hesitant fuzzy linguistic ORESTE method is developed and the calculation process of this method is described. A case study on supplier selection is then presented to illustrate the method. Finally, some comparative analyses with other methods are conducted to show the practicability and reliability of the proposed method.

The probabilistic linguistic term set (PLTS) is a powerful technique in representing linguistic evaluations of individuals or groups in the process of decision making. The aim of this paper is to propose a strongly robust method to solve multiexperts multicriteria decision making problems with linguistic evaluations. To enrich the computation and to improve the measures of PLTS, we first define an expectation function of it. In addition, we advance three kinds of probabilistic linguistic distance measures reflecting on the difference of linguistic terms and probabilities at the same time to make up for the defects of the existing distance measures, and then propose the similarity and correlation measures. Integrating the subjective opinions with the correlation coefficients between criteria, we put forward a combined weight determining method. The robustness of the ranking method, MULTIMOORA, is enhanced by the improved Borda rule. Based on these research findings, a probabilistic linguistic MULTIMOORA method is proposed. Finally, the developed method is applied to an empirical example concerning the selection of shared karaoke television brands. The effectiveness of the proposed method is verified by some comparative analyses.

Finger workspace is one of the most important features to evaluate the kinematic performances of the multifinger hands. It is also benefit for grasp planning, mechanical design, and motion control. Although many multi-finger hands have been developed over the last few decades, how to generate and visualize their workspaces is still a challengeable problem. We propose a workspace generation and visualization approach to explain the workspace characteristics of a multi-finger hand (ZSTU hand), which can help to solve the problem of choosing a grasp with the gripper constrains. Compared with previous researches, this approach can model more integrated fingertip workspaces with vivid forms and contours, which is valuable for researchers to accurately and intuitionally understand the fingertip motion ranges and the analysis of the workspace volume. These performances can help researchers to optimize the mechanical structures of the multi-finger hand, and improve the kinematic performances of the multi-finger hand.