Data & Knowledge Engineering最新文献

{"title":"Four decades of data & knowledge engineering: A bibliometric analysis and topic evolution study (1985–2024)","authors":"Tatsawan Timakum ,&nbsp;Soobin Lee ,&nbsp;Dongha Kim ,&nbsp;Min Song ,&nbsp;Il-Yeol Song","doi":"10.1016/j.datak.2025.102462","DOIUrl":"10.1016/j.datak.2025.102462","url":null,"abstract":"<div><div>The Data and Knowledge Engineering (DKE) journal has established a significant global research presence over four decades, substantially contributing to the advancement of data and knowledge engineering disciplines. This comprehensive bibliometric study analyzes the journal’s publications over the past 40 years (1985–2024), employing bibliographic records and citation data from Scopus, Web of Science (WoS), and ScienceDirect. By utilizing CiteSpace for citation and co-citation mapping and Dirichlet Multinomial Regression (DMR) topic modeling for trend analysis, the research provides a multifaceted examination of the journal’s scholarly landscape. Over its 40-year history, DKE has published 1951 articles, accumulating 53,594 citations. The study comprehensively explores key bibliometric dimensions, including influential authors, author networks, citation patterns, topic clusters, institutional contributions, and research funding sponsors, as well as evolution of topics, showing increasing, decreasing, or constant trends. Comprehensive analysis offers a meta-analytical perspective on DKE’s scholarly contributions, positioning the journal as a pioneering publication platform that advances critical knowledge and methodological innovations in data and knowledge engineering research domains. Through an in-depth examination of the journal’s publication trajectory, the study provides insights into the field’s scholarly evolution, highlighting DKE’s pivotal role in shaping academic discourse and technological understanding.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102462"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble model with combined feature set for Big data classification in IoT scenario","authors":"Harivardhagini S (Professor) ,&nbsp;Pranavanand S (Associate Professor) ,&nbsp;Raghuram A (Professor)","doi":"10.1016/j.datak.2025.102447","DOIUrl":"10.1016/j.datak.2025.102447","url":null,"abstract":"<div><div>Sensor nodes that are wirelessly connected to the internet and several systems make up the Internet of Things system. Large volumes of data are often stored in big data, which complicates the classification process. There are many Big data classification strategies in use, but the main issues are the management of secure information as well as computational time. This paper's goal is to suggest a novel classification system for big data in Internet of Things networks that operates in four main phases. Particularly, the healthcare data is considered as the Big data perspective to solve the classification problem. Since the healthcare Big data is the revolutionary tool in this industry, it is becoming the most vital point of patient-centric care. Different data sources are aggregated in this Big data healthcare ecosystem. The first stage is data acquisition which takes place via Internet of Things through sensors. The second stage is improved DSig normalization for input data preprocessing. The third stage is MapReduce framework-based feature extraction for handling the Big data. This extract features like raw data, mutual information, information gain, and improved Renyi entropy. Finally, the fourth stage is an ensemble disease classification model by the combination of Recurrent Neural Network, Neural Network, and Improved Support Vector Machine for predicting normal and abnormal diseases. The suggested work is implemented by the Python tool, and the effectiveness, specificity, sensitivity, precision, and other factors of the results are assessed. The proposed ensemble model achieves superior precision of 0.9573 for the training rate of 90 % when compared to the traditional models.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102447"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"G2MBCF: Enhanced Named Entity Recognition for sensitive entities identification","authors":"Weibin Tian ,&nbsp;Kaiming Gu ,&nbsp;Shihui Xiao ,&nbsp;Junbo Zhang ,&nbsp;Wei Cui","doi":"10.1016/j.datak.2025.102444","DOIUrl":"10.1016/j.datak.2025.102444","url":null,"abstract":"<div><div>With the increasing growth of data, work on data security is becoming increasingly important. As the core of important data detection, the sensitive entities identification (SEI) problem has become a hot topic in natural language processing (NLP) science. Named Entity Recognition (NER) is the foundation of SEI, however, current studies treat SEI only as a special case of the NER problem. It lacks more detailed considerations of implicit links between entities and relations. In this paper, we propose a novel enhanced method called G2MBCF based on latent factor model (LFM). We use knowledge graph to represent the NER primary result with semantic structure. Then we use G2MBCF to inscribe entities and relations through a <span><math><mrow><mi>E</mi><mo>−</mo><mi>R</mi></mrow></math></span> matrix to mine implicit connections. Experiments show that compared to existing NER methods, our method enhances <span><math><mrow><mi>R</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span> and <span><math><mrow><mi>P</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span> of SEI. We also studied the influence of parameters in the experiments.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102444"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Customized long short-term memory architecture for multi-document summarization with improved text feature set","authors":"Satya Deo ,&nbsp;Debajyoty Banik ,&nbsp;Prasant Kumar Pattnaik","doi":"10.1016/j.datak.2025.102440","DOIUrl":"10.1016/j.datak.2025.102440","url":null,"abstract":"<div><div>One <strong>a</strong>mong the most crucial concerns in the domain of Natural Language Processing (NLP) is the Multi-Document Summarization (MDS) and in recent decades, the focus on this issue has risen massively. Hence, it is vital for the NLP community to provide effective and reliable MDS methods. Current deep learning-dependent MDS techniques rely on the extraordinary capacity of neural networks, in order to extract distinctive features. Motivated by this fact, we introduce a novel MDS technique, named as Customized Long Short-Term Memory-based Multi-Document Summarization using IBi-GRU <strong>(</strong>CLSTM-MDS+IBi-GRU), which includes the following working processes. Firstly, the input data gets converted into tokens by the Bi-directional Transformer (BERT) tokenizer. The features, such as Term Frequency- Inverse Document Frequency (TF-IDF), Bag of Words (BoW), thematic features and an improved aspect term-based feature are then extracted afterwards. Finally, the summarization process takes place by utilizing the concatenation of Customized Long Short-Term Memory (CLSTM) with a pre-eminent layer. Accurate and high-quality summary is provided via introducing this layer in the LSTM module and the Bi-GRU-based Inception module (IBi-GRU), which can capture long range dependences through parallel convolution. The outcomes of this work prove the superiority of our CLSTM-MDS in the Multi-Document Summarization task.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102440"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Philosophical reflections on conceptual modeling as communication","authors":"Mattia Fumagalli ,&nbsp;Giancarlo Guizzardi","doi":"10.1016/j.datak.2025.102453","DOIUrl":"10.1016/j.datak.2025.102453","url":null,"abstract":"<div><div>Conceptual modeling is a complex and demanding task. It is a task centered around the challenge of representing a portion of the world in a way that is objective, understandable, shareable, and reusable by a community of practitioners, who rely on models to design and implement software or to clarify the concepts within a given domain. The difficulty of conceptual modeling stems from the inherent limitations of human representation abilities, which cannot fully capture the infinite richness and diversity of the world, nor the endless possibilities for description enabled by language. Significant effort has been invested in addressing these challenges, particularly in the creation of effective and reusable conceptual models, which have presented numerous difficulties. This paper explores conceptual modeling from a philosophical standpoint, proposing that conceptual models should not be viewed merely as the static representational output of an a priori activity, subject to modification only during a preliminary design phase. Instead, they should be seen as dynamic artifacts that require continuous design, adaptation, and evolution from their inception to their application, which may account for multiple purposes. The paper seeks to highlight the importance of understanding conceptual modeling primarily as an act of communication, rather than just a process of information transmission. It also aims to clarify the distinction between these two aspects and to examine the potential implications of adopting a <em>communicative approach to modeling</em>. These implications extend not only to the tools and methodologies used in modeling but also to the ethical considerations that arise from such an approach.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102453"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaboration with GenAI in engineering research design","authors":"Fazel Naghdy","doi":"10.1016/j.datak.2025.102445","DOIUrl":"10.1016/j.datak.2025.102445","url":null,"abstract":"<div><div>Over the past five years, the fast development and use of generative artificial intelligence (GenAI) and large language models (LLMs) has ushered in a new era of study, teaching, and learning in many domains. The role that GenAIs can play in engineering research is addressed. The related previous works report on the potential of GenAIs in the literature review process. However, such potential is not demonstrated by case studies and practical examples. The previous works also do not address how GenAIs can assist with all the steps traditionally taken to design research. This study examines the effectiveness of collaboration with GenAIs at various stages of research design. It explores whether collaboration with GenAIs can result in more focused and comprehensive outcomes. A generalised approach for collaboration with AI tools in research design is proposed. A case study to develop a research design on the concept of “shared machine-human driving” is deployed to show the validity of the articulated concepts. The case study demonstrates both the pros and cons of collaboration with GenAIs. The results generated at each stage are rigorously validated and thoroughly examined to ensure they remain free from inaccuracies or hallucinations and align with the original research objectives. When necessary, the results are manually adjusted and refined to uphold their integrity and accuracy. The findings produced by the various GenAI models utilized in this study highlight the key attributes of generative artificial intelligence, namely speed, efficiency, and scope. However, they also underscore the critical importance of researcher oversight, as unexamined inferences and interpretations can render the results irrelevant or meaningless.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102445"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conceptual design of multidimensional cubes with LLMs: An investigation","authors":"Stefano Rizzi,&nbsp;Matteo Francia,&nbsp;Enrico Gallinucci,&nbsp;Matteo Golfarelli","doi":"10.1016/j.datak.2025.102452","DOIUrl":"10.1016/j.datak.2025.102452","url":null,"abstract":"<div><div>Large Language Models (LLMs) can simulate human linguistic capabilities, thus producing a disruptive impact across several domains, including software engineering. In this paper we focus on a specific scenario of software engineering, that of conceptual design of multidimensional data cubes. The goal is to evaluate the performance of LLMs (precisely, of ChatGPT-4o) in multidimensional conceptual design using the Dimensional Fact Model as a reference. To this end, we formulate nine research questions to (i) understand the competences of ChatGPT in multidimensional conceptual design, following either a supply- or a demand-driven approach, and (ii) investigate to what extent they can be improved via prompt engineering. After describing the research process in terms of base criteria, technological setting, input/output format, prompt templates, test cases, and metrics for evaluating the results, we discuss the output of the experiment. Our main conclusions are that (i) when prompts are enhanced with detailed procedural instructions and examples, the results produced significantly improve in all cases; and (ii) overall, ChatGPT is better at demand-driven design than at supply-driven design.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102452"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Derived multi-objective function for latency sensitive-based cloud object storage system using hybrid heuristic algorithm","authors":"N Nataraj ,&nbsp;RV Nataraj","doi":"10.1016/j.datak.2025.102448","DOIUrl":"10.1016/j.datak.2025.102448","url":null,"abstract":"<div><div>Cloud Object Storage System (COSS) is capable of storing and retrieving a ton of unstructured data items called objects which act as a core cloud service for contemporary web-based applications. While sharing the data among different parties, privacy preservation becomes challenging. <em>Research Problem:</em> From day-to-day activities, a high volume of requests are served daily thus, it leads to cause the latency issues. In a cloud storage system, the adaption of a holistic approach helps the user to identify sensitive information and analyze the unwanted files/data. With evolving of Internet of Things (IoT) applications are latency-sensitive, which does not function well with these new ideas and platforms that are available today. <em>Overall Purpose of the Study:</em> Therefore, a novel latency-aware COSS is implemented with the aid of multi-objective functionalities to allocate and reallocate data efficiently in order to sustain the storage process in the cloud environment. <em>Design of the Study:</em> This goal is accomplished by implementing a hybrid meta-heuristic approach with the integration of the Mother Optimization Algorithm (MOA) with Dolphin Swarm Optimization (DSO) algorithm. The implemented hybrid optimization algorithm is called the Hybrid Dolphin Swarm-based Mother Optimization Algorithm (HDS-MOA). The HDS-MOA considers the objective function by considering constraints like throughput, latency, resource usage, and active servers during the data allocation process. While considering data reallocation process, the developed HDS-MOA algorithm is also performed by considering the multi-objective constraints like cost, makespan, and energy. The diverse experimental test is conducted to prove its effectiveness by comparing it with other existing methods for storing data efficiently across cloud networks. <em>Major findings of results:</em> In the configuration 3, the proposed HDS-MOA attains 31.11 %, 55.71 %, 55.71 %, and 68.21 % enhanced than the OSSperf, queuing theory, scheduling technique, and Monte Carlo-PSO based on the latency analysis. <em>Overview of Interpretations and Conclusions:</em> The developed HDS-MOA assured the better performance on the data is preserved in the optimal locations having appropriate access time and less latency that is highly essential for the cloud object storage. This supports to enhance the overall user experience by boosting the data retrieval. <em>Limitations of this Study with Solutions:</em> The ability of the proposed algorithm needs to enhance on balancing the multiple objectives such as performance, cost, and fault tolerance for optimally performing the operations in real-time that makes the system to be more efficient as well as responsive in the dynamic variations in the demand.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"159 ","pages":"Article 102448"},"PeriodicalIF":2.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling process durations with gamma mixtures for right-censored data: Applications in customer clustering, pattern recognition, drift detection, and rationalisation","authors":"Lingkai Yang ,&nbsp;Sally McClean ,&nbsp;Kevin Burke ,&nbsp;Mark Donnelly ,&nbsp;Kashaf Khan","doi":"10.1016/j.datak.2025.102430","DOIUrl":"10.1016/j.datak.2025.102430","url":null,"abstract":"<div><div>Customer modelling, particularly concerning length of stay or process duration, is vital for identifying customer patterns and optimising business processes. Recent advancements in computing and database technologies have revolutionised statistics and business process analytics by producing heterogeneous data that reflects diverse customer behaviours. Different models should be employed for distinct customer categories, culminating in an overall mixture model. Furthermore, some customers may remain “alive” at the conclusion of the observation period, meaning their journeys are incomplete, resulting in right-censored (RC) duration data. This combination of heterogeneous and right-censored data introduces complexity to process duration modelling and analysis. This paper presents a general approach to modelling process duration data using a gamma mixture model, where each gamma distribution represents a specific customer pattern. The model is adapted to account for RC data by modifying the likelihood function during model fitting. The paper explores three key application scenarios: (1) offline pattern clustering, which categorises customers who have completed their journeys; (2) online pattern tracking, which monitors and predicts customer behaviours in real-time; and (3) concept drift detection and rationalisation, which identifies shifts in customer patterns and explains their underlying causes. The proposed method has been validated using synthetically generated data and real-world data from a hospital billing process. In all instances, the fitted models effectively represented the data and demonstrated strong performance across the three application scenarios.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"158 ","pages":"Article 102430"},"PeriodicalIF":2.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CriMOnto: A generalized domain-specific ontology for modeling procedural norms of the Lebanese criminal law","authors":"Mirna El Ghosh ,&nbsp;Hala Naja ,&nbsp;Habib Abdulrab ,&nbsp;Mohamad Khalil","doi":"10.1016/j.datak.2025.102419","DOIUrl":"10.1016/j.datak.2025.102419","url":null,"abstract":"<div><div>Criminal (or penal) law regulates offenses, offenders, and legal punishments. Modeling criminal law is gaining much attention in the ontology engineering community. However, a significant aspect is neglected: the explicit representation of procedural knowledge. Procedural norms, such as regulative norms, are addressed to agents in the normative system. They govern the different interactions among these agents. In this study, we propose a formal and faithful representation of the procedural aspect of legal norms in the context of the Lebanese Criminal Code. A modular domain-specific ontology named CriMOnto is developed for this purpose. CriMOnto is grounded in the Unified Foundational Ontology (UFO) and the legal core ontology UFO-L by applying the Ontology-Driven Conceptual Modeling (ODCM) process. Conceptual Ontology Patterns (COPs) are reused from UFO and UFO-L to build the hierarchical and procedural content of the ontology. CriMOnto is validated as a formal ontology and evaluated using a dual evaluation approach. The potential use of CriMOnto for lightweight rule-based decision support is discussed in this study.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"158 ","pages":"Article 102419"},"PeriodicalIF":2.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}