Data & Knowledge Engineering最新文献_第3页

Supporting Sound Multi-Level Modeling—Specification and Implementation of a Multi-Dimensional Modeling Approach 支持健全的多层次建模——多维建模方法的规范与实现

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-19 DOI: 10.1016/j.datak.2025.102481

Thomas Kühne , Manfred A. Jeusfeld

引用次数: 0

Inference-based schema discovery for RDF data RDF数据的基于推理的模式发现

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-19 DOI: 10.1016/j.datak.2025.102491

Redouane Bouhamoum , Zoubida Kedad , Stéphane Lopes

{"title":"Inference-based schema discovery for RDF data","authors":"Redouane Bouhamoum , Zoubida Kedad , Stéphane Lopes","doi":"10.1016/j.datak.2025.102491","DOIUrl":"10.1016/j.datak.2025.102491","url":null,"abstract":"<div><div>The Semantic Web represents a huge information space where an increasing number of datasets, described in RDF, are made available to users and applications. In this context, the data is not constrained by a predefined schema. In RDF datasets, the schema may be incomplete or even missing. While this offers high flexibility in creating data sources, it also makes their use difficult. Several works have addressed the problem of automatic schema discovery for RDF datasets, but existing approaches rely only on the explicit information provided by the data source, which may limit the quality of the results. Indeed, in an RDF data source, an entity is described by explicitly declared properties, but also by implicit properties that can be derived using reasoning rules. These implicit properties are not considered by existing schema discovery approaches.</div><div>In this work, we propose a first contribution towards a hybrid schema discovery approach capable of exploiting all the semantics of a data source, which is represented not only by the explicitly declared triples, but also by the ones that can be inferred through reasoning. By considering both explicit and implicit properties, the quality of the generated schema is improved. We provide a scalable design of our approach to enable the processing of large RDF data sources while improving the quality of the results. We present some experiments which demonstrate the efficiency of our proposal and the quality of the discovered schema.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102491"},"PeriodicalIF":2.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670538","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}

引用次数: 0

Data and knowledge engineering: Insights from forty years of publication 数据和知识工程：来自出版四十年的见解

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-17 DOI: 10.1016/j.datak.2025.102492

Jacky Akoka , Isabelle Comyn-Wattiau , Nicolas Prat , Veda C. Storey

{"title":"Data and knowledge engineering: Insights from forty years of publication","authors":"Jacky Akoka , Isabelle Comyn-Wattiau , Nicolas Prat , Veda C. Storey","doi":"10.1016/j.datak.2025.102492","DOIUrl":"10.1016/j.datak.2025.102492","url":null,"abstract":"<div><div>The journal, <em>Data and Knowledge Engineering (DKE),</em> first published by Elsevier in 1985, has now been in existence for forty years. This journal has evolved and matured to play an important role in establishing and progressing research on conceptual modeling and related areas. To accurately characterize the history and current state of the research contributions and their impact, we analyze its publications in three phases, by employing bibliometric techniques of co-citation, bibliographic coupling, main path analysis, and topic modeling. Using descriptive bibliometrics, the results from the first phase provide an overview of the articles that have been published in the journal. It analyzes the dynamics and trend patterns of publications, specifically, their main topics and contributions. Using bibliometric mapping, the second phase identifies the journal's intellectual structure, its primary research themes, and the pathways through which knowledge is disseminated between the most influential articles. The third phase entails a comparison of DKE with other scientific journals that share at least some of its scope. In addition to delineating the strengths of DKE, we provide insights into how DKE might continue to evolve and progress the contributions to the field.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102492"},"PeriodicalIF":2.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780864","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}

引用次数: 0

Detecting and repairing anomaly patterns in business process event logs 检测和修复业务流程事件日志中的异常模式

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-16 DOI: 10.1016/j.datak.2025.102488

Jonghyeon Ko , Marco Comuzzi , Fabrizio Maria Maggi

{"title":"Detecting and repairing anomaly patterns in business process event logs","authors":"Jonghyeon Ko , Marco Comuzzi , Fabrizio Maria Maggi","doi":"10.1016/j.datak.2025.102488","DOIUrl":"10.1016/j.datak.2025.102488","url":null,"abstract":"<div><div>Event log anomaly detection and log repairing concern the identification of anomalous traces in an event log and the reconstruction of a correct trace for the anomalous ones, respectively. Trace-level anomalies in event logs often appear according to specific patterns, such events inserted, repeated, or skipped. This paper proposes P-BEAR (Pattern-Based Event Log Anomaly Reconstruction), a semi-supervised pattern-based anomaly detection and log repairing approach that exploits the pattern-based nature of trace-level anomalies in event logs. P-BEAR captures, in a set of ad-hoc graphs, the behaviour of clean traces in a log and uses these to identify anomalous traces, determine the specific anomaly pattern that applies to them, and then reconstruct the correct trace. The proposed approach is evaluated using artificial and real event logs against traditional trace alignment in conformance checking, the edit distance-based alignment method, and an unsupervised method based on deep learning. Overall, the proposed method outperforms the alignment method in anomalous trace reconstruction while providing interpretability with respect to anomaly pattern classification. P-BEAR is also quicker to execute, and its performance is more balanced across different types of anomaly patterns.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102488"},"PeriodicalIF":2.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656577","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}

引用次数: 0

Behavior Driven Development for 3D games 3D游戏的行为驱动开发

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-09 DOI: 10.1016/j.datak.2025.102486

Fernando Pastor Ricós , Beatriz Marín , I.S.W.B. Prasetya , Tanja E.J. Vos , Joseph Davidson , Karel Hovorka

{"title":"Behavior Driven Development for 3D games","authors":"Fernando Pastor Ricós , Beatriz Marín , I.S.W.B. Prasetya , Tanja E.J. Vos , Joseph Davidson , Karel Hovorka","doi":"10.1016/j.datak.2025.102486","DOIUrl":"10.1016/j.datak.2025.102486","url":null,"abstract":"<div><div>Computer 3D games are complex software environments that require novel testing processes to ensure high-quality standards. The Intelligent Verification/Validation for Extended Reality Based Systems (<span>iv4XR</span>) framework addresses this need by enabling the implementation of autonomous agents to automate game testing scenarios. This framework facilitates the automation of regression test cases for complex 3D games like Space Engineers. Nevertheless, the technical expertise required to define test scripts using <span>iv4XR</span> can constrain seamless collaboration between developers and testers. This paper reports how integrating a Behavior-Driven Development (BDD) approach with the <span>iv4XR</span> framework allows the industrial company behind Space Engineers to automate regression testing. The success of this industrial collaboration has inspired the <span>iv4XR</span> team to integrate the BDD approach to improve the automation of play-testing for the experimental 3D game LabRecruits. Furthermore, the <span>iv4XR</span> framework has been extended with tactical programming to enable the automation of long-play test scenarios in Space Engineers. These results underscore the versatility of the <span>iv4XR</span> framework in supporting diverse testing approaches while showcasing how BDD empowers users to create, manage, and execute automated game tests using comprehensive and human-readable statements.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102486"},"PeriodicalIF":2.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588942","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}

引用次数: 0

Conceptual modeling: Foundations, a historical perspective, and a vision for the future 概念建模：基础、历史视角和对未来的展望

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-07 DOI: 10.1016/j.datak.2025.102483

John Mylopoulos , Giancarlo Guizzardi , Nicola Guarino

引用次数: 0

IDL-BiGRU: Integrated deep learning assisted smart scheduling of big data over cloud environment IDL-BiGRU：集成深度学习辅助云环境下大数据智能调度

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-06 DOI: 10.1016/j.datak.2025.102489

Rama Satish K V , Vibha M B , Lovely Sasidharan

{"title":"IDL-BiGRU: Integrated deep learning assisted smart scheduling of big data over cloud environment","authors":"Rama Satish K V , Vibha M B , Lovely Sasidharan","doi":"10.1016/j.datak.2025.102489","DOIUrl":"10.1016/j.datak.2025.102489","url":null,"abstract":"<div><div>The rapid expansion of Internet of Things (IoT) applications generates a continuous and massive flow of data, creating significant challenges in both data processing and storage management. Cloud computing offers scalable infrastructure to handle such data intensive workloads, but optimal task scheduling remains critical to ensure performance and resource efficiency. Traditional scheduling algorithms often fall short due to limited adaptability and consideration of only a few system parameters. In this paper, a novel integrated deep learning-assisted scheduling framework is utilized for scheduling big data over a cloud environment. The proposed framework integrated deep reinforcement learning with the bidirectional gated recurrent unit (IDL-BiGRU) model to intelligently schedule tasks based on real-time system states. The IDL-BiGRU model leverages the advantage of deep Q-learning for decision making and BiGRU's ability to capture bidirectional temporal dependencies in task and resource usage patterns. In this work, RAM, CPU, bandwidth utilization of the network, and disk storage are considered for scheduling purposes. The suggested method is to shorten the makespan and increase resource utilization. The Java tool is utilized for conducting the experimental verifications. Analysis and comparison of the suggested deep learning framework's performance with current methods are done. For 1000 tasks, the proposed method attains 0.90 degrees of imbalance, 291.17 ms downtime, 1050 ms throughput, and 721.58 makespan. The performance analysis demonstrates that the suggested strategy outperforms previous methods.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102489"},"PeriodicalIF":2.7,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711633","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}

引用次数: 0

Dirigo: A method to extract event logs for object-centric processes Dirigo：为以对象为中心的进程提取事件日志的方法

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-05 DOI: 10.1016/j.datak.2025.102485

Jia Wei , Chun Ouyang , Ying Wang , Lei Huang

{"title":"Dirigo: A method to extract event logs for object-centric processes","authors":"Jia Wei , Chun Ouyang , Ying Wang , Lei Huang","doi":"10.1016/j.datak.2025.102485","DOIUrl":"10.1016/j.datak.2025.102485","url":null,"abstract":"<div><div>Real-world processes involve multiple object types with intricate interrelationships. Traditional event logs (in XES format), which record process execution centred around the case notion, are restricted to a single-object perspective, making it difficult to capture the behaviour of multiple objects and their interactions. To address this limitation, object-centric event logs (OCEL) have been introduced to capture both the objects involved in a process and their interactions with events. The object-centric event data (OCED) metamodel extends the OCEL format by further capturing dynamic object attributes and object-to-object relations. Recently OCEL 2.0 has been proposed based on OCED metamodel. Current research on generating OCEL logs requires specific input data sources, and resulting log data often fails to fully conform to OCEL 2.0. Moreover, the generated OCEL logs vary across different representational formats and their quality remains unevaluated. To address these challenges, a set of quality criteria for evaluating OCEL log representations is established. Guided by these criteria, <em>Dirigo</em> is proposed—a method for extracting event logs that not only conforms to OCEL 2.0 but also extends it by capturing the temporal aspect of dynamic object-to-object relations. Object-role Modelling (ORM), a conceptual data modelling technique, is employed to describe the artifact produced at each step of <em>Dirigo</em>. To validate the applicability of <em>Dirigo</em>, it is applied to a real-life use case. The quality of the log representation of the extracted event log is compared to those of existing OCEL logs using the established quality criteria.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102485"},"PeriodicalIF":2.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614765","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}

引用次数: 0

Editorial introduction for special issue on research challenges and practices in conceptual modeling – ER 2023 关于概念建模的研究挑战和实践的特刊编辑导言- er2023

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-07-03 DOI: 10.1016/j.datak.2025.102487

João Paulo A Almeida , José Borbinha , Giancarlo Guizzardi , Sebastian Link , Jelena Zdravkovic

引用次数: 0

PSA-GAT: Integrating position-syntax and cross-aspect graph attention networks for aspect-based sentiment analysis PSA-GAT：整合位置语法和跨方面图注意网络，用于基于方面的情感分析

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-06-26 DOI: 10.1016/j.datak.2025.102477

Ning Zhou, Linfu Sun, Min Han, Songlin He

{"title":"PSA-GAT: Integrating position-syntax and cross-aspect graph attention networks for aspect-based sentiment analysis","authors":"Ning Zhou, Linfu Sun, Min Han, Songlin He","doi":"10.1016/j.datak.2025.102477","DOIUrl":"10.1016/j.datak.2025.102477","url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) is widely applied in analyzing user review data on web platforms to identify sentiment polarity toward specific aspects of web reviews. However, individual reviews often contain multiple conditions and coordinating and conflicting elements or relationships, which significantly increases the complexity of this task. In recent years, exploiting semantic–syntactic information with graph neural networks has been widely used to address such tasks. However, such methods overlook the features of the location influence factor of words and may provide irrelevant or even interfering noisy signals for ABSA because of the word association relationships mined by the syntax tree and semantic composition tree. To alleviate the effect of noise information and fully strengthen the context for multiple-aspect representation in ABSA, we propose a new framework, PSA-GAT, that mines information on position importance, syntactic–semantic dependencies and cross-aspect correlations. Overall, the structural features of the multi-aspect sentiment set are learned by using various variations of graph neural networks. Moreover, the experimental results on four real-world datasets demonstrate the effectiveness of PSA-GAT compared to state-of-the-art methods. The code is available at <span><span>https://github.com/zhouning6000/PSA_GAT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102477"},"PeriodicalIF":2.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534819","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}

引用次数: 0