Data & Knowledge Engineering最新文献_第3页

Quality matters: A decadal systematic exploration of data quality in IoT environment 质量至关重要：物联网环境下数据质量的十年系统探索

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-11-20 DOI: 10.1016/j.datak.2025.102533

Tarandeep Kaur, Pankaj Deep Kaur

{"title":"Quality matters: A decadal systematic exploration of data quality in IoT environment","authors":"Tarandeep Kaur, Pankaj Deep Kaur","doi":"10.1016/j.datak.2025.102533","DOIUrl":"10.1016/j.datak.2025.102533","url":null,"abstract":"<div><div>The proliferation of the Internet of Things (IoT) has led to an unprecedented surge in data generation, making the enhancement of data quality indispensable for unlocking IoT's full potential and enabling intelligent, data-driven decision-making. This systematic literature review examines scholarly research from the past decade to unravel the complexities of data quality in IoT. Seven research questions have been formulated, an extensive search of relevant academic databases has been conducted, and criteria for inclusion and exclusion have been defined. Key insights from these selected studies address the research questions, analyzing the multi-faceted landscape of IoT data and its quality. This study explores various data quality dimensions that play a pivotal role in assessing overall data quality, identifying critical gaps and limitations, and offering a roadmap for future research. The comprehensive overview provides a nuanced understanding of the factors influencing data quality and highlights the contributions of various researchers to ensure data quality. The study consolidates perspectives on the significance of data quality as perceived by users, while also emphasizing the paramount importance of security and privacy in managing IoT data. The findings of this SLR will provide valuable insights to researchers and practitioners, advancing efforts to maintain robust data quality across IoT ecosystems.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102533"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684460","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

S3: A simple strong sample-effective multimodal dialog system S3：一个简单的强样本有效的多模式对话系统

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-11-25 DOI: 10.1016/j.datak.2025.102538

Elisei Rykov , Alexander Panchenko

引用次数: 0

All-words pronunciation estimation of Japanese homographs 日语同音异义词的全词发音估计

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.datak.2025.102537

Kanako Komiya , Taichiro Kobayashi , Masayuki Asahara , Hiroyuki Shinnou

{"title":"All-words pronunciation estimation of Japanese homographs","authors":"Kanako Komiya , Taichiro Kobayashi , Masayuki Asahara , Hiroyuki Shinnou","doi":"10.1016/j.datak.2025.102537","DOIUrl":"10.1016/j.datak.2025.102537","url":null,"abstract":"<div><div>The Japanese language has many homographs, which are words that share the same kanji characters, regardless of their pronunciations. Therefore, pronunciation estimation of homographs is necessary to read Japanese sentences accurately. We developed a system to estimate the pronunciations of homographs using a Bidirectional Encoder Representations from the Transformer model. This is the first research paper on pronunciation estimation of all homographs and we achieved this goal using the technique for all-word word sense disambiguation. We used the Corpus of Spontaneous Japanese (CSJ), a transcription of spoken Japanese, as the test data and utilized the non-core data of the Balanced Corpus of Contemporary Written Japanese, for which pronunciations are automatically tagged by a Japanese morphological analyzer, in addition to CSJ, as training data to reduce the cost of transcription. We also investigated the case where pseudo-pronunciation data was assigned to CSJ using a morphological analyzer as training examples. We show that automatically tagged data can improve the accuracy of pronunciation estimation.</div><div>Additionally, to evaluate an all-words pronunciation estimation system, we developed a dataset through crowdsourcing. We asked 20 crowdworkers to select pronunciations for the sentences from the Nihon Keizai Shimbun newspaper (the NIKKEI). For this NIKKEI data, multiple correct pronunciations were allowed, and the answer provided by the majority of crowdworkers was treated as the correct answer for evaluation purposes. When comparing the model trained on pseudo-data from BCCWJ with the model trained on pseudo-data from CSJ, the model using BCCWJ pseudo-data demonstrated superior performance.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102537"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790026","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

Optimized adaptive depression state prediction and severity estimation from twitter data 基于twitter数据的抑郁状态优化自适应预测和严重程度估计

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1016/j.datak.2025.102532

Pavani Chirasani , Gatram Rama Mohan Babu

{"title":"Optimized adaptive depression state prediction and severity estimation from twitter data","authors":"Pavani Chirasani , Gatram Rama Mohan Babu","doi":"10.1016/j.datak.2025.102532","DOIUrl":"10.1016/j.datak.2025.102532","url":null,"abstract":"<div><div>Social media sites like Twitter, which offer an abundance of content supplemented with emojis, can be used to identify and treat depression, a widespread mental health issue. Several prediction methods exist to predict the depression. However, the relevant outcome was not good because of inaccurate prediction. The input Twitter emoji data contains more error features, which increases the complexity of predicting depression. These drawbacks resulted in poor prediction and low Accuracy. So, the proposed work aims to design a novel Zebra-based Long former Emoji Analysis (ZLEA) for predicting depression. The Twitter Emoji database was initially collected from the standard website and provided to the Python environment as input. First, the pre-processing function is run to remove the noisy features that are present in the trained database. Moreover, the necessary features were extracted and the depression condition was predicted using the current emoji. Finally, the depression severe state was assessed based on the emoji's grade level, and the performance was confirmed with other conventional research with metrics.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102532"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617823","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

Exploring cutting-edge data ecosystems: A comprehensive analysis 探索前沿数据生态系统：综合分析

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.datak.2025.102539

Ioannis Chrysakis , David Chaves-Fraga , Giorgos Flouris , Erik Mannens , Anastasia Dimou

{"title":"Exploring cutting-edge data ecosystems: A comprehensive analysis","authors":"Ioannis Chrysakis , David Chaves-Fraga , Giorgos Flouris , Erik Mannens , Anastasia Dimou","doi":"10.1016/j.datak.2025.102539","DOIUrl":"10.1016/j.datak.2025.102539","url":null,"abstract":"<div><div>Data-driven innovation has recently changed the mindset in data sharing from centralized architectures and monolithic data exploitation by data providers (data platforms) to decentralized architectures and different data sharing options among all involved participants (data ecosystems). Data sharing is further strengthened through the establishment of several legal frameworks (e.g., European Strategy for Data, Data Act, Data Governance Act) and the emerging initiatives that provide the means to build data ecosystems, which is evident in the formulated communities, established use cases, and the technical solutions. However, the data ecosystems have not been thoroughly studied so far. The differences between the various data ecosystems are not clear, making it hard to choose the most suitable for each use case, negatively impacting their adoption. Since the domain is growing fast, a review of the state-of-the-art data ecosystem initiatives is needed to analyze what each initiative offers, identify collaboration prospects, and highlight features for improvement and open research topics. In this paper, we review the state-of-the-art data ecosystem initiatives, describe their innovative aspects, compare their technical and business features, and identify open research challenges. We aim to assist practitioners in choosing the most suitable data ecosystem for their use cases and scientists to explore emerging research opportunities. Furthermore, we will provide a framework that outlines the key criteria for evaluating these initiatives, ensuring that stakeholders can make informed decisions based on their specific needs and objectives. By synthesizing our findings, we hope to foster a deeper understanding of the evolving landscape of data ecosystems and encourage further advancements in this critical field.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102539"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736659","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

Human vs. Automated data annotation: Labeling the data set for an ML-driven support ticket classifier 人工与自动数据注释：为ml驱动的支持票证分类器标记数据集

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-11-23 DOI: 10.1016/j.datak.2025.102534

Simon Fuchs, Janik Schnellbach, Holger Wittges, Helmut Krcmar

{"title":"Human vs. Automated data annotation: Labeling the data set for an ML-driven support ticket classifier","authors":"Simon Fuchs, Janik Schnellbach, Holger Wittges, Helmut Krcmar","doi":"10.1016/j.datak.2025.102534","DOIUrl":"10.1016/j.datak.2025.102534","url":null,"abstract":"<div><div>In general, supervised Machine Learning approaches using labeled training data currently promise the best classification results with respect to classification accuracy. Therefore, data annotation is a key component of most Machine Learning projects implemented. However, creating labels for a training data set is often an elaborate project involving arduous and repetitive work, which is why data scientists often try to minimize the effort for data annotation by automating the data annotation process itself. In this paper, we present a case study of two data annotation projects on the same data set of support tickets and compare these: one using human annotators and the other using algorithmic Learning Functions in a combination of Active Learning and Weak Supervision. Here, we achieved a weighted confidence score of >94 % for the human-created labels, while also achieving up to 92 % agreement between the labels of our automated project and the labels created by human annotators, with the need for only 10 % of human annotation as the starting input of the automated approach. Additionally, we were able to reproduce the value of 85 % for initial human classification accuracy in support ticket distribution from previous papers. We close with a reflection about the worth of business understanding in data annotation projects and the problem and proposed solutions to ticket ambiguity.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102534"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684462","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

Enhancing clustering stability, compactness, and separation in multimodal data environments 增强多模态数据环境中的聚类稳定性、紧凑性和分离性

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-03-01 Epub Date: 2025-11-26 DOI: 10.1016/j.datak.2025.102536

Fillipe dos Santos Silva , Júlio Cesar dos Reis , Marcelo da Silva Reis

{"title":"Enhancing clustering stability, compactness, and separation in multimodal data environments","authors":"Fillipe dos Santos Silva , Júlio Cesar dos Reis , Marcelo da Silva Reis","doi":"10.1016/j.datak.2025.102536","DOIUrl":"10.1016/j.datak.2025.102536","url":null,"abstract":"<div><div>Effective customer segmentation, crucial for tailored marketing strategies, relies on stable and distinct clustering methods. Traditional clustering approaches often focus on structured data, limiting their effectiveness when handling multimodal information. This study originally introduces a multimodal framework to enhance clustering stability, compactness, and separation by integrating categorical, numerical, and textual data. Our framework addresses existing limitations through three core components: a transformer-based embedding model for textual analysis, a data fusion layer for integrating diverse data types, and a generative model for refining cluster consistency. We rigorously assess the effectiveness of our framework using five stability metrics: Adjusted Rand Index (ARI), Adjusted Mutual Information Score (AMIS), BagClust (BG), Hierarchical Agglomerative Nesting (HAN), and Optimal Transport Alignment (OTA). Additionally, we use the Davies–Bouldin Score (DBS) to evaluate cluster compactness and separation. Real-world datasets (Yelp, Melbourne Airbnb, PetFinder.my, Women’s Clothing Reviews) were used to benchmark our approach against four existing methods. Results demonstrate that our framework achieves superior clustering stability, compactness, and separation, advancing multimodal learning for more nuanced customer segmentation.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102536"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618297","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

A graph-based model for semantic textual similarity measurement 基于图的语义文本相似度度量模型

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-01-01 Epub Date: 2025-09-12 DOI: 10.1016/j.datak.2025.102509

Van-Tan Bui , Quang-Minh Nguyen , Van-Vinh Nguyen , Duc-Toan Nguyen

{"title":"A graph-based model for semantic textual similarity measurement","authors":"Van-Tan Bui , Quang-Minh Nguyen , Van-Vinh Nguyen , Duc-Toan Nguyen","doi":"10.1016/j.datak.2025.102509","DOIUrl":"10.1016/j.datak.2025.102509","url":null,"abstract":"<div><div>Measuring semantic similarity between sentence pairs is a fundamental problem in Natural Language Processing with applications in various domains, including machine translation, speech recognition, automatic question answering, and text summarization. Despite its significance, accurately assessing semantic similarity remains a challenging task, particularly for underrepresented languages such as Vietnamese. Existing methods have yet to fully leverage the unique linguistic characteristics of Vietnamese for semantic similarity measurement. To address this limitation, we propose GBNet-STS (Graph-Based Network for Semantic Textual Similarity), a novel framework for measuring the semantic similarity of Vietnamese sentence pairs. GBNet-STS integrates lexical-grammatical similarity scores and distributional semantic similarity scores within a multi-layered graph-based model. By capturing different semantic perspectives through multiple interconnected layers, our approach provides a more comprehensive and robust similarity estimation. Experimental results demonstrate that GBNet-STS outperforms traditional methods, achieving state-of-the-art performance in Vietnamese semantic similarity tasks.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102509"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060462","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

Secure data storage in multi-cloud environments using lattice-based saber with Diffie-Hellman cryptography and authenticate based on PUF-ECC 在多云环境中使用基于格子的军刀与Diffie-Hellman加密和基于PUF-ECC的身份验证来保护数据存储

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-01-01 Epub Date: 2025-09-16 DOI: 10.1016/j.datak.2025.102512

R. Iyswarya , R. Anitha

{"title":"Secure data storage in multi-cloud environments using lattice-based saber with Diffie-Hellman cryptography and authenticate based on PUF-ECC","authors":"R. Iyswarya , R. Anitha","doi":"10.1016/j.datak.2025.102512","DOIUrl":"10.1016/j.datak.2025.102512","url":null,"abstract":"<div><div>Human life has become highly dependent on data in recent decades almost every facet of daily activities, leading to its storage in multi-cloud environments. To ensure data integrity, confidentiality, and privacy, it is essential to protect data from unauthorized access. This paper proposes a novel approach for securing data in multi-cloud environments for user authentication and data storage using Lattice-Based Saber Cryptography combined with PUF-ECC and the Enhanced Goose Optimization Algorithm (EGOA). The initial user authentication is achieved through the PUF-ECC digital signature algorithm, which verifies both the user's and the device's identity. Once authenticated, user data is securely transmitted to the cloud server based on Lattice-Based Saber post-quantum cryptography combined with the Diffie-Hellman key exchange protocol. The encrypted data is then stored across multiple cloud storage through a cloud controller using RAM-based chunking. For efficient data retrieval, the Enhanced Goose Optimization Algorithm (EGOA) is employed to extract encrypted data from clouds. Finally, the data is decrypted using the Lattice-Based Saber decryption algorithm and securely retrieved by the authenticated user. This method enhances both the security and efficiency of cloud data management and retrieval. The experiment is carried out with the proposed methodologies and also compared with the existing technologies. The proposed approach achieves encryption times of 9.68 ms, key generation times of 4.84 ms, and block creation times of 1.59 ms, while maintaining a 93.7 % confidentiality rate, a 98 % packet delivery ratio, a transmission delay of 0.026 ms, throughput of 407.33 MB/s, jitter of 3.26 ms, and an RTT of 0.17 ms, demonstrating its effectiveness in secure data storage and retrieval in multi-cloud environments.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102512"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158079","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

An integrated requirements framework for analytical and AI projects 分析和人工智能项目的集成需求框架

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2026-01-01 Epub Date: 2025-09-06 DOI: 10.1016/j.datak.2025.102493

Juan Trujillo , Ana Lavalle , Alejandro Reina-Reina , Jorge García-Carrasco , Alejandro Maté , Wolfgang Maaß

{"title":"An integrated requirements framework for analytical and AI projects","authors":"Juan Trujillo , Ana Lavalle , Alejandro Reina-Reina , Jorge García-Carrasco , Alejandro Maté , Wolfgang Maaß","doi":"10.1016/j.datak.2025.102493","DOIUrl":"10.1016/j.datak.2025.102493","url":null,"abstract":"<div><div>To this day, the requirements of data warehouses, user visualizations and ML projects have been tackled in an independent manner, ignoring the possible cross-requirements, collective constraints and dependencies between the outputs of the different systems that should be taken into account to ensure a successful analytical project. In this work, we take a holistic approach and propose a methodology that supports modeling and subsequent analysis while taking into account these three aspects. This methodology has several advantages, mainly that (i) it enables us to identify possible conflicts between actors on different tasks that are overlooked if the systems are treated in an isolated manner and (ii) this holistic view enables modeling multi-company systems, where the information or even the analytical results can be provided by third-parties, identifying key participants in federated environments. After presenting the required formalism to carry out this kind of analysis, we showcase it on a real-world running example of the tourism sector.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102493"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094812","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