Data & Knowledge Engineering最新文献_第10页

Business intelligence and cognitive loads: Proposition of a dashboard adoption model 商业智能与认知负荷：仪表盘应用模型的提出

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-11 DOI: 10.1016/j.datak.2024.102310

Corentin Burnay, Mathieu Lega, Sarah Bouraga

{"title":"Business intelligence and cognitive loads: Proposition of a dashboard adoption model","authors":"Corentin Burnay, Mathieu Lega, Sarah Bouraga","doi":"10.1016/j.datak.2024.102310","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102310","url":null,"abstract":"<div><p>Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Data-driven Decision Support Systems (data-driven DSS), and more specifically business intelligence (BI) intend to achieve this. Organizations invest massively in the development of BI data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of BI dashboards, which can be seen as the visible tip of the BI data-driven DSS iceberg and which play a major role in the adoption of the entire system. In this paper, the dashboard content is investigated as one possible root cause for BI data-driven DSS dashboard adoption or rejection through an early empirical research. More precisely, this work is composed of three parts. In the first part, the concept of cognitive loads is studied in the context of BI dashboards and the informational, the representational and the non-informational loads are introduced. In the second part, the effects of these loads on the adoption of BI dashboards are then studied through an experiment with 167 respondents and a Structural Equation Modeling (SEM) analysis. The result is a Dashboard Adoption Model, enriching the seminal Technology Acceptance Model with new content-oriented variables to support the design of more supportive BI data-driven DSS dashboards. Finally, in the third part, a set of indicators is proposed to help dashboards designers in the monitoring of the loads of their dashboards practically.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"152 ","pages":"Article 102310"},"PeriodicalIF":2.5,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951807","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

Machine learning for predicting off-block delays: A case study at Paris — Charles de Gaulle International Airport 预测非阻塞延误的机器学习：巴黎戴高乐国际机场案例研究

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-08 DOI: 10.1016/j.datak.2024.102303

Thibault Falque , Bertrand Mazure , Karim Tabia

引用次数: 0

To prompt or not to prompt: Navigating the use of Large Language Models for integrating and modeling heterogeneous data 提示还是不提示？使用大型语言模型对异构数据进行整合和建模的导航

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-06 DOI: 10.1016/j.datak.2024.102313

Adel Remadi , Karim El Hage , Yasmina Hobeika , Francesca Bugiotti

{"title":"To prompt or not to prompt: Navigating the use of Large Language Models for integrating and modeling heterogeneous data","authors":"Adel Remadi , Karim El Hage , Yasmina Hobeika , Francesca Bugiotti","doi":"10.1016/j.datak.2024.102313","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102313","url":null,"abstract":"<div><p>Manually integrating data of diverse formats and languages is vital to many artificial intelligence applications. However, the task itself remains challenging and time-consuming. This paper highlights the potential of Large Language Models (LLMs) to streamline data extraction and resolution processes. Our approach aims to address the ongoing challenge of integrating heterogeneous data sources, encouraging advancements in the field of data engineering. Applied on the specific use case of learning disorders in higher education, our research demonstrates LLMs’ capability to effectively extract data from unstructured sources. It is then further highlighted that LLMs can enhance data integration by providing the ability to resolve entities originating from multiple data sources. Crucially, the paper underscores the necessity of preliminary data modeling decisions to ensure the success of such technological applications. By merging human expertise with LLM-driven automation, this study advocates for the further exploration of semi-autonomous data engineering pipelines.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"152 ","pages":"Article 102313"},"PeriodicalIF":2.5,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000375/pdfft?md5=11ee9c76542d55fac49075892a9a8c7d&pid=1-s2.0-S0169023X24000375-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140918204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Berlin SPARQL benchmark to evaluate virtual SPARQL endpoints over relational databases 使用柏林 SPARQL 基准评估关系数据库上的虚拟 SPARQL 端点

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-03 DOI: 10.1016/j.datak.2024.102309

Milos Chaloupka, Martin Necasky

{"title":"Using Berlin SPARQL benchmark to evaluate virtual SPARQL endpoints over relational databases","authors":"Milos Chaloupka, Martin Necasky","doi":"10.1016/j.datak.2024.102309","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102309","url":null,"abstract":"<div><p>The RDF is a popular and well-documented format for publishing structured data on the web. It enables data to be consumed without the knowledge of how the data is internally stored. There are already several native RDF storage solutions that provide a SPARQL endpoint. However, native RDF stores are not widely adopted. It is still more common to store data in a relational database. One of the useful features of native RDF storage solutions is providing a SPARQL endpoint, a web service to query RDF data with SPARQL. To provide this feature also on top of prevalent relational databases, solutions for virtual SPARQL endpoints on top of a relational database have appeared. To benchmark these solutions, a state-of-the-art tool, the Berlin SPARQL Benchmark (BSBM), is used. However, BSBM was designed primarily to benchmark native RDF stores. It can also be used to benchmark solutions for virtual SPARQL endpoints. However, since BSBM was not designed for virtual SPARQL endpoints, each implementation uses that tool differently for evaluation. As a result, the evaluation is not consistent and therefore hardly comparable. In this paper, we demonstrate how this well-defined benchmarking tool for SPARQL endpoints can be used to evaluate virtual endpoints over relational databases, perform the evaluation on the available implementations, and provide instructions on how to repeat the same evaluation in the future.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"152 ","pages":"Article 102309"},"PeriodicalIF":2.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905621","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

Effective text classification using BERT, MTM LSTM, and DT 使用 BERT、MTM LSTM 和 DT 进行有效的文本分类

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-01 DOI: 10.1016/j.datak.2024.102306

Saman Jamshidi , Mahin Mohammadi , Saeed Bagheri , Hamid Esmaeili Najafabadi , Alireza Rezvanian , Mehdi Gheisari , Mustafa Ghaderzadeh , Amir Shahab Shahabi , Zongda Wu

{"title":"Effective text classification using BERT, MTM LSTM, and DT","authors":"Saman Jamshidi , Mahin Mohammadi , Saeed Bagheri , Hamid Esmaeili Najafabadi , Alireza Rezvanian , Mehdi Gheisari , Mustafa Ghaderzadeh , Amir Shahab Shahabi , Zongda Wu","doi":"10.1016/j.datak.2024.102306","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102306","url":null,"abstract":"<div><p>Text classification plays a critical role in managing large volumes of electronically produced texts. As the number of such texts increases, manual analysis becomes impractical, necessitating an intelligent approach for processing information. Deep learning models have witnessed widespread application in text classification, including the use of recurrent neural networks like Many to One Long Short-Term Memory (MTO LSTM). Nonetheless, this model is limited by its reliance on only the last token for text labelling. To overcome this limitation, this study introduces a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT), Many To Many Long Short-Term Memory (MTM LSTM), and Decision Templates (DT) for text classification. In this new model, the text is first embedded using the BERT model and then trained using MTM LSTM to approximate the target at each token. Finally, the approximations are fused using DT. The proposed model is evaluated using the well-known IMDB movie review dataset for binary classification and Drug Review Dataset for multiclass classification. The results demonstrate superior performance in terms of accuracy, recall, precision, and F1 score compared to previous models. The hybrid model presented in this study holds significant potential for a wide range of text classification tasks and stands as a valuable contribution to the field.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"151 ","pages":"Article 102306"},"PeriodicalIF":2.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825257","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

Evaluating Transformers and Linguistic Features integration for Author Profiling tasks in Spanish 评估转换器和语言特征整合在西班牙语作者分析任务中的应用

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-01 DOI: 10.1016/j.datak.2024.102307

José Antonio García-Díaz , Ghassan Beydoun , Rafel Valencia-García

{"title":"Evaluating Transformers and Linguistic Features integration for Author Profiling tasks in Spanish","authors":"José Antonio García-Díaz , Ghassan Beydoun , Rafel Valencia-García","doi":"10.1016/j.datak.2024.102307","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102307","url":null,"abstract":"<div><p>Author profiling consists of extracting their demographic and psychographic information by examining their writings. This information can then be used to improve the reader experience and to detect bots or propagators of hoaxes and/or hate speech. Therefore, author profiling can be applied to build more robust and efficient Knowledge-Based Systems for tasks such as content moderation, user profiling, and information retrieval. Author profiling is typically performed automatically as a document classification task. Recently, language models based on transformers have also proven to be quite effective in this task. However, the size and heterogeneity of novel language models, makes it necessary to evaluate them in context. The contributions we make in this paper are four-fold: First, we evaluate which language models are best suited to perform author profiling in Spanish. These experiments include basic, distilled, and multilingual models. Second, we evaluate how feature integration can improve performance for this task. We evaluate two distinct strategies: knowledge integration and ensemble learning. Third, we evaluate the ability of linguistic features to improve the interpretability of the results. Fourth, we evaluate the performance of each language model in terms of memory, training, and inference times. Our results indicate that the use of lightweight models can indeed achieve similar performance to heavy models and that multilingual models are actually less effective than models trained with one language. Finally, we confirm that the best models and strategies for integrating features ultimately depend on the context of the task.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"151 ","pages":"Article 102307"},"PeriodicalIF":2.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000314/pdfft?md5=42a482dbed2e2a640c46e89a6f3a69c8&pid=1-s2.0-S0169023X24000314-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Managerial risk data analytics applications using grey influence analysis (GINA) 利用灰色影响分析（GINA）进行管理风险数据分析应用

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-05-01 DOI: 10.1016/j.datak.2024.102312

R. Rajesh

{"title":"Managerial risk data analytics applications using grey influence analysis (GINA)","authors":"R. Rajesh","doi":"10.1016/j.datak.2024.102312","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102312","url":null,"abstract":"<div><p>We observe and analyze the causal relations among risk factors in a system, considering the manufacturing supply chains. Seven major categories of risks were identified and scrutinized and the detailed analysis of causal relations using the grey influence analysis (GINA) methodology is outlined. With expert response based survey, we conduct an initial analysis of the risks using risk matrix analysis (RMA) and the risks under high priority are identified. Later, the GINA is implemented to understand the causal relations among various categories of risks, which is particularly useful in group decision-making environments. The results from RMA concludes that the <em>capacity risks (CR)</em> and <em>delays (DL)</em> are in the category of very high priority risks. GINA results also ratify the conclusions from RMA and observes that managers need to control and manage <em>capacity risks (CR)</em> and <em>delays (DL)</em> with high priorities. Additionally from the results of GINA, the causal factors <em>disruptions (DS)</em> and <em>forecast risks (FR)</em> appear to be primary importance and if unattended can lead to the initiation of several other risks in supply chains. Managers are recommended to identify disruptions at an early stage in supply chains and reduce the forecast errors to avoid bullwhips in supply chains.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"151 ","pages":"Article 102312"},"PeriodicalIF":2.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140879377","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 named entity disambiguation using clique partitioning and semantic relatedness 利用小块分割和语义相关性进行基于图的命名实体消歧

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-04-30 DOI: 10.1016/j.datak.2024.102308

Ramla Belalta , Mouhoub Belazzoug , Farid Meziane

{"title":"A graph based named entity disambiguation using clique partitioning and semantic relatedness","authors":"Ramla Belalta , Mouhoub Belazzoug , Farid Meziane","doi":"10.1016/j.datak.2024.102308","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102308","url":null,"abstract":"<div><p>Disambiguating name mentions in texts is a crucial task in Natural Language Processing, especially in entity linking. The credibility and efficiency of such systems depend largely on this task. For a given name entity mention in a text, there are many potential candidate entities that may refer to it in the knowledge base. Therefore, it is very difficult to assign the correct candidate from the whole set of candidate entities of this mention. To solve this problem, collective entity disambiguation is a prominent approach. In this paper, we present a novel algorithm called CPSR for collective entity disambiguation, which is based on a graph approach and semantic relatedness. A clique partitioning algorithm is used to find the best clique that contains a set of candidate entities. These candidate entities provide the answers to the corresponding mentions in the disambiguation process. To evaluate our algorithm, we carried out a series of experiments on seven well-known datasets, namely, AIDA/CoNLL2003-TestB, IITB, MSNBC, AQUAINT, ACE2004, Cweb, and Wiki. The Kensho Derived Wikimedia Dataset (KDWD) is used as the knowledge base for our system. From the experimental results, our CPSR algorithm outperforms both the baselines and other well-known state-of-the-art approaches.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"152 ","pages":"Article 102308"},"PeriodicalIF":2.5,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140901817","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

CQuAE: A new Contextualized QUestion Answering corpus on Education domain CQuAE：新的教育领域语境化问题解答语料库

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-04-15 DOI: 10.1016/j.datak.2024.102305

Thomas Gerald , Louis Tamames , Sofiane Ettayeb , Ha-Quang Le , Patrick Paroubek , Anne Vilnat

{"title":"CQuAE: A new Contextualized QUestion Answering corpus on Education domain","authors":"Thomas Gerald , Louis Tamames , Sofiane Ettayeb , Ha-Quang Le , Patrick Paroubek , Anne Vilnat","doi":"10.1016/j.datak.2024.102305","DOIUrl":"10.1016/j.datak.2024.102305","url":null,"abstract":"<div><p>Generating education-related questions and answers remains an open issue while being useful for students, teachers, and teaching aids. Given textual course material, we are interested in generating non-factual questions that require an elaborate answer (relying on analysis or reasoning). Despite the availability of annotated corpora of questions and answers, the effort to develop a generator using deep learning faces two main challenges. Firstly, freely accessible and qualitative data are insufficient to train generative approaches. Secondly, for a stand-alone application, we do not have explicit support to guide the generation toward complex questions. To tackle the first issue, we propose a new corpus based on education documents. For the second point, we propose to study several retargetable language algorithms to produce answers by extracting text spans from contextual documents to help the generation of questions. We particularly study the contribution of deep neural syntactic parsing and transformer-based semantic representation, taking into account the question type (according to our specific question typology) and the contextual support text span. Additionally, recent advances in generation models have proven the efficiency of the instruction-based approach for natural language generation. Consequently, we propose a first investigation of very large language models to generate questions related to the education domain.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"151 ","pages":"Article 102305"},"PeriodicalIF":2.5,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140768347","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

Artificial intelligence in digital twins—A systematic literature review 数字双胞胎中的人工智能--系统文献综述

IF 2.5 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-04-03 DOI: 10.1016/j.datak.2024.102304

Tim Kreuzer, Panagiotis Papapetrou, Jelena Zdravkovic

{"title":"Artificial intelligence in digital twins—A systematic literature review","authors":"Tim Kreuzer, Panagiotis Papapetrou, Jelena Zdravkovic","doi":"10.1016/j.datak.2024.102304","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102304","url":null,"abstract":"<div><p>Artificial intelligence and digital twins have become more popular in recent years and have seen usage across different application domains for various scenarios. This study reviews the literature at the intersection of the two fields, where digital twins integrate an artificial intelligence component. We follow a systematic literature review approach, analyzing a total of 149 related studies. In the assessed literature, a variety of problems are approached with an artificial intelligence-integrated digital twin, demonstrating its applicability across different fields. Our findings indicate that there is a lack of in-depth modeling approaches regarding the digital twin, while many articles focus on the implementation and testing of the artificial intelligence component. The majority of publications do not demonstrate a virtual-to-physical connection between the digital twin and the real-world system. Further, only a small portion of studies base their digital twin on real-time data from a physical system, implementing a physical-to-virtual connection.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"151 ","pages":"Article 102304"},"PeriodicalIF":2.5,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000284/pdfft?md5=7bf249b030dadbb8c82308b54aef035d&pid=1-s2.0-S0169023X24000284-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140549919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0