Pub Date : 2024-06-08DOI: 10.1016/j.datak.2024.102333
Marcel Altendeitering , Tobias Moritz Guggenberger , Frederik Möller
Data ecosystems are a novel inter-organizational form of cooperation. They require at least one data provider and one or more data consumers. Existing research mainly addresses generativity mechanisms in this relationship, such as business models or role models for data ecosystems. However, an essential prerequisite for thriving data ecosystems is high data quality in the shared data. Without sufficient data quality, sharing data might lead to negative business consequences, given that the information drawn from them or services built on them might be incorrect or produce fraudulent results. We tackle this issue precisely since we report on a multi-case study deploying data quality tools in data ecosystem scenarios. From these cases, we derive generalized prescriptive design knowledge as a design theory to make the knowledge available for others designing data quality tools for data sharing. Subsequently, our study contributes to integrating the issue of data quality in data ecosystem research and provides practitioners with actionable guidelines inferred from three real-world cases.
{"title":"A design theory for data quality tools in data ecosystems: Findings from three industry cases","authors":"Marcel Altendeitering , Tobias Moritz Guggenberger , Frederik Möller","doi":"10.1016/j.datak.2024.102333","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102333","url":null,"abstract":"<div><p>Data ecosystems are a novel inter-organizational form of cooperation. They require at least one data provider and one or more data consumers. Existing research mainly addresses generativity mechanisms in this relationship, such as business models or role models for data ecosystems. However, an essential prerequisite for thriving data ecosystems is high data quality in the shared data. Without sufficient data quality, sharing data might lead to negative business consequences, given that the information drawn from them or services built on them might be incorrect or produce fraudulent results. We tackle this issue precisely since we report on a multi-case study deploying data quality tools in data ecosystem scenarios. From these cases, we derive generalized prescriptive design knowledge as a design theory to make the knowledge available for others designing data quality tools for data sharing. Subsequently, our study contributes to integrating the issue of data quality in data ecosystem research and provides practitioners with actionable guidelines inferred from three real-world cases.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000570/pdfft?md5=c13245062cdefc052035d38866a21318&pid=1-s2.0-S0169023X24000570-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324527","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}
Pub Date : 2024-05-30DOI: 10.1016/j.datak.2024.102322
Liang-Ching Chen
Keyword extraction involves the application of Natural Language Processing (NLP) algorithms or models developed in the realm of text mining. Keyword extraction is a common technique used to explore linguistic patterns in the corpus linguistic field, and Dunning’s Log-Likelihood Test (LLT) has long been integrated into corpus software as a statistic-based NLP model. While prior research has confirmed the widespread applicability of keyword extraction in corpus-based research, LLT has certain limitations that may impact the accuracy of keyword extraction in such research. This paper summarized the limitations of LLT, which include benchmark corpus interference, elimination of grammatical and generic words, consideration of sub-corpus relevance, flexibility in feature selection, and adaptability to different research goals. To address these limitations, this paper proposed an extended Term Frequency-Inverse Document Frequency (TF-IDF) method. To verify the applicability of the proposed method, 20 highly cited research articles on climate change from the Web of Science (WOS) database were used as the target corpus, and a comparison was conducted with the traditional method. The experimental results indicated that the proposed method could effectively overcome the limitations of the traditional method and demonstrated the feasibility and practicality of incorporating the TF-IDF algorithm into relevant corpus-based research.
关键词提取涉及应用文本挖掘领域开发的自然语言处理(NLP)算法或模型。关键词提取是语料库语言学领域探索语言模式的常用技术,而邓宁对数似然检验(LLT)作为一种基于统计的 NLP 模型,早已被整合到语料库软件中。虽然之前的研究已经证实了关键词提取在基于语料库的研究中的广泛适用性,但 LLT 存在一定的局限性,可能会影响此类研究中关键词提取的准确性。本文总结了 LLT 的局限性,其中包括基准语料干扰、消除语法词和通用词、考虑子语料相关性、特征选择的灵活性以及对不同研究目标的适应性。针对这些局限性,本文提出了一种扩展的词频-反向文档频率(TF-IDF)方法。为了验证该方法的适用性,本文使用了 Web of Science(WOS)数据库中 20 篇关于气候变化的高被引研究文章作为目标语料,并与传统方法进行了比较。实验结果表明,所提出的方法能有效克服传统方法的局限性,并证明了将 TF-IDF 算法纳入基于语料库的相关研究的可行性和实用性。
{"title":"An extended TF-IDF method for improving keyword extraction in traditional corpus-based research: An example of a climate change corpus","authors":"Liang-Ching Chen","doi":"10.1016/j.datak.2024.102322","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102322","url":null,"abstract":"<div><p>Keyword extraction involves the application of Natural Language Processing (NLP) algorithms or models developed in the realm of text mining. Keyword extraction is a common technique used to explore linguistic patterns in the corpus linguistic field, and Dunning’s Log-Likelihood Test (LLT) has long been integrated into corpus software as a statistic-based NLP model. While prior research has confirmed the widespread applicability of keyword extraction in corpus-based research, LLT has certain limitations that may impact the accuracy of keyword extraction in such research. This paper summarized the limitations of LLT, which include benchmark corpus interference, elimination of grammatical and generic words, consideration of sub-corpus relevance, flexibility in feature selection, and adaptability to different research goals. To address these limitations, this paper proposed an extended Term Frequency-Inverse Document Frequency (TF-IDF) method. To verify the applicability of the proposed method, 20 highly cited research articles on climate change from the Web of Science (WOS) database were used as the target corpus, and a comparison was conducted with the traditional method. The experimental results indicated that the proposed method could effectively overcome the limitations of the traditional method and demonstrated the feasibility and practicality of incorporating the TF-IDF algorithm into relevant corpus-based research.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285881","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}
Pub Date : 2024-05-28DOI: 10.1016/j.datak.2024.102323
Christina Antoniou, Kalliopi Kravari, Nick Bassiliades
This paper presents a combination of an ontology and boilerplates, which are requirements templates for the syntactic structure of individual requirements that try to alleviate the problem of ambiguity caused using natural language and make it easier for inexperienced engineers to create requirements. However, still the use of boilerplates restricts the use of natural language only syntactically and not semantically. Boilerplates consists of fixed and attributes elements. Using ontologies, restricts the vocabulary of the words used in the requirements boilerplates to entities, their properties and entity relationships that are semantically meaningful to the application domain, leading thus to fewer errors. In this work we combine the advantages of boilerplates and ontologies. Usually, the attributes of boilerplates are completed with the help of the ontology. The contribution of this paper is that the whole boilerplates are stored in the ontology, based on the fact that RDF triples have similar syntax to the boilerplate syntax, so that attributes and fixed elements are part of the ontology. This combination helps to construct semantically and syntactically correct requirements. The contribution and novelty of our method is that we exploit the natural language syntax of boilerplates mapping them to Resource Description Framework triples which have also a linguistic nature. In this paper we created and present the development of a domain-specific ontology as well as a minimal set of boilerplates for a specific application domain, namely that of engineering software for an ATM, while maintaining flexibility on the one hand and generality on the other.
{"title":"Semantic requirements construction using ontologies and boilerplates","authors":"Christina Antoniou, Kalliopi Kravari, Nick Bassiliades","doi":"10.1016/j.datak.2024.102323","DOIUrl":"https://doi.org/10.1016/j.datak.2024.102323","url":null,"abstract":"<div><p>This paper presents a combination of an ontology and boilerplates, which are requirements templates for the syntactic structure of individual requirements that try to alleviate the problem of ambiguity caused using natural language and make it easier for inexperienced engineers to create requirements. However, still the use of boilerplates restricts the use of natural language only syntactically and not semantically. Boilerplates consists of fixed and attributes elements. Using ontologies, restricts the vocabulary of the words used in the requirements boilerplates to entities, their properties and entity relationships that are semantically meaningful to the application domain, leading thus to fewer errors. In this work we combine the advantages of boilerplates and ontologies. Usually, the attributes of boilerplates are completed with the help of the ontology. The contribution of this paper is that the whole boilerplates are stored in the ontology, based on the fact that RDF triples have similar syntax to the boilerplate syntax, so that attributes and fixed elements are part of the ontology. This combination helps to construct semantically and syntactically correct requirements. The contribution and novelty of our method is that we exploit the natural language syntax of boilerplates mapping them to Resource Description Framework triples which have also a linguistic nature. In this paper we created and present the development of a domain-specific ontology as well as a minimal set of boilerplates for a specific application domain, namely that of engineering software for an ATM, while maintaining flexibility on the one hand and generality on the other.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141289760","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}
Pub Date : 2024-05-23DOI: 10.1016/j.datak.2024.102324
Robert Buchmann , Johann Eder , Hans-Georg Fill , Ulrich Frank , Dimitris Karagiannis , Emanuele Laurenzi , John Mylopoulos , Dimitris Plexousakis , Maribel Yasmina Santos
The hype of Large Language Models manifests in disruptions, expectations or concerns in scientific communities that have focused for a long time on design-oriented research. The current experiences with Large Language Models and associated products (e.g. ChatGPT) lead to diverse positions regarding the foreseeable evolution of such products from the point of view of scholars who have been working with designed abstractions for most of their careers - typically relying on deterministic design decisions to ensure systems and automation reliability. Such expectations are collected in this paper in relation to a flavor of systems engineering that relies on explicit knowledge structures, introduced here as “semantics-driven systems engineering”.
The paper was motivated by the panel discussion that took place at CAiSE 2023 in Zaragoza, Spain, during the workshop on Knowledge Graphs for Semantics-driven Systems Engineering (KG4SDSE). The workshop brought together Conceptual Modeling researchers with an interest in specific applications of Knowledge Graphs and the semantic enrichment benefits they can bring to systems engineering. The panel context and consensus are summarized at the end of the paper, preceded by a proposed research agenda considering the expressed positions.
{"title":"Large language models: Expectations for semantics-driven systems engineering","authors":"Robert Buchmann , Johann Eder , Hans-Georg Fill , Ulrich Frank , Dimitris Karagiannis , Emanuele Laurenzi , John Mylopoulos , Dimitris Plexousakis , Maribel Yasmina Santos","doi":"10.1016/j.datak.2024.102324","DOIUrl":"10.1016/j.datak.2024.102324","url":null,"abstract":"<div><p>The hype of Large Language Models manifests in disruptions, expectations or concerns in scientific communities that have focused for a long time on design-oriented research. The current experiences with Large Language Models and associated products (e.g. ChatGPT) lead to diverse positions regarding the foreseeable evolution of such products from the point of view of scholars who have been working with designed abstractions for most of their careers - typically relying on deterministic design decisions to ensure systems and automation reliability. Such expectations are collected in this paper in relation to a flavor of systems engineering that relies on explicit knowledge structures, introduced here as “semantics-driven systems engineering”.</p><p>The paper was motivated by the panel discussion that took place at CAiSE 2023 in Zaragoza, Spain, during the workshop on Knowledge Graphs for Semantics-driven Systems Engineering (KG4SDSE). The workshop brought together Conceptual Modeling researchers with an interest in specific applications of Knowledge Graphs and the semantic enrichment benefits they can bring to systems engineering. The panel context and consensus are summarized at the end of the paper, preceded by a proposed research agenda considering the expressed positions.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141134203","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}
Pub Date : 2024-05-16DOI: 10.1016/j.datak.2024.102315
Gabriele Scaffidi Militone, Daniele Apiletti, Giovanni Malnati
In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by third parties, such as human operators or specific business logic, even while the media files are still being collected. However, using transactions to ensure data persistence often limits system throughput and latency. This paper presents a solution that enables both high ingestion rates with transactional data persistence and near real-time, low-latency access to the stream during collection. This immediate access enables the prompt application of specialized data engineering algorithms during data acquisition. The proposed solution is particularly suitable for binary data sources such as audio and video recordings in surveillance systems, and it can be extended to various big data scenarios via well-defined general interfaces. The scalability of the approach is based on the microservice architecture. Preliminary results obtained with Apache Kafka and MongoDB replica sets show that the proposed solution provides up to 3 times higher throughput and 2.2 times lower latency compared to standard multi-document transactions.
{"title":"Hermes, a low-latency transactional storage for binary data streams from remote devices","authors":"Gabriele Scaffidi Militone, Daniele Apiletti, Giovanni Malnati","doi":"10.1016/j.datak.2024.102315","DOIUrl":"10.1016/j.datak.2024.102315","url":null,"abstract":"<div><p>In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by third parties, such as human operators or specific business logic, even while the media files are still being collected. However, using transactions to ensure data persistence often limits system throughput and latency. This paper presents a solution that enables both high ingestion rates with transactional data persistence and near real-time, low-latency access to the stream during collection. This immediate access enables the prompt application of specialized data engineering algorithms during data acquisition. The proposed solution is particularly suitable for binary data sources such as audio and video recordings in surveillance systems, and it can be extended to various big data scenarios via well-defined general interfaces. The scalability of the approach is based on the microservice architecture. Preliminary results obtained with Apache Kafka and MongoDB replica sets show that the proposed solution provides up to 3 times higher throughput and 2.2 times lower latency compared to standard multi-document transactions.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042236","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}
Hotel reviews play a vital role in tourism recommender system. They should be analyzed effectively to enhance the accuracy of recommendations which can be generated either from crisp ratings on a fixed scale or real sentiments of reviews. But crisp ratings cannot represent the actual feelings of reviewers. Existing tourism recommender systems mostly recommend hotels on the basis of vague and sparse ratings resulting in inaccurate recommendations or preferences for online users. This paper presents a semantic approach to analyze the online reviews being crawled from tripadvisor.in. It discovers the underlying fuzzy semantics of reviews with respect to the multiple criteria of hotels rather than using the crisp ratings. The crawled reviews are preprocessed via data cleaning such as stopword and punctuation removal, tokenization, lemmatization, pos tagging to understand the semantics efficiently. Nouns representing frequent features of hotels are extracted from pre-processed reviews which are further used to identify opinion phrases. Fuzzy weights are derived from normalized frequency of frequent nouns and combined with sentiment score of all the synonyms of adjectives in the identified opinion phrases. This results in fuzzy semantics which form an ideal representation of reviews for a multi-criteria tourism recommender system. The proposed work is implemented in python by crawling the recent reviews of Jaipur hotels from TripAdvisor and analyzing their semantics. The resultant fuzzy semantics form a manually tagged dataset of reviews tagged with sentiments of identified aspects, respectively. Experimental results show improved sentiment score while considering all the synonyms of adjectives. The results are further used to fine-tune BERT models to form encodings for a query-based recommender system. The proposed approach can help tourism and hospitality service providers to take advantage of such sentiment analysis to examine the negative comments or unpleasant experiences of tourists and making appropriate improvements. Moreover, it will help online users to get better recommendations while planning their trips.
{"title":"Analyzing fuzzy semantics of reviews for multi-criteria recommendations","authors":"Navreen Kaur Boparai , Himanshu Aggarwal , Rinkle Rani","doi":"10.1016/j.datak.2024.102314","DOIUrl":"10.1016/j.datak.2024.102314","url":null,"abstract":"<div><p>Hotel reviews play a vital role in tourism recommender system. They should be analyzed effectively to enhance the accuracy of recommendations which can be generated either from crisp ratings on a fixed scale or real sentiments of reviews. But crisp ratings cannot represent the actual feelings of reviewers. Existing tourism recommender systems mostly recommend hotels on the basis of vague and sparse ratings resulting in inaccurate recommendations or preferences for online users. This paper presents a semantic approach to analyze the online reviews being crawled from tripadvisor.in. It discovers the underlying fuzzy semantics of reviews with respect to the multiple criteria of hotels rather than using the crisp ratings. The crawled reviews are preprocessed via data cleaning such as stopword and punctuation removal, tokenization, lemmatization, pos tagging to understand the semantics efficiently. Nouns representing frequent features of hotels are extracted from pre-processed reviews which are further used to identify opinion phrases. Fuzzy weights are derived from normalized frequency of frequent nouns and combined with sentiment score of all the synonyms of adjectives in the identified opinion phrases. This results in fuzzy semantics which form an ideal representation of reviews for a multi-criteria tourism recommender system. The proposed work is implemented in python by crawling the recent reviews of Jaipur hotels from TripAdvisor and analyzing their semantics. The resultant fuzzy semantics form a manually tagged dataset of reviews tagged with sentiments of identified aspects, respectively. Experimental results show improved sentiment score while considering all the synonyms of adjectives. The results are further used to fine-tune BERT models to form encodings for a query-based recommender system. The proposed approach can help tourism and hospitality service providers to take advantage of such sentiment analysis to examine the negative comments or unpleasant experiences of tourists and making appropriate improvements. Moreover, it will help online users to get better recommendations while planning their trips.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141034319","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}
Pub Date : 2024-05-11DOI: 10.1016/j.datak.2024.102310
Corentin Burnay, Mathieu Lega, Sarah Bouraga
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.
组织中的决策者都在努力提高决策质量。改进这一过程的方法之一就是用事实将决策客观化。数据驱动的决策支持系统(DSS),更具体地说就是商业智能(BI),就是为了实现这一目标。各组织在开发商业智能数据驱动型决策支持系统方面投入了大量资金,并期望这些系统能够被采用并为决策者提供有效支持。这就提出了许多技术和方法上的挑战,尤其是在商业智能仪表盘的设计方面,它可以被视为商业智能数据驱动型数据支持系统的冰山一角,在整个系统的采用方面发挥着重要作用。本文通过早期实证研究,将仪表盘内容作为 BI 数据驱动的 DSS 仪表盘采用或拒绝的可能根源之一进行调查。更确切地说,这项工作由三部分组成。在第一部分中,研究了 BI 面板背景下的认知负荷概念,并介绍了信息负荷、表征负荷和非信息负荷。在第二部分中,通过对 167 名受访者进行实验和结构方程建模(SEM)分析,研究了这些负载对采用商业智能仪表盘的影响。研究结果是仪表盘采用模型,该模型用新的内容导向变量丰富了开创性的技术接受模型,以支持设计更具支持性的商业智能数据驱动的 DSS 仪表盘。最后,第三部分提出了一套指标,以帮助仪表盘设计者切实监测仪表盘的负载情况。
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Pub Date : 2024-05-08DOI: 10.1016/j.datak.2024.102303
Thibault Falque , Bertrand Mazure , Karim Tabia
Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling operational costs. This paper presents a real and challenging problem of predicting and explaining flight off-block delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the specificities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us to believe that the proposed approach could help improve the management of delays and reduce the impact of the resulting consequences.
准点率是大型机场和枢纽的一个敏感问题,关系到乘客体验和运营成本控制。本文提出了一个具有挑战性的实际问题,即如何预测和解释航班延误。我们以巴黎戴高乐国际机场(Paris Charles de Gaulle,简称 "Paris-CDG")为例进行研究,从巴黎戴高乐机场这一问题的特殊性入手,到提出模型和解决方案,再到对全年活动的真实数据进行结果分析。本文所提供的概念证明让我们相信,所提出的方法有助于改善延误管理并减少由此造成的影响。
{"title":"Machine learning for predicting off-block delays: A case study at Paris — Charles de Gaulle International Airport","authors":"Thibault Falque , Bertrand Mazure , Karim Tabia","doi":"10.1016/j.datak.2024.102303","DOIUrl":"10.1016/j.datak.2024.102303","url":null,"abstract":"<div><p>Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling operational costs. This paper presents a real and challenging problem of predicting and explaining flight off-block delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the specificities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us to believe that the proposed approach could help improve the management of delays and reduce the impact of the resulting consequences.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000272/pdfft?md5=ff8c7468240914b3ce61469a0954468c&pid=1-s2.0-S0169023X24000272-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043841","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}
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-05-03DOI: 10.1016/j.datak.2024.102309
Milos Chaloupka, Martin Necasky
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.
{"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":null,"pages":null},"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}