WIREs Data Mining and Knowledge Discovery最新文献_第4页

A review of reasoning characteristics of RDF-based Semantic Web systems 基于 RDF 的语义网系统的推理特点综述

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-03-28 DOI: 10.1002/widm.1537

Simona Colucci, Francesco M. Donini, Eugenio Di Sciascio

Presented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross-domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF-based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW.

语义网（Semantic Web，SW）于 2001 年作为一项研究挑战提出，如今已成为一项成熟的技术，被用于多个跨领域应用中。其主要优势之一是 RDF 数据格式的正式语义，它使系统能够验证数据、通过自动推理推断出隐含知识并向用户解释；然而，本文对 71 个基于 RDF 的 SW 系统（其中有 17 个推理系统）进行的分析表明，所有 SW 应用程序对这种语义的利用存在很大差异。由于简单地列举系统（每个系统都有自己的特点）可能会导致毫无头绪的罗列，因此我们借鉴了软件工程中成熟度模型的概念，并围绕它来组织我们的分类。我们的模型有三个正交维度：空白节点的处理、演绎能力的程度和结果的解释。对于每个维度，我们定义了 3-4 个对语义利用程度不断提高的等级，对应于该维度中越来越复杂的输出。然后，根据每个系统的文档和发表的文章，将其在每个维度上进行分类。系统在每个维度上的分布情况见图摘。我们特意将资源消耗（时间和空间）排除在外，因为这不是 SW 特有的维度。

{"title":"A review of reasoning characteristics of RDF-based Semantic Web systems","authors":"Simona Colucci, Francesco M. Donini, Eugenio Di Sciascio","doi":"10.1002/widm.1537","DOIUrl":"https://doi.org/10.1002/widm.1537","url":null,"abstract":"Presented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross-domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF-based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140333756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Does a language model “understand” high school math? A survey of deep learning based word problem solvers 语言模型 "理解 "高中数学吗？基于深度学习的文字解题器调查

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-03-24 DOI: 10.1002/widm.1534

Sowmya S. Sundaram, Sairam Gurajada, Deepak Padmanabhan, Savitha Sam Abraham, Marco Fisichella

From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we still have a lot to explore for building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last 2 years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyze why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavor to provide a road-map for future math word problem research.

从过去十年的后半期开始，人们对开发自动解决数学文字问题（MWP）的算法越来越感兴趣。这是一项具有挑战性的独特任务，需要将表面文字模式识别与数学推理相结合。尽管进行了广泛的研究，但我们仍有许多工作要做，以建立健全的初等数学文字问题表征，并为一般任务提供有效的解决方案。在本文中，我们将批判性地审视为解决文字问题而开发的各种模型、它们的优缺点以及面临的挑战。在过去两年中，许多深度学习模型在基准数据集上取得了竞争性的结果，因此在这个时刻对文献进行批判性和概念性分析非常有用。我们回过头来分析一下，为什么尽管学术界对此兴趣浓厚，但主要使用的实验和数据集设计仍然是一个绊脚石。从仔细分析文献的角度出发，我们还努力为未来的数学文字问题研究提供一个路线图。

{"title":"Does a language model “understand” high school math? A survey of deep learning based word problem solvers","authors":"Sowmya S. Sundaram, Sairam Gurajada, Deepak Padmanabhan, Savitha Sam Abraham, Marco Fisichella","doi":"10.1002/widm.1534","DOIUrl":"https://doi.org/10.1002/widm.1534","url":null,"abstract":"From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we still have a lot to explore for building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last 2 years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyze why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavor to provide a road-map for future math word problem research.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140209772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing privacy concerns with wearable health monitoring technology 利用可穿戴健康监测技术解决隐私问题

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-03-23 DOI: 10.1002/widm.1535

C. L. V. Sivakumar, Varda Mone, Rakhmanov Abdumukhtor

The growing popularity of wearable health devices like fitness trackers and smartwatches enables continuous personal health monitoring but also raises significant privacy concerns due to the real-time collection of sensitive data. Many users are unaware of vulnerabilities that could lead to unauthorized access or discrimination if health information is revealed without consent. However, even informed users may willingly share data despite understanding privacy risks. The recent implementation of the General Data Protection Regulation (GDPR) in the EU and states taking initiatives to regulate privacy shows growing regulatory efforts to address these threats. This paper evaluates the key privacy threats posed specifically by consumer wearable devices. It provides a focused analysis of how health data could be exploited or shared without users' knowledge and the security flaws that enable such risks. Potential solutions including improving protections, empowering user control, enhancing transparency, and strengthening regulations are examined. However, it is argued that effective change requires balancing privacy risks with health benefits while also considering human decision-making behaviors. The paper concludes by proposing a multifaceted approach to enable informed choices about wearable health data.

健身追踪器和智能手表等可穿戴健康设备日益普及，实现了持续的个人健康监测，但由于实时收集敏感数据，也引发了严重的隐私问题。许多用户并不知道，如果未经同意泄露健康信息，可能会导致未经授权的访问或歧视。然而，即使是知情的用户，也可能在了解隐私风险的情况下仍然愿意分享数据。欧盟最近实施的《通用数据保护条例》（GDPR）和各国采取的隐私监管措施表明，为应对这些威胁，监管部门做出了越来越多的努力。本文专门评估了消费类可穿戴设备带来的主要隐私威胁。它重点分析了健康数据如何在用户不知情的情况下被利用或共享，以及导致此类风险的安全漏洞。研究还探讨了潜在的解决方案，包括改进保护措施、增强用户控制能力、提高透明度和加强监管。不过，本文认为，要想实现有效变革，就必须在隐私风险与健康益处之间取得平衡，同时还要考虑到人类的决策行为。本文最后提出了一种多层面的方法，使人们能够对可穿戴健康数据做出明智的选择。

{"title":"Addressing privacy concerns with wearable health monitoring technology","authors":"C. L. V. Sivakumar, Varda Mone, Rakhmanov Abdumukhtor","doi":"10.1002/widm.1535","DOIUrl":"https://doi.org/10.1002/widm.1535","url":null,"abstract":"The growing popularity of wearable health devices like fitness trackers and smartwatches enables continuous personal health monitoring but also raises significant privacy concerns due to the real-time collection of sensitive data. Many users are unaware of vulnerabilities that could lead to unauthorized access or discrimination if health information is revealed without consent. However, even informed users may willingly share data despite understanding privacy risks. The recent implementation of the General Data Protection Regulation (GDPR) in the EU and states taking initiatives to regulate privacy shows growing regulatory efforts to address these threats. This paper evaluates the key privacy threats posed specifically by consumer wearable devices. It provides a focused analysis of how health data could be exploited or shared without users' knowledge and the security flaws that enable such risks. Potential solutions including improving protections, empowering user control, enhancing transparency, and strengthening regulations are examined. However, it is argued that effective change requires balancing privacy risks with health benefits while also considering human decision-making behaviors. The paper concludes by proposing a multifaceted approach to enable informed choices about wearable health data.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"150 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140209698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to “Expression of Concern: Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREsData Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278” 对 "关注的表达 "的更正Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition.WIREsData Mining Knowl.Discov.9, e1278 (2019). https://doi.org/10.1002/widm.1278"

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-03-20 DOI: 10.1002/widm.1532

Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278. WIREs Data Mining Knowl. Discov., 10, e1386 (2020). https://doi.org/10.1002/widm.1386

The originally published version of this Expression of Concern has been updated to include new information raised to us by a third party. The corrected version is also presented here with the amended text in bold.

This Expression of Concern is for the above article, published online on August 2, 2018, in Wiley Online Library (wileyonlinelibrary.com) and has been published by agreement between the journal Editor-in-Chief, Dr. Witold Pedrycz, and Wiley Periodicals LLC. The Expression of Concern has been agreed due to concerns raised regarding possible misrepresentation of the data set of facial images used in this article. Based on information provided by the authors, data collection for the above mentioned article took place in 2014. However, it has subsequently been noted that images from this same data set have purportedly been used in Duan et al. (2010), Wang et al. (2018), in which the data reported was purportedly collected in 2012 and the article co-authored by the corresponding author of the above mentioned article, and Ma (2012), which acknowledges the guidance and support of the corresponding author of the above mentioned article in the student's thesis work and includes at least one similar data point to the above mentioned article in the form of a facial image. The journal therefore has concerns about when data collection actually took place. Additionally, Figure 1 in the above mentioned article appears to be the same as Figure 1 in Wang et al. (2018), though there is no citation, and permission was not obtained to reuse the figure. The authors have not provided further information to the journal to help clarify when data collection took place. As a result, the journal has decided to issue an Expression of Concern to readers.

The online version of the originally published Expression of Concern has been corrected accordingly.

REFERENCES

Duan, X., Wang, C., Liu, X., Li, Z., Wu, J., & Zhang, H. (2010). Ethnic Features Extraction and Recognition of Human Faces. 2010 second International Conference on Advanced Computer Control, 2, 125–130. https://doi.org/10.1109/ICACC.2010.5487194

Wang, C., Zhang, Q., Dian, X., & Gan, J. (2018). Multi-ethnical Chinese facial characterization and analysis. Multimedia Tools and Applications, 77, 30,311–30,329. https://doi.org/10.1007/s11042-018-6,018-1

Ma, Y. (2012). The Technique Research of Multi-Minorities Facial Expression Understanding and Analysis [Master's Thesis, Northeastern University, Shenyang, China]. https://www.doc88.com/p-6721329496334.html

Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. 面向人种识别的面部特征发现。WIREs Data Mining Knowl.Discov.9, e1278 (2019). https://doi.org/10.1002/widm.1278.WIREs Data Mining Knowl.10, e1386 (2020)。 https://doi.org/10.1002/widm.1386The 本 "关注表达 "的最初发布版本已更新，纳入了第三方向我们提出的新信息。更正后的版本也在此列出，修正后的文字以粗体显示。本《关注声明》针对的是 2018 年 8 月 2 日在线发表在 Wiley Online Library（wileyonlinelibrary.com）上的上述文章，由期刊主编 Witold Pedrycz 博士和 Wiley Periodicals LLC 协议发布。之所以同意发表 "关注声明"，是因为有人担心这篇文章中使用的面部图像数据集可能存在失实陈述。根据作者提供的信息，上述文章的数据收集工作发生在 2014 年。然而，随后我们注意到，Duan 等人（2010 年）、Wang 等人（2018 年）（其中报告的数据据称是在 2012 年收集的，文章由上述文章的通讯作者共同撰写）和 Ma（2012 年）（其中承认上述文章的通讯作者在学生的毕业论文工作中给予了指导和支持，并以面部图像的形式包含了至少一个与上述文章类似的数据点）都声称使用了来自同一数据集的图像。因此，期刊对数据收集的实际时间表示担忧。此外，上述文章中的图 1 似乎与 Wang 等人（2018）的图 1 相同，但没有引用，也没有获得重用该图的许可。作者没有向本刊提供进一步信息，以帮助澄清数据收集的时间。因此，本刊决定向读者发布《关注声明》。原发布的《关注声明》网络版已作相应更正。参考文献Duan, X., Wang, C., Liu, X., Li, Z., Wu, J., & Zhang, H. (2010).人脸的民族特征提取与识别》。https://doi.org/10.1109/ICACC.2010.5487194Wang, C., Zhang, Q., Dian, X., & Gan, J. (2018).多民族中国人面部特征描述与分析。多媒体工具与应用，77，30,311-30,329。https://doi.org/10.1007/s11042-018-6,018-1Ma, Y. (2012).多少数民族面部表情理解与分析技术研究[东北大学硕士论文，沈阳，中国].https://www.doc88.com/p-6721329496334.html

{"title":"Correction to “Expression of Concern: Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREsData Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278”","authors":"","doi":"10.1002/widm.1532","DOIUrl":"https://doi.org/10.1002/widm.1532","url":null,"abstract":"Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278. WIREs Data Mining Knowl. Discov., 10, e1386 (2020). https://doi.org/10.1002/widm.1386\u0000The originally published version of this Expression of Concern has been updated to include new information raised to us by a third party. The corrected version is also presented here with the amended text in bold.\u0000This Expression of Concern is for the above article, published online on August 2, 2018, in Wiley Online Library (wileyonlinelibrary.com) and has been published by agreement between the journal Editor-in-Chief, Dr. Witold Pedrycz, and Wiley Periodicals LLC. The Expression of Concern has been agreed due to concerns raised regarding possible misrepresentation of the data set of facial images used in this article. Based on information provided by the authors, data collection for the above mentioned article took place in 2014. However, it has subsequently been noted that images from this same data set have purportedly been used in Duan et al. (2010), Wang et al. (2018), in which the data reported was purportedly collected in 2012 and the article co-authored by the corresponding author of the above mentioned article, and Ma (2012), which acknowledges the guidance and support of the corresponding author of the above mentioned article in the student's thesis work and includes at least one similar data point to the above mentioned article in the form of a facial image. The journal therefore has concerns about when data collection actually took place. Additionally, Figure 1 in the above mentioned article appears to be the same as Figure 1 in Wang et al. (2018), though there is no citation, and permission was not obtained to reuse the figure. The authors have not provided further information to the journal to help clarify when data collection took place. As a result, the journal has decided to issue an Expression of Concern to readers.\u0000The online version of the originally published Expression of Concern has been corrected accordingly.\u0000REFERENCES\u0000Duan, X., Wang, C., Liu, X., Li, Z., Wu, J., & Zhang, H. (2010). Ethnic Features Extraction and Recognition of Human Faces. 2010 second International Conference on Advanced Computer Control, 2, 125–130. https://doi.org/10.1109/ICACC.2010.5487194\u0000Wang, C., Zhang, Q., Dian, X., & Gan, J. (2018). Multi-ethnical Chinese facial characterization and analysis. Multimedia Tools and Applications, 77, 30,311–30,329. https://doi.org/10.1007/s11042-018-6,018-1\u0000Ma, Y. (2012). The Technique Research of Multi-Minorities Facial Expression Understanding and Analysis [Master's Thesis, Northeastern University, Shenyang, China]. https://www.doc88.com/p-6721329496334.html","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141526313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques 利用机器学习技术检测和调整流数据中的概念漂移的系统综述

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-03-19 DOI: 10.1002/widm.1536

Shruti Arora, Rinkle Rani, Nitin Saxena

Last decade demonstrate the massive growth in organizational data which keeps on increasing multi-fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real-time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts.

近十年来，随着每秒更新的记录数以百万计，组织数据不断成倍增长。处理如此庞大和连续的数据极具挑战性，这进一步开辟了许多研究领域。来自不同来源的实时、持续流动的数据被称为流数据。在从数据流中获取有价值的统计数据时，数据分布中出现的变化被称为概念漂移。这些漂移在数据挖掘、机器学习、泛在知识发现、定量决策理论等多个学科中发挥着重要作用。因此，为研究处理漂移的方法和途径，开展了大量研究。然而，现有资料比较分散，缺乏为特定应用选择有效技术的指导原则。本调查报告的主要新目标是介绍对概念漂移挑战和相关研究的理解。此外，它还有助于不同领域的研究人员在其应用中采用概念漂移的检测和适应算法。总之，本研究旨在帮助人们深入了解各种类型的漂移分类、检测和适应方法，以及它们的主要特点和局限性。此外，本研究还强调了用于评估流数据概念漂移检测方法的性能指标。本文通过强调现有文献在开发处理概念漂移的技术方面存在的不足，提出了未来的研究范围。

{"title":"A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques","authors":"Shruti Arora, Rinkle Rani, Nitin Saxena","doi":"10.1002/widm.1536","DOIUrl":"https://doi.org/10.1002/widm.1536","url":null,"abstract":"Last decade demonstrate the massive growth in organizational data which keeps on increasing multi-fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real-time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140162232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge graph-driven data processing for business intelligence 面向商业智能的知识图谱驱动型数据处理

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-02-11 DOI: 10.1002/widm.1529

Lipika Dey

With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre-training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use-cases from various domains.

随着大数据的激增，组织决策也变得更加复杂。商业智能（BI）不再局限于查询营销和销售数据。它更多地涉及到将不同应用中的数据联系起来，以及对大量非结构化数据（如电子邮件、通话记录、社交媒体、新闻等）进行分析，以试图获得洞察力，从而为未来的战略制定提供可操作的情报和更好的投入。事实证明，知识图谱等语义技术是非常有用的工具，有助于智能地连接不同的数据源，并通过连接后形成的复杂网络进行推理。在过去的十年中，知识图谱的创建、存储和维护过程已经足够成熟，现在也开始进入商业决策领域。最近，这些知识图谱还被视为减少大型语言模型幻觉的一种潜在方法，在预训练和生成输出时都可以使用这些知识图谱。此外，还存在一些挑战。这些挑战包括构建和维护图谱、对缺失链接进行推理等。虽然这些问题仍是有待解决的研究课题，但我们将在本文中介绍目前如何利用知识图谱从不同领域的用例中获取商业智能。

{"title":"Knowledge graph-driven data processing for business intelligence","authors":"Lipika Dey","doi":"10.1002/widm.1529","DOIUrl":"https://doi.org/10.1002/widm.1529","url":null,"abstract":"With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre-training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use-cases from various domains.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141526461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing programming languages for data analytics: Accuracy of estimation in Python and R 比较数据分析编程语言：Python 和 R 的估算精度

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-02-02 DOI: 10.1002/widm.1531

Chelsey Hill, Lanqing Du, Marina Johnson, B. D. McCullough

Several open-source programming languages, particularly R and Python, are utilized in industry and academia for statistical data analysis, data mining, and machine learning. While most commercial software programs and programming languages provide a single way to deliver a statistical procedure, open-source programming languages have multiple libraries and packages offering many ways to complete the same analysis, often with varying results. Applying the same statistical method across these different libraries and packages can lead to entirely different solutions due to the differences in their implementations. Therefore, reliability and accuracy should be essential considerations when making library and package usage decisions while conducting statistical analysis using open source programming languages. Instead, most users take this for granted, assuming that their chosen libraries and packages produce accurate results for their statistical analysis. To this extent, this study assesses the estimation accuracy and reliability of Python and R's various libraries and packages by evaluating the univariate summary statistics, analysis of variance (ANOVA), and linear regression procedures using benchmarking data from the National Institutes of Standards and Technology (NIST). Further, experimental results are presented comparing machine learning methods for classification and regression. The libraries and packages assessed in this study include the stats package in R and Pandas, Statistics, NumPy, statsmodels, SciPy, statsmodels, scikit-learn, and pingouin in Python. The results show that the stats package in R and statsmodels library in Python are reliable for univariate summary statistics. In contrast, Python's scikit-learn library produces the most accurate results and is recommended for ANOVA. Among the libraries and packages assessed for linear regression, the results demonstrated that the stats package in R is more reliable, accurate, and flexible; thus, it is recommended for linear regression analysis. Further, we present results and recommendations for machine learning using R and Python.

一些开源编程语言，特别是 R 和 Python，被工业界和学术界用于统计数据分析、数据挖掘和机器学习。大多数商业软件程序和编程语言只提供一种统计程序的单一方法，而开源编程语言则有多个库和软件包，提供多种方法来完成相同的分析，而且结果往往各不相同。在这些不同的程序库和软件包中应用相同的统计方法，可能会因为实现方法的不同而导致完全不同的解决方案。因此，在使用开源编程语言进行统计分析时，可靠性和准确性应该是决定使用库和软件包时必须考虑的重要因素。然而，大多数用户却想当然地认为，他们所选择的库和软件包会为他们的统计分析提供准确的结果。为此，本研究使用美国国家标准与技术研究院（NIST）的基准数据，通过评估单变量汇总统计、方差分析（ANOVA）和线性回归程序，对 Python 和 R 的各种库和软件包的估计准确性和可靠性进行了评估。此外，实验结果还对分类和回归的机器学习方法进行了比较。本研究评估的库和软件包包括 R 中的 stats 软件包和 Python 中的 Pandas、Statistics、NumPy、statsmodels、SciPy、statsmodels、scikit-learn 和 pingouin。结果表明，R 中的 stats 软件包和 Python 中的 statsmodels 库对于单变量汇总统计是可靠的。相比之下，Python 的 scikit-learn 库产生的结果最准确，建议用于方差分析。在评估的线性回归库和软件包中，结果表明 R 中的 stats 软件包更可靠、更准确、更灵活，因此推荐使用它进行线性回归分析。此外，我们还介绍了使用 R 和 Python 进行机器学习的结果和建议。

{"title":"Comparing programming languages for data analytics: Accuracy of estimation in Python and R","authors":"Chelsey Hill, Lanqing Du, Marina Johnson, B. D. McCullough","doi":"10.1002/widm.1531","DOIUrl":"https://doi.org/10.1002/widm.1531","url":null,"abstract":"Several open-source programming languages, particularly R and Python, are utilized in industry and academia for statistical data analysis, data mining, and machine learning. While most commercial software programs and programming languages provide a single way to deliver a statistical procedure, open-source programming languages have multiple libraries and packages offering many ways to complete the same analysis, often with varying results. Applying the same statistical method across these different libraries and packages can lead to entirely different solutions due to the differences in their implementations. Therefore, reliability and accuracy should be essential considerations when making library and package usage decisions while conducting statistical analysis using open source programming languages. Instead, most users take this for granted, assuming that their chosen libraries and packages produce accurate results for their statistical analysis. To this extent, this study assesses the estimation accuracy and reliability of Python and R's various libraries and packages by evaluating the univariate summary statistics, analysis of variance (ANOVA), and linear regression procedures using benchmarking data from the National Institutes of Standards and Technology (NIST). Further, experimental results are presented comparing machine learning methods for classification and regression. The libraries and packages assessed in this study include the stats package in R and Pandas, Statistics, NumPy, statsmodels, SciPy, statsmodels, scikit-learn, and pingouin in Python. The results show that the stats package in R and statsmodels library in Python are reliable for univariate summary statistics. In contrast, Python's scikit-learn library produces the most accurate results and is recommended for ANOVA. Among the libraries and packages assessed for linear regression, the results demonstrated that the stats package in R is more reliable, accurate, and flexible; thus, it is recommended for linear regression analysis. Further, we present results and recommendations for machine learning using R and Python.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139662921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A literature review on satellite image time series forecasting: Methods and applications for remote sensing 卫星图像时间序列预测文献综述：遥感方法与应用

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-01-29 DOI: 10.1002/widm.1528

Carlos Lara-Alvarez, Juan J. Flores, Hector Rodriguez-Rangel, Rodrigo Lopez-Farias

Satellite image time-series are time series produced from remote sensing images; they generally correspond to features or indicators extracted from those images. With the increasing availability of remote sensing images and new methodologies to process such data, image time-series methods have been used extensively for assessing temporal pattern detection, monitoring, classification, object detection, and feature estimation. Since the study of time series is broad, this article focuses on analyzing articles related to forecasting the value of one or more attributes of the image time-series. The image time series forecasting (ITSF) problem appears in different disciplines; most focus on improving the quality of life by harnessing natural resources for sustainable development and minimizing the lethality of dangerous natural phenomena. Scientists tackle these problems using different tools or methods depending on the application. This review analyzes the field's leading, most recent contributions, grouping them by application area and solution methods. Our findings indicate that artificial neural networks, regression trees, support vector regression, and cellular automata are the most common methods for ITSF. Application areas address this problem as renewable energy, agriculture, and land-use change. This study retrieved and analyzed relevant information about the recent activity of image time series forecasting, generating a reproducible list of the most pertinent articles in the field published from 2009 to 2021. To the author's best knowledge, this is the first review presenting and analyzing a reproducible list of the most relevant state-of-the-art articles focusing on the applications, techniques, and research trends for ITSF.

卫星图像时间序列是从遥感图像中产生的时间序列；它们通常与从这些图像中提取的特征或指标相对应。随着遥感图像的日益普及和处理此类数据的新方法的出现，图像时间序列方法已被广泛用于评估时间模式检测、监测、分类、目标检测和特征估计。由于时间序列的研究范围很广，本文重点分析与预测图像时间序列的一个或多个属性值有关的文章。图像时间序列预测（ITSF）问题出现在不同的学科中；大多数学科侧重于通过利用自然资源促进可持续发展和最大限度地减少危险自然现象的致命性来提高生活质量。科学家们根据不同的应用，使用不同的工具或方法来解决这些问题。本综述按应用领域和解决方法对该领域的领先最新成果进行了分析。我们的研究结果表明，人工神经网络、回归树、支持向量回归和细胞自动机是 ITSF 最常用的方法。解决这一问题的应用领域包括可再生能源、农业和土地利用变化。本研究检索并分析了近期图像时间序列预测活动的相关信息，生成了 2009 年至 2021 年期间发表的该领域最相关文章的可复制列表。据作者所知，这是第一份以可再现的方式介绍和分析最相关的最新文章清单的综述，其重点是 ITSF 的应用、技术和研究趋势。

{"title":"A literature review on satellite image time series forecasting: Methods and applications for remote sensing","authors":"Carlos Lara-Alvarez, Juan J. Flores, Hector Rodriguez-Rangel, Rodrigo Lopez-Farias","doi":"10.1002/widm.1528","DOIUrl":"https://doi.org/10.1002/widm.1528","url":null,"abstract":"Satellite image time-series are time series produced from remote sensing images; they generally correspond to features or indicators extracted from those images. With the increasing availability of remote sensing images and new methodologies to process such data, image time-series methods have been used extensively for assessing temporal pattern detection, monitoring, classification, object detection, and feature estimation. Since the study of time series is broad, this article focuses on analyzing articles related to forecasting the value of one or more attributes of the image time-series. The image time series forecasting (ITSF) problem appears in different disciplines; most focus on improving the quality of life by harnessing natural resources for sustainable development and minimizing the lethality of dangerous natural phenomena. Scientists tackle these problems using different tools or methods depending on the application. This review analyzes the field's leading, most recent contributions, grouping them by application area and solution methods. Our findings indicate that artificial neural networks, regression trees, support vector regression, and cellular automata are the most common methods for ITSF. Application areas address this problem as renewable energy, agriculture, and land-use change. This study retrieved and analyzed relevant information about the recent activity of image time series forecasting, generating a reproducible list of the most pertinent articles in the field published from 2009 to 2021. To the author's best knowledge, this is the first review presenting and analyzing a reproducible list of the most relevant state-of-the-art articles focusing on the applications, techniques, and research trends for ITSF.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139655906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of autonomous monitoring systems in mental health 心理健康自主监测系统调查

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-01-24 DOI: 10.1002/widm.1527

Abinaya Gopalakrishnan, R. Gururajan, Xujuan Zhou, Revathi Venkataraman, K. C. Chan, Niall Higgins

Smartphones and personal sensing technologies have made collecting data continuously and in real time feasible. The promise of pervasive sensing technologies in the realm of mental health has recently garnered increased attention. Using Artificial Intelligence methods, it is possible to forecast a person's emotional state based on contextual information such as their current location, movement patterns, and so on. As a result, conditions like anxiety, stress, depression, and others might be tracked automatically and in real‐time. The objective of this research was to survey the state‐of‐the‐art autonomous psychological health monitoring (APHM) approaches, including those that make use of sensor data, virtual chatbot communication, and artificial intelligence methods like Machine learning and deep learning algorithms. We discussed the main processing phases of APHM from the sensing layer to the application layer and an observation taxonomy deals with various observation devices, observation duration, and phenomena related to APHM. Our goal in this study includes research works pertaining to working of APHM to predict the various mental disorders and difficulties encountered by researchers working in this sector and potential application for future clinical use highlighted.This article is categorized under:Technologies > Machine LearningTechnologies > PredictionApplication Areas > Health Care

智能手机和个人传感技术使持续、实时地收集数据成为可能。最近，普适传感技术在心理健康领域的应用前景日益受到关注。利用人工智能方法，可以根据人的当前位置、运动模式等上下文信息预测人的情绪状态。因此，焦虑、压力、抑郁等情况可能会被自动实时跟踪。本研究旨在调查最先进的自主心理健康监测（APHM）方法，包括那些利用传感器数据、虚拟聊天机器人通信以及机器学习和深度学习算法等人工智能方法的方法。我们讨论了从传感层到应用层的自主心理健康监测的主要处理阶段，并对各种观察设备、观察持续时间以及与自主心理健康监测相关的现象进行了观察分类。我们在本研究中的目标包括有关 APHM 预测各种精神障碍的研究工作，以及在该领域工作的研究人员遇到的困难和未来临床使用的潜在应用。

{"title":"A survey of autonomous monitoring systems in mental health","authors":"Abinaya Gopalakrishnan, R. Gururajan, Xujuan Zhou, Revathi Venkataraman, K. C. Chan, Niall Higgins","doi":"10.1002/widm.1527","DOIUrl":"https://doi.org/10.1002/widm.1527","url":null,"abstract":"Smartphones and personal sensing technologies have made collecting data continuously and in real time feasible. The promise of pervasive sensing technologies in the realm of mental health has recently garnered increased attention. Using Artificial Intelligence methods, it is possible to forecast a person's emotional state based on contextual information such as their current location, movement patterns, and so on. As a result, conditions like anxiety, stress, depression, and others might be tracked automatically and in real‐time. The objective of this research was to survey the state‐of‐the‐art autonomous psychological health monitoring (APHM) approaches, including those that make use of sensor data, virtual chatbot communication, and artificial intelligence methods like Machine learning and deep learning algorithms. We discussed the main processing phases of APHM from the sensing layer to the application layer and an observation taxonomy deals with various observation devices, observation duration, and phenomena related to APHM. Our goal in this study includes research works pertaining to working of APHM to predict the various mental disorders and difficulties encountered by researchers working in this sector and potential application for future clinical use highlighted.This article is categorized under:\u0000Technologies > Machine Learning\u0000Technologies > Prediction\u0000Application Areas > Health Care\u0000","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"55 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139599391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The role of lifelong machine learning in bridging the gap between human and machine learning: A scientometric analysis 终身机器学习在缩小人类学习与机器学习之间差距方面的作用：科学计量分析

WIREs Data Mining and Knowledge Discovery

Pub Date : 2024-01-10 DOI: 10.1002/widm.1526

Muhammad Abulaish, Nesar Ahmad Wasi, Shachi Sharma

Due to advancements in data collection, storage, and processing techniques, machine learning has become a thriving and dominant paradigm. However, one of its main shortcomings is that the classical machine learning paradigm acts in isolation without utilizing the knowledge gained through learning from related tasks in the past. To circumvent this, the concept of Lifelong Machine Learning (LML) has been proposed, with the goal of mimicking how humans learn and acquire cognition. Human learning research has revealed that the brain connects previously learned information while learning new information from a single or small number of examples. Similarly, an LML system continually learns by storing and applying acquired information. Starting with an analysis of how the human brain learns, this paper shows that the LML framework shares a functional structure with the brain when it comes to solving new problems using previously learned information. It also provides a description of the LML framework, emphasizing its similarities to human brain learning. It also provides citation graph generation and scientometric analysis algorithms for the LML literatures, including information about the datasets and evaluation metrics that have been used in the empirical evaluation of LML systems. Finally, it presents outstanding issues and possible future research directions in the field of LML.

由于数据收集、存储和处理技术的进步，机器学习已成为一种蓬勃发展的主流模式。然而，它的一个主要缺点是，经典的机器学习范式是孤立行动的，没有利用从过去相关任务的学习中获得的知识。为了避免这种情况，人们提出了终身机器学习（Lifelong Machine Learning，LML）的概念，目的是模仿人类学习和获得认知的方式。人类学习研究表明，大脑在从单个或少量示例中学习新信息的同时，会将以前学习到的信息联系起来。同样，LML 系统通过存储和应用已获得的信息来不断学习。本文从分析人脑的学习方式入手，说明 LML 框架在利用以前学习的信息解决新问题方面与人脑具有相同的功能结构。本文还描述了 LML 框架，强调了它与人脑学习的相似之处。它还为 LML 文献提供了引文图生成和科学计量分析算法，包括 LML 系统实证评估中使用的数据集和评估指标的相关信息。最后，它介绍了 LML 领域的未决问题和未来可能的研究方向。

{"title":"The role of lifelong machine learning in bridging the gap between human and machine learning: A scientometric analysis","authors":"Muhammad Abulaish, Nesar Ahmad Wasi, Shachi Sharma","doi":"10.1002/widm.1526","DOIUrl":"https://doi.org/10.1002/widm.1526","url":null,"abstract":"Due to advancements in data collection, storage, and processing techniques, machine learning has become a thriving and dominant paradigm. However, one of its main shortcomings is that the classical machine learning paradigm acts in isolation without utilizing the knowledge gained through learning from related tasks in the past. To circumvent this, the concept of Lifelong Machine Learning (LML) has been proposed, with the goal of mimicking how humans learn and acquire cognition. Human learning research has revealed that the brain connects previously learned information while learning new information from a single or small number of examples. Similarly, an LML system continually learns by storing and applying acquired information. Starting with an analysis of how the human brain learns, this paper shows that the LML framework shares a functional structure with the brain when it comes to solving new problems using previously learned information. It also provides a description of the LML framework, emphasizing its similarities to human brain learning. It also provides citation graph generation and scientometric analysis algorithms for the LML literatures, including information about the datasets and evaluation metrics that have been used in the empirical evaluation of LML systems. Finally, it presents outstanding issues and possible future research directions in the field of LML.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139420462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0