WIREs Data Mining and Knowledge Discovery最新文献

{"title":"Machine learning applied to tourism: A systematic review","authors":"José Carlos Sancho Núñez, Juan A. Gómez‐Pulido, Rafael Robina Ramírez","doi":"10.1002/widm.1549","DOIUrl":"https://doi.org/10.1002/widm.1549","url":null,"abstract":"The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Application Areas &gt; Society and Culture</jats:list-item> <jats:list-item>Technologies &gt; Machine Learning</jats:list-item> <jats:list-item>Application Areas &gt; Business and Industry</jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545848","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}

{"title":"A systematic review of multidimensional relevance estimation in information retrieval","authors":"Georgios Peikos, Gabriella Pasi","doi":"10.1002/widm.1541","DOIUrl":"https://doi.org/10.1002/widm.1541","url":null,"abstract":"In information retrieval, relevance is perceived as a multidimensional and dynamic concept influenced by user, task, and domain factors. Relying on this perspective, researchers have introduced multidimensional relevance models addressing diverse search tasks across numerous knowledge domains. Through our systematic review of 72 studies, we categorize research based on domain specificity and the distinct relevance aspects employed for estimating multidimensional relevance. Moreover, we highlight the approaches used to aggregate scores related to these factors and rank information items. Our insights underline the importance of concise definitions and unified methods for estimating relevance factors within and across domains. Finally, we identify benchmark collections for evaluations based on multiple relevance aspects while underscoring the necessity for new ones. Our findings suggest that large language models hold considerable promise for shaping future research in this field, mainly due to their relevance labeling abilities.This article is categorized under:\u0000Application Areas > Science and Technology\u0000Technologies > Computational Intelligence\u0000","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"89 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002250","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}

{"title":"Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions","authors":"Vankamamidi S. Naresh, Guduru V. N. S. R. Ratnakara Rao, D. V. N. Prabhakar","doi":"10.1002/widm.1539","DOIUrl":"https://doi.org/10.1002/widm.1539","url":null,"abstract":"This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real-time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward-looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527496","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}

{"title":"Navigating the metaverse: A technical review of emerging virtual worlds","authors":"H. M. K. K. M. B. Herath, Mamta Mittal, Aman Kataria","doi":"10.1002/widm.1538","DOIUrl":"https://doi.org/10.1002/widm.1538","url":null,"abstract":"The metaverse, a burgeoning virtual reality realm, has garnered substantial attention owing to its multifaceted applications. Rapid advancements and widespread acceptance of metaverse technologies have birthed a dynamic and intricate digital landscape. As various platforms, virtual worlds, and social networks within the metaverse increase, there is a growing imperative for a comprehensive analysis of its implications across societal, technological, and business dimensions. Notably, existing review studies have, for the past decade, primarily overlooked a metaverse-based multidomain approach. A meticulous examination encompassing 207 research studies delves into the technological innovation of the metaverse, elucidating its future trajectory and ethical imperatives. Additionally, the article introduces the term “<i>MetaWarria</i>” to conceptualize potential conflicts arising from metaverse dynamics. The study discerns that healthcare (45%) and education (22%) are pivotal sectors steering metaverse developments, while the entertainment sector (9%) reshapes the corporate landscape. Artificial intelligence (AI) plays a 9% role in enhancing the metaverse's marketing and user experience. Security, privacy, and policy concerns (11%) are addressed due to escalating threats, yielding practical solutions. The analysis underscores the metaverse's profound influence (57%) on the digital realm, a phenomenon accelerated by the COVID-19 pandemic. The article culminates in contemplating the metaverse's role in future warfare and national security, introducing “<i>MetaWarria</i>” as a conceptual framework for such discussions.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140333758","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}

{"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}

{"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}

{"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}

{"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":"<p>Wang, C., Zhang, Q., Liu, W., Liu, Y. &amp; Miao, L. Facial feature discovery for ethnicity recognition. <i>WIREs Data Mining Knowl. Discov</i>. 9, e1278 (2019). https://doi.org/10.1002/widm.1278. <i>WIREs Data Mining Knowl. Discov</i>., 10, e1386 (2020). https://doi.org/10.1002/widm.1386</p>\u0000<p>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.</p>\u0000<p>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 <b>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</b>. 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. <b>The authors have not provided</b> 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.</p>\u0000<p>The online version of the originally published Expression of Concern has been corrected accordingly.</p>\u0000<p><b>REFERENCES</b></p>\u0000<p>Duan, X., Wang, C., Liu, X., Li, Z., Wu, J., &amp; Zhang, H. (2010). Ethnic Features Extraction and Recognition of Human Faces. <i>2010 second International Conference on Advanced Computer Control</i>, 2, 125–130. https://doi.org/10.1109/ICACC.2010.5487194</p>\u0000<p>Wang, C., Zhang, Q., Dian, X., &amp; Gan, J. (2018). Multi-ethnical Chinese facial characterization and analysis. <i>Multimedia Tools and Applications</i>, 77, 30,311–30,329. https://doi.org/10.1007/s11042-018-6,018-1</p>\u0000<p><b>Ma, Y. (2012). <i>The Technique Research of Multi-Minorities Facial Expression Understanding and Analysis</i> [Master's Thesis, Northeastern University, Shenyang, China]</b>. https://www.doc88.com/p-6721329496334.html</p>","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}

{"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}

{"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}