WIREs Data Mining and Knowledge Discovery最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"A survey of episode mining","authors":"Oualid Ouarem, Farid Nouioua, Philippe Fournier-Viger","doi":"10.1002/widm.1524","DOIUrl":"https://doi.org/10.1002/widm.1524","url":null,"abstract":"Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode-mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up-to-date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"107 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455943","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":"Multispectral data mining: A focus on remote sensing satellite images","authors":"Sin Liang Lim, Jaya Sreevalsan-Nair, B. S. Daya Sagar","doi":"10.1002/widm.1522","DOIUrl":"https://doi.org/10.1002/widm.1522","url":null,"abstract":"This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state-of-the-art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"35 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455933","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}