WIREs Data Mining and Knowledge Discovery最新文献

{"title":"Review on Information Fusion‐Based Data Mining for Improving Complex Anomaly Detection","authors":"Sorin‐Claudiu Moldovan, Laszlo Barna Iantovics","doi":"10.1002/widm.70017","DOIUrl":"https://doi.org/10.1002/widm.70017","url":null,"abstract":"Anomaly predicated upon multiple distributed hybrid sensors frequently uses hybrid approaches, integrating techniques derived from statistical analysis, probability, data mining, machine learning, deep learning, and signal denoising. Many of these methods are based on the analysis of irregularities, data continuity, correlation, and data consistency, aiming to discern anomalous patterns from normal behavior. By leveraging these techniques information fusion aims to enhance situational awareness, detect potential threats or abnormalities, and improve decision‐making processes in complex environments. It addresses uncertainties by integrating data from diverse sources, thereby enhancing performance, and reducing dependency on individual sensors. This study examines applications based on single and multiple sensor data, revealing common strategies, identifying strengths and weaknesses, and potential solutions for detecting and diagnosing anomalies by analyzing low, large, and complex data derived from the context of homogeneous or heterogeneous systems. Information fusion techniques are evaluated for their performance on various levels of algorithm complexity. This in‐depth bibliographic study involved searching top indexing databases such as Web of Science and Scopus. The inclusion criteria were articles published between 2012 and 2024. The search capitalized on specific keywords as follows: “sensor malfunction,” “sensor anomaly,” “sensor failure,” “sensor fusion,” and “anomaly data mining.” Publications that did not strictly focus on analytical processing for anomaly detection, diagnosis, and prognosis in sensor data were excluded. In conclusion, the practice of information fusion promotes transparency by elucidating the process of combining information, thereby enabling the inclusion of multitude of perspectives, and aligning with established best practices in the field. Data deviation remains the primary criterion for detecting anomalies using mostly deep learning and extensively hybrid techniques. Nevertheless, state‐of‐the‐art algorithms based on neural networks still require further contextual interpretation and analysis. Functional safety and safety of intended functionality breaching can lead to decision‐making errors, physical harm, and erosion of trust in autonomous systems. This is due to the lack of interpretability in AI approaches, making it challenging to predict and understand the system's behavior under various conditions.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930745","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 Causality in Explainable Artificial Intelligence","authors":"Gianluca Carloni, Andrea Berti, Sara Colantonio","doi":"10.1002/widm.70015","DOIUrl":"https://doi.org/10.1002/widm.70015","url":null,"abstract":"Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the “optimal” form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue‐worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920261","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":"AI‐Assisted Literature Review: Integrating Visualization and Geometric Features for Insightful Analysis","authors":"Grigorios Papageorgiou, Ekaterini Skamnia, Polychronis Economou","doi":"10.1002/widm.70016","DOIUrl":"https://doi.org/10.1002/widm.70016","url":null,"abstract":"Rapid advancements in technology and Artificial Intelligence have increased the volume of scientific research, making it challenging for researchers and scholars to keep pace with the evolving literature and state‐of‐the‐art techniques and methods. Traditional review papers offer a way to mitigate these difficulties but are often time‐consuming and labor‐intensive. This article introduces a novel AI‐assisted narrative review methodology that integrates advanced text retrieval and visualization techniques, enhanced with geometric features, to address this. The proposed approach relies on the automatic identification of research topics/clusters within a large different document corpus of different time periods. This approach not only facilitates the systematic exploration of trends over time but also serves as a valuable adjunct, enabling experts to focus on specific, homogeneous areas within scientific fields/clusters. Initially, the methodology in its generality and mapping of the evolution of emerging topics are described, revealing the temporal dynamics and interconnections within the literature of time series anomalies. Subsequently, the proposed method is applied to time series data and an in‐depth exploration of the identified dominant cluster is presented. The cluster involves advanced techniques and models for anomaly detection in time series analysis. Focusing on such a homogeneous subfield enables the derivation of a wealth of characteristics and outcomes regarding the evolution of this topic, revealing its temporal dynamics and trends. The review process demonstrates the effectiveness of the proposed AI‐driven approach in literature reviews and provides researchers with a powerful tool to synthesize and interpret complex, dynamically changing, advanced scientific fields.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898093","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":"Neuromorphic Computing and Applications: A Topical Review","authors":"Pavan Kumar Enuganti, Basabdatta Sen Bhattacharya, Teresa Serrano Gotarredona, Oliver Rhodes","doi":"10.1002/widm.70014","DOIUrl":"https://doi.org/10.1002/widm.70014","url":null,"abstract":"Neuromorphic computers achieve energy efficiency by emulating brain structure and event‐driven processing that reduces energy consumption significantly. An increasing interest in this technology started in the initial years of this millennium, sparked by the awareness and concern on the ever‐increasing power demands of modern‐day computing. In current times, there are several neuromorphic computers and sensors that continue to be developed in both industry and academic research. The focus of this survey is on the neuromorphic computing applications of these devices that include brain‐inspired neural networks, brain‐inspired artificial neural networks, and Hybrid circuits comprising both artificial and brain‐inspired units of computation. Many of these applications use neuromorphic sensors as input devices. We have surveyed three specific neuromorphic computers viz. SpiNNaker, TrueNorth, Loihi, and one neuromorphic sensor viz. Dynamic vision sensor (DVS)‐based electronic retina; the demonstration of neuromorphic computing and applications using these devices far outnumbers those on the others that are currently available, which forms the basis of our choice. The applications include low‐power cognitive machine intelligence as well as neuropathological understanding and knowledge discovery. Overall, our survey identifies the potential for neuromorphic computing to provide low power, low cost, and dynamic solutions for societal and scientific problems in the not‐too‐distant future.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889727","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 Process Mining for Curricular Analysis","authors":"Daniel Calegari, Andrea Delgado","doi":"10.1002/widm.70005","DOIUrl":"https://doi.org/10.1002/widm.70005","url":null,"abstract":"Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. It is based on Process Mining (PM), which involves gathering records (logs) of events to discover process models and analyze the data from a process‐centric perspective. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. This is important for institutional curriculum decision‐making and quality improvement. Therefore, academic institutions can benefit from organizing the existing techniques, capabilities, and limitations. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research. We reviewed 27 primary studies published across seven major databases. Our analysis classified these studies into five main research objectives: discovery of educational trajectories, identification of deviations in student behavior, bottleneck analysis, dropout/stopout analysis, and generation of recommendations. Key findings highlight challenges such as standardization to facilitate cross‐university analysis, better integration of process and data mining techniques, and improved tools for educational stakeholders. This review provides a comprehensive overview of the current landscape in curricular process mining and outlines specific research opportunities to support more robust and actionable curricular analyses in educational settings.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872832","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":"How AI Contributes to Poverty Alleviation: A Systematic Literature Review","authors":"Sepehr Ghazinoory, Mercedeh Pahlavanian, Mehdi Fatemi, Fatemeh Parvin, Sayna Ahad Bhat","doi":"10.1002/widm.70003","DOIUrl":"https://doi.org/10.1002/widm.70003","url":null,"abstract":"Artificial intelligence and smart city initiatives are pivotal to achieving sustainable development goals, particularly in poverty alleviation. Artificial intelligence has high potential in controlling, monitoring, and alleviating poverty, offering innovative solutions that can improve living conditions and welfare. AI‐assisted poverty alleviation requires a comprehensive approach to creating supportive institutions, appropriate regulations, comprehensive training programs, and resource allocation. This article systematically reviews the AI‐poverty literature based on problem‐oriented innovation system functions. By screening existing articles, it analyzes 30 sources specifically focused on AI‐enhanced poverty control to highlight the articles' focus and identify the neglected functions. The findings can help governments, policymakers, and scholars guide decisions to address poverty through AI and fill the existing gaps and shortcomings.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857474","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":"Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives","authors":"Kwok P. Chun, Thanti Octavianti, Nilay Dogulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Rowberry, Pingyu Fan, Mark Everard, Maria Francesch‐Huidobro, Wellington Migliari, David M. Hannah, John Travis Marshall, Rafael Tolosana Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño‐Golle, Ye Mu, Manuel Davila Delgado, Elizabeth Silvestre Espinoza, Martin Keulertz, Deepak Gopinath, Cheng Li","doi":"10.1002/widm.70011","DOIUrl":"https://doi.org/10.1002/widm.70011","url":null,"abstract":"Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. From a co‐production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasize environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony. Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy. The responsible execution of AI approaches, based on the dynamic interplay between AI, law, and environmental risk, promotes sustainable and equitable practices in data mining.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837122","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":"Algorithmic Profiling and Facial Recognition in EU Border Control: Examining ETIAS Decision‐Making, Privacy and Law","authors":"Abhishek Thommandru, Varda Mone, Fayzulloyev Shokhijakhon, Giyosbek Mirzayev","doi":"10.1002/widm.70013","DOIUrl":"https://doi.org/10.1002/widm.70013","url":null,"abstract":"The growing use of algorithmic and biometric technologies in border control is part of a larger trend in global security governance that has significant legal and ethical implications for their effect on individual rights and procedural justice. As central features in the EU's shifting security regime, ETIAS and facial recognition technologies deploy algorithmic profiling and biometric risk assessment to screen visa‐exempt third‐country nationals. The research systematically examines the decision‐making processes of ETIAS and the overall facial recognition system, demonstrating the interplay between algorithmic risk assessments and discretionary human discretion by national authorities. It contends that the algorithmic profiling lack of transparency, combined with sweeping national security exceptions, produces a procedural void, in which the right to reasoned decisions and effective remedies is compromised. Second, the use of interoperable databases and risk indicators puts core data protection principles into jeopardy, notably purpose limitation and the right to be forgotten. This paper also argues that ETIAS and the application of facial recognition technologies represent a larger trend toward “techno‐regulatory assemblages” in EU governance, where technological infrastructures increasingly influence legal and administrative decisions. It critically assesses whether the human oversight mechanisms incorporated within ETIAS National Units are adequate to prevent the risks involved in automated decision‐making, especially in the face of strict time pressures and security requirements. The study detects a latent paradox: though these systems aim to strengthen a “Security Union,” they might inadvertently lead to an “Insecurity Union” by undermining transparency, procedural protections, and citizen rights.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"31 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822888","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":"Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems","authors":"Tamanna Sachdeva, Lalit Mohan Goyal, Mamta Mittal","doi":"10.1002/widm.70006","DOIUrl":"https://doi.org/10.1002/widm.70006","url":null,"abstract":"Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommendations. E‐commerce giants such as Amazon, Netflix, and YouTube are offering recommendations to users based on their interests, past experiences, demographic information, etc. hence, increasing the user's engagement on these applications. This study offers a comprehensive review of recommendation systems, covering their types, fundamental techniques, and emerging trends, with a focus on the predictive models and algorithms that power personalization. This study shows how in comparison to traditional collaborative and content‐based recommendation systems‐building techniques, the novel approaches of deep learning, graph‐based techniques, meta‐learning, few‐shot learning, exploration, and federated learning offer promising prospects to improve recommendation systems' scalability, privacy‐preserving abilities, and accuracy. These advanced methods deliver more diverse, context‐aware, and personalized recommendations by leveraging large‐scale data and complex predictive algorithms. Furthermore, this paper depicts forthcoming trajectories in the field of recommendation systems, including the adoption of graph‐based approaches, federated learning, and the exploration of ethical considerations. By mapping the current landscape of prediction‐driven personalization and identifying emerging trends, this review serves as a valuable resource for scholars and practitioners seeking to deepen their understanding of the field and drive innovation in recommendation systems. Readers can expect to gain insights into both foundational and cutting‐edge techniques and how these can shape the future of personalized recommendations.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823019","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 Brief Review on Benchmarking for Large Language Models Evaluation in Healthcare","authors":"Leona Cilar Budler, Hongyu Chen, Aokun Chen, Maxim Topaz, Wilson Tam, Jiang Bian, Gregor Stiglic","doi":"10.1002/widm.70010","DOIUrl":"https://doi.org/10.1002/widm.70010","url":null,"abstract":"This paper reviews benchmarking methods for evaluating large language models (LLMs) in healthcare settings. It highlights the importance of rigorous benchmarking to ensure LLMs' safety, accuracy, and effectiveness in clinical applications. The review also discusses the challenges of developing standardized benchmarks and metrics tailored to healthcare‐specific tasks such as medical text generation, disease diagnosis, and patient management. Ethical considerations, including privacy, data security, and bias, are also addressed, underscoring the need for multidisciplinary collaboration to establish robust benchmarking frameworks that facilitate LLMs' reliable and ethical use in healthcare. Evaluation of LLMs remains challenging due to the lack of standardized healthcare‐specific benchmarks and comprehensive datasets. Key concerns include patient safety, data privacy, model bias, and better explainability, all of which impact the overall trustworthiness of LLMs in clinical settings.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805785","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}