WIREs Data Mining and Knowledge Discovery最新文献

{"title":"A Systematic Review of Artificial Intelligence ( AI )‐Based Task Scheduling Techniques for Cloud‐Fog Computing Environments","authors":"Sumit Bansal, Rinkle Rani, Himanshu Aggarwal","doi":"10.1002/widm.70139","DOIUrl":"https://doi.org/10.1002/widm.70139","url":null,"abstract":"The rapid proliferation of latency‐sensitive and data‐intensive applications has accelerated the adoption of cloud–fog computing architectures to support real‐time processing and distributed resource management. However, efficient task scheduling in cloud–fog environments remains a challenging problem because of resource heterogeneity, dynamic workloads, network variability, and stringent quality of service (QoS) requirements. Artificial intelligence (AI)‐based techniques, including machine learning (ML), deep learning (DL), deep reinforcement learning (DRL), and hybrid AI approaches, have emerged as promising solutions for addressing these challenges. This paper presents a systematic review of AI‐based task scheduling techniques in cloud–fog computing environments published between 2021 and 2025. Following a PRISMA‐inspired methodology, 74 high‐quality studies were selected and analyzed. The reviewed works are classified into ML, DL, DRL, and hybrid AI‐based scheduling categories and evaluated with respect to scheduling objectives, QoS parameters, datasets, evaluation tools, and implementation environments. The analysis reveals that AI‐driven scheduling approaches significantly improve key performance metrics, including task scheduling performance (14%–90%), latency reduction (15%–65%), energy optimization (15%–41%), load balancing (20%–45%), execution time reduction (15%–45%), and cost optimization (10%–32%). This review further highlights the increasing adoption of DL, DRL, and hybrid AI techniques for managing complex and dynamic cloud–fog infrastructures. Additionally, this study identifies critical research challenges, including scalability, resource heterogeneity, lack of standardized benchmark datasets, limited real‐world deployments, and insufficient consideration of network‐layer dynamics. Finally, emerging research directions and future opportunities for developing scalable, adaptive, and intelligent scheduling frameworks in cloud–fog computing environments are discussed. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Technologies &gt; Cloud Computing </jats:list-item> <jats:list-item> Technologies &gt; Internet of Things </jats:list-item> <jats:list-item> Technologies &gt; Artificial Intelligence </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198903","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":"Comparative Analysis of Multimodal GANs for Cross‐Modal Information Retrieval","authors":"Aamir Khan, S. Nisha Chandran, Krishna Mohan Pandey, Khalid Mohiuddin, Farhad Alam, Shaweta Sachdeva, Sharik Ahmad, D. R. Gangodkar","doi":"10.1002/widm.70140","DOIUrl":"https://doi.org/10.1002/widm.70140","url":null,"abstract":"Cross‐modal information systems have emerged as an important area of research that enables information retrieval in different modalities such as text, image and audio. Existing Cross‐modal retrieval methods focus on semantic consistency between modalities but rarely consider retrieval effectiveness in generative models. This study investigates the effectiveness of the Multimodal Generative Advertisement Network (MMGAN) in enhancing Cross‐modal Information Retrieval (CMIR) performance. Using benchmark datasets that include various modalities, it conducts a comprehensive analysis of MMGAN against several state‐of‐the‐art models, including traditional Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN). The experimental evaluation considers both representation quality and cross‐modal retrieval effectiveness. Controlled unimodal benchmarks are used to examine latent representation quality, whereas MS‐COCO, containing paired image–text samples, serves as the primary benchmark for genuine cross‐modal retrieval. Retrieval performance is assessed using Mean Average Precision (mAP), Precision@K, Recall@K, and NDCG@K for image‐to‐text and text‐to‐image retrieval, while NMI, ARI, and clustering accuracy are employed as supplementary measures of representation and semantic grouping quality. The results demonstrate the effectiveness of the proposed MMGAN‐based framework in learning semantically aligned multimodal representations and improving cross‐modal retrieval performance. Furthermore, it analyses the strengths and limitations of MMGAN, informing readers of their potential applications and exploring future research directions in CMIR. It contributes to the advancement of generative models to improve Cross‐modal retrieval systems, emphasizing more efficient and effective information retrieval.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198937","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 ‐Powered Surveillance Systems: A Comprehensive Review of Technologies, Applications, Challenges, and Deployment Considerations","authors":"Tanzila Kehkashan, Abdul Rehman, Ahmad Sami Al‐Shamayleh, Nor Azman Ismail, Adnan Akhunzada, Imran Ashraf, Nikola Ivković, Shahbaz Pervez","doi":"10.1002/widm.70137","DOIUrl":"https://doi.org/10.1002/widm.70137","url":null,"abstract":"Artificial intelligence (AI) has rapidly transformed smart surveillance systems, significantly leveraging technologies such as deep learning, computer vision, and adaptive monitoring. Driven by the need for real‐time object detection, behavioral analysis, and automated alerting, AI‐powered surveillance is increasingly vital for enhancing public safety, operational efficiency, and decision‐making in fields such as security, health‐care, transportation, and environmental monitoring. The growing societal impact and complexity of surveillance necessitate a comprehensive examination of foundational methodologies, key application domains, and the technical, ethical, and operational challenges associated with these systems. Analysis reveals that although AI‐driven surveillance solutions offer improved accuracy, scalability, and responsiveness, they continue to face notable barriers including high computational demands, privacy and data quality concerns, interpretability limitations, and inadequate regulatory frameworks. The review further identifies critical emerging trends, such as sustainable AI practices, human‐AI collaborative models, and the integration of AI with the Internet of Things (IoT) and blockchain technologies, as significant influences shaping the future trajectory of the field. Addressing these challenges through ongoing research, innovation, and responsible deployment is essential to fully unlock the societal and industrial potential of AI‐enabled surveillance solutions.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172896","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":"CASARS Recommender Systems: A Systematic Review on Methods, Datasets, Limitations, and Future Directions","authors":"Matheus Henrique Cecilio Leme, Vinicius Sylvestre Simm, Douglas Rorie Tanno, Marcos Aurelio Domingues","doi":"10.1002/widm.70135","DOIUrl":"https://doi.org/10.1002/widm.70135","url":null,"abstract":"Among the main objectives of recommender systems, the minimization of the problem known as information overload stands out. The personalization of recommendations, which emerge as users interact across various digital platforms, aims to mitigate this recurring issue in the modern world. Over the years, different recommendation paradigms have been explored, revealing both the potentialities and limitations of each technique and their respective proposed models. As a result, it has been shown that combining two recommendation paradigms, that is, the hybridization of methodologies, has the potential to be a more efficient alternative to meet the expectations and needs of these systems. Accordingly, this study presents a systematic literature review on recommender systems, focusing on two paradigms: context‐aware recommendation and session‐aware recommendation (CASARS). Our research seeks to elucidate which studies in the literature address recommendation strategies that leverage users' historical information to contribute to item recommendations throughout their ongoing sessions, as well as mechanisms for incorporating situational contextual information, that is, the user's context expressed strictly through interactions among items within the current session. Our goal is to outline the trajectory of the proposed models, identify the predominant application domains, critically analyze the evaluation methodologies, and, based on that, highlight the fundamental challenges and future research directions. In this study, we adopted a rigorous systematic review protocol, through which 25 articles were selected and thoroughly analyzed. The analysis reveals a clear chronological and conceptual progression of architectures, evolving from sequential models based on Recurrent Neural Networks to more complex structural representations with Graph Neural Networks, culminating in current approaches employing Transformers and attention mechanisms. We identified the main application focuses in domains such as e‐commerce, which accounts for 44% of the studies, and highlighted a critical gap in evaluation practices, characterized by the preference for accuracy metrics, such as Recall and NDCG, over dimensions essential to user experience, such as diversity, novelty, and serendipity. Although the field demonstrates rapid evolution and technical sophistication, future research directions were identified and thoroughly discussed.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"245 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149123848","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":"Clinically Meaningful Multimodality in Medical AI : Complementarity, Redundancy, and Conflict Across Clinical Data Streams","authors":"Zhipeng Wang, Aiqing Li, Wei Xiong, Bo Zhou, Koo Han Yoo, Yifan Wang, Qi Zhang, Dechao Feng","doi":"10.1002/widm.70136","DOIUrl":"https://doi.org/10.1002/widm.70136","url":null,"abstract":"Multimodal artificial intelligence (AI) increasingly integrates imaging, pathology, omics, electronic health records, clinical text, and physiological signals. However, multimodality is often defined by the number of input types, leaving unclear when additional data improve a clinical decision, duplicate available evidence, or introduce informative disagreement. This concept‐driven critical perspective proposes a relationship‐centered framework for clinically meaningful multimodality. Drawing on a focused synthesis of recent PubMed literature and foundational studies, we define complementarity, redundancy, and conflict as context‐dependent relationships shaped by the clinical question, decision point, timing, and available evidence. We examine how attention mechanisms, gating mechanisms, graph structures, mixture‐of‐experts models, and modular encoders can represent or regulate these relationships. We then organize the framework across five dimensions linking modality relationships to clinical meaning, representative contexts, modeling strategies, and evaluation approaches. Relationship‐centered design principles are further connected to a staged evaluation pathway spanning representation‐level validation, relationship‐aware technical validation, clinical‐utility assessment, prospective implementation, and monitoring. Pulmonary nodule assessment, reperfusion decision‐making in acute ischemic stroke, and immunotherapy response assessment illustrate how the same modality may shift between complementary, redundant, and conflicting roles across clinical workflows. We argue that multimodal systems should be evaluated not only by discrimination, but also by marginal modality value, calibration, reduced‐input robustness, behavior under missing, asynchronous, or discordant evidence, and effects on clinical decisions. Future research should prioritize relationship‐preserving representations, decision‐aware modality acquisition, relationship‐specific benchmarks, and human‐centered evaluation. Clinically meaningful multimodality depends not on how many data streams a model can fuse, but on whether it uses each stream for a defined clinical purpose while preserving evidence provenance, timing, and uncertainty.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149123692","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":"Smart Farming using Machine Learning: A Review on Recent Trends and Approaches of Crop Yield Prediction","authors":"Deeksha Tripathi, Saroj K. Biswas","doi":"10.1002/widm.70132","DOIUrl":"https://doi.org/10.1002/widm.70132","url":null,"abstract":"Crop Yield Prediction (CYP) supports the finest decision‐making to support farmers for crop yields forecasting effectively which can help farmers make precise decisions about planting, harvesting, and development of their crops. However, CYP is a challenging endeavor due to the complexity of the underlying mechanisms and the impact of many variables, such as weather patterns, soil conditions, and crop management practices. Researchers have proposed different CYP models using Machine Learning (ML) techniques on different datasets and experimental settings. However, the reported performance of these models is often not directly comparable due to variations in data sources, spatial and temporal scales, crop types, and evaluation protocols. In this context, this review provides a conceptually integrated and analytical synthesis of CYP approaches. Rather than identifying a universally optimal model, this study highlights that model effectiveness is highly context‐dependent, varying with data modality, agro‐climatic conditions and application requirements. The article also discusses advantages and limitations of different ML models for CYP by a thorough analytical analysis. Besides, it highlights the key challenges for the implementation of ML models for CYP to produce improved crop productivity. This paper has the potential to be a helpful resource for both beginners and experts in the field of agriculture, particularly those who focus on ML‐based CYP and employing different varieties of crops. The outcomes of this review reveal that learning paradigms combined with multi‐source data integration deliver the most reliable yield predictions while also exposing critical gaps in crop diversity, climate coverage, and real‐world expert system deployment. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Algorithmic Development &gt; Ensemble Methods </jats:list-item> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> <jats:list-item> Fundamental Concepts of Data and Knowledge &gt; Explainable AI </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085981","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 Comprehensive Review on Federated and Incremental Learning in Spatial Crowdsourcing Systems","authors":"Md Mujibur Rahman, Quazi Mamun, Michael Bewong, Md Zahidul Islam","doi":"10.1002/widm.70128","DOIUrl":"https://doi.org/10.1002/widm.70128","url":null,"abstract":"Spatial Crowdsourcing (SC) coordinates workers and requesters through personal mobile devices to complete location‐dependent tasks in real time. The combination of continuous worker mobility, self‐interested participants, and real‐time service requirements creates a learning environment that is more adversarial, more dynamic, and more resource‐constrained than the settings addressed by existing federated learning (FL) surveys. FL preserves location data privacy but remains vulnerable to inference, poisoning, Byzantine, and Sybil attacks, whose consequences in SC directly affect task allocation, pricing, and worker safety. Incremental learning (IL) addresses the continuous concept drift caused by mobility and demand shifts, enabling model adaptation without full retraining on resource‐constrained devices. Despite their complementary roles, FL and IL have largely been surveyed in parallel, and their integration within SC has not been systematically addressed. This survey analyses 172 primary studies published between 2019 and 2026. It contributes: an SC‐focused assessment of FL and IL techniques identifying where non‐SC results do not transfer; a connected taxonomy linking SC‐specific threats to evaluated countermeasures and their trade‐offs; a conceptual FL‐IL framework that jointly addresses privacy, security, and continual adaptation; a review of available datasets and open‐source tooling with identified benchmark gaps; and a prioritized research agenda on SC‐specific assumptions and deployment conditions that require further investigation. The survey is intended to guide researchers and practitioners in developing secure, adaptive, and resource‐efficient SC systems. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Technologies &gt; Crowdsourcing </jats:list-item> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> <jats:list-item> Commercial, Legal, and Ethical Issues &gt; Security and Privacy </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085980","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 Evolution of Question Generation: Analyzing the Impact of Large Language Models","authors":"Pawan Bhakuni, Ganesh Bahadur Singh, Aanchal Punia, Himanshu Suyal, Nitin Sharma","doi":"10.1002/widm.70133","DOIUrl":"https://doi.org/10.1002/widm.70133","url":null,"abstract":"Automatic question generation (AQG) has evolved quite a bit with the advent of transformer‐based large language models (LLMs), transitioning from rule‐based methods and early neural approaches to more adaptable transformer‐based and LLM‐based systems. This process has enabled greater potential for AQG but, at the same time, has posed new problems regarding control, structure, evaluation, and grounding of the generated questions in a wide range of fields. In this survey, we look at the evolution of AQG with respect to five different aspects—answer format, contextual scale, cognitive complexity, control, and multilingual or multimodal capacity. In our analysis of this transition, we focus on the progression of AQG from rule‐based and neural methods to transformer‐based and LLM‐based methods with an emphasis on prompting, retrieval‐augmented generation (RAG), and agentic workflows. Furthermore, we also discuss the technologies involved in modern AQG, such as LLMs, SLMs, retrieval, and agentic systems with reasoning and memory and tool‐use capabilities. Moreover, this paper contributes a taxonomy that captures paradigm shifts in model architecture, prompting, and pipeline design. It also compiles the existing datasets, evaluation techniques, and benchmarks, emphasizing the increasing relevance of retrieval‐based integration, controllable prompting, and agentic pipelines. These advances create ways for the generation of context‐ and cognition‐aware questions tailored to users' needs but also present new challenges associated with factual grounding and evaluation. In summary, the survey reflects the current trend toward developing more flexible, resilient, and multimodal AQG systems with wide‐ranging applications, including educational settings, knowledge‐based systems across various domains, and human‐computer collaboration. The paper helps establish a solid foundation for future research on question generation through its critical analysis of current trends and research directions.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009800","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 Data‐Driven Multi‐Factor Model for Android Mobile Security Risk Assessment","authors":"Anuradha Dahiya, Sukhdip Singh, Gulshan Shrivastava","doi":"10.1002/widm.70134","DOIUrl":"https://doi.org/10.1002/widm.70134","url":null,"abstract":"Android is one of the most widely adopted mobile operating systems and, because of its extensive application ecosystem and large user base, remains an important target for cybersecurity threats. This study investigates the factors associated with respondents' perceived likelihood of increases in Android‐related threats and in Android device security, using a structured questionnaire‐based survey. A total of 403 valid responses have been collected from Android users belonging to different professional groups. Principal component analysis (PCA) has been used to identify meaningful security‐related components from the questionnaire items, followed by multiple linear regression and binary logistic regression to examine the relationships between the identified factors and Android device security and respondents' perceived likelihood of an increase in Android‐related threats. The findings indicate that selected security‐related factors have statistically significant relationships with device security and respondents' perceived likelihood of an increase in Android‐related threats, whereas some user‐related factors do not demonstrate statistically significant associations. The results highlight the importance of system security, safe usage practices, user behavior, malware protection, and application‐related security considerations in reducing security risks. The study provides a user‐oriented statistical perspective on Android security and establishes a basis for future research involving technical vulnerability assessment, automated threat detection, and machine‐learning‐based security mechanisms.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009979","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":"Time Series Domain Adaptation: A Review","authors":"M. T. Furqon, Mahardhika Pratama, Wisnu Jatmiko","doi":"10.1002/widm.70130","DOIUrl":"https://doi.org/10.1002/widm.70130","url":null,"abstract":"Time‐series domain adaptation (TSDA) has emerged as a critical paradigm for addressing distribution shifts across sensing environments, operating conditions, and acquisition devices in sequential data. Building upon the theoretical foundations of vision domain adaptation, TSDA introduces additional challenges stemming from temporal dependency, spectral variability, and privacy constraints inherent to real‐world systems. This survey provides a comprehensive and systematic review of TSDA methods from the perspectives of access‐privacy constraints, category‐space semantics, and source‐target topology, three orthogonal axes that unify existing approaches within a common taxonomy. Specifically, we analyze representative algorithms across four access regimes (UDA, SFUDA, BBDA, FDA), four category‐space configurations (closed, partial, open, universal), and three topological structures (single‐, multi‐, and multi‐target), highlighting their theoretical motivations, architectural trends, and cross‐domain generalization strategies. We further examine real‐world applications across diverse temporal domains, linking methodological evolution to practical deployment constraints. Benchmarking results from recent studies are summarized to establish empirical baselines across representative time‐series datasets and applied domains, encompassing both core classification benchmarks and real‐world TSDA applications. Finally, we discuss open challenges and future directions in this field.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148915384","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}