Synthetic media or "deepfakes" are making great advances in visual quality, diversity, and verisimilitude, empowered by large‐scale publicly accessible datasets and rapid technical progress in deep generative modeling. Heralding a paradigm shift in how online content is trusted, researchers in digital image forensics have responded with different proposals to reliably detect AI‐generated images in the wild. However, binary classification of image authenticity is insufficient to regulate the ethical usage of deepfake technology as new applications are developed. This article provides an overview of the major innovations in synthetic forgery detection as of 2020, while highlighting the recent shift in research towards ways to attribute AI‐generated images to their generative sources with evidence. We define the various categories of deepfakes in existence, the subtle processing traces and fingerprints that distinguish AI‐generated images from reality and each other, and the different degrees of attribution possible with current understanding of generative algorithms. Additionally, we describe the limitations of synthetic image recognition methods in practice, the counter‐forensic attacks devised to exploit these limitations, and directions for new research to assure the long‐term relevance of deepfake forensics. Reliable, explainable, and generalizable attribution methods would hold malicious users accountable for AI‐enabled disinformation, grant plausible deniability to appropriate users, and facilitate intellectual property protection of deepfake technology.
{"title":"Deepfake attribution: On the source identification of artificially generated images","authors":"Brandon Khoo, Raphaël C.-W. Phan, C. H. Lim","doi":"10.1002/widm.1438","DOIUrl":"https://doi.org/10.1002/widm.1438","url":null,"abstract":"Synthetic media or \"deepfakes\" are making great advances in visual quality, diversity, and verisimilitude, empowered by large‐scale publicly accessible datasets and rapid technical progress in deep generative modeling. Heralding a paradigm shift in how online content is trusted, researchers in digital image forensics have responded with different proposals to reliably detect AI‐generated images in the wild. However, binary classification of image authenticity is insufficient to regulate the ethical usage of deepfake technology as new applications are developed. This article provides an overview of the major innovations in synthetic forgery detection as of 2020, while highlighting the recent shift in research towards ways to attribute AI‐generated images to their generative sources with evidence. We define the various categories of deepfakes in existence, the subtle processing traces and fingerprints that distinguish AI‐generated images from reality and each other, and the different degrees of attribution possible with current understanding of generative algorithms. Additionally, we describe the limitations of synthetic image recognition methods in practice, the counter‐forensic attacks devised to exploit these limitations, and directions for new research to assure the long‐term relevance of deepfake forensics. Reliable, explainable, and generalizable attribution methods would hold malicious users accountable for AI‐enabled disinformation, grant plausible deniability to appropriate users, and facilitate intellectual property protection of deepfake technology.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"6 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85441320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well‐considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision‐making through visualizations that facilitate interaction with algorithms. Although many visual analytics systems have been developed for healthcare, a clear overview of their explanation techniques is lacking. Therefore, we review 71 visual analytics systems for healthcare, and analyze how they explain advanced algorithms through visualization, interaction, shepherding, and direct explanation. Based on our analysis, we outline research opportunities and challenges to further guide the exciting rapprochement of visual analytics and healthcare.
{"title":"Explaining artificial intelligence with visual analytics in healthcare","authors":"Jeroen Ooge, G. Štiglic, K. Verbert","doi":"10.1002/widm.1427","DOIUrl":"https://doi.org/10.1002/widm.1427","url":null,"abstract":"To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well‐considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision‐making through visualizations that facilitate interaction with algorithms. Although many visual analytics systems have been developed for healthcare, a clear overview of their explanation techniques is lacking. Therefore, we review 71 visual analytics systems for healthcare, and analyze how they explain advanced algorithms through visualization, interaction, shepherding, and direct explanation. Based on our analysis, we outline research opportunities and challenges to further guide the exciting rapprochement of visual analytics and healthcare.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"290 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77503803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Histopathology is diagnosis based on visual examination of tissue sections under a microscope. With the growing number of digitally scanned tissue slide images, computer‐based segmentation and classification of these images is a high‐demand area of research. Convolutional neural networks (CNNs) constitute the most popular classification architecture for a variety of image classification problems. However, applying CNNs to histology slides is not a trivial task and has several challenges, ranging from variations in the colors of slides to excessive high resolution and lack of proper labeling. In this advanced review, we introduce the application of CNN‐based architectures to digital histological image analysis, discuss some problems associated with such analysis, and look at possible solutions.
{"title":"Deep learning in histopathology: A review","authors":"S. Banerji, S. Mitra","doi":"10.1002/widm.1439","DOIUrl":"https://doi.org/10.1002/widm.1439","url":null,"abstract":"Histopathology is diagnosis based on visual examination of tissue sections under a microscope. With the growing number of digitally scanned tissue slide images, computer‐based segmentation and classification of these images is a high‐demand area of research. Convolutional neural networks (CNNs) constitute the most popular classification architecture for a variety of image classification problems. However, applying CNNs to histology slides is not a trivial task and has several challenges, ranging from variations in the colors of slides to excessive high resolution and lack of proper labeling. In this advanced review, we introduce the application of CNN‐based architectures to digital histological image analysis, discuss some problems associated with such analysis, and look at possible solutions.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"127 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84912360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neelam Sharma, Naorem Leimarembi Devi, Satakshi Gupta, G. Raghava
Healthcare is the most important component in the life of all human beings as each individual wish to have happy, healthy, and wealthy life‐span. Most of the branches of science are dedicated to improve the healthcare. In the era of knowledge mining, informatics is playing a crucial role in different branches of research. Thus, a wide range of informatics‐based fields have emerged in the last three decades that include medical informatics, bioinformatics, cheminformatics, pharmacoinformatics, immunoinformatics, and clinical informatics. In the past, a number of reviews have been focused on the application of an informatics‐based field in the healthcare. In this review, an attempt is made to summarize the major computational resources developed in any informatics‐based field that have an application in healthcare. This review enlists computational resources in following groups ‐ drug discovery, toxicity prediction, vaccine designing, disease biomarkers, and Internet of Things. We mainly focused on freely available, functional resources like data repositories, prediction models, standalone software, mobile apps, and web services. In order to provide service to the community, we developed a health portal that maintain links related to healthcare http://webs.iiitd.edu.in/.
{"title":"Computational resources in healthcare","authors":"Neelam Sharma, Naorem Leimarembi Devi, Satakshi Gupta, G. Raghava","doi":"10.1002/widm.1437","DOIUrl":"https://doi.org/10.1002/widm.1437","url":null,"abstract":"Healthcare is the most important component in the life of all human beings as each individual wish to have happy, healthy, and wealthy life‐span. Most of the branches of science are dedicated to improve the healthcare. In the era of knowledge mining, informatics is playing a crucial role in different branches of research. Thus, a wide range of informatics‐based fields have emerged in the last three decades that include medical informatics, bioinformatics, cheminformatics, pharmacoinformatics, immunoinformatics, and clinical informatics. In the past, a number of reviews have been focused on the application of an informatics‐based field in the healthcare. In this review, an attempt is made to summarize the major computational resources developed in any informatics‐based field that have an application in healthcare. This review enlists computational resources in following groups ‐ drug discovery, toxicity prediction, vaccine designing, disease biomarkers, and Internet of Things. We mainly focused on freely available, functional resources like data repositories, prediction models, standalone software, mobile apps, and web services. In order to provide service to the community, we developed a health portal that maintain links related to healthcare http://webs.iiitd.edu.in/.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"19 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82040784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoyun Jia, Ruili Wang, James H. Liu, Chuntao Jiang
Discovery of behavioral patterns in online social commerce practice becomes important in this digital era. In this article, we propose a systematic approach to behavioral pattern discovery, and apply it in an emerging online social commerce venue: live streaming. We investigate behavioral patterns in gifting encouragement in live streaming to understand online social commerce practice. Our proposed approach is based on multiple triangulation, including data source triangulation (i.e., streamers, viewers, and actual behavior) and data collection method triangulation (i.e., interviews, focus groups, and observations). Through multiple triangulation, four behavioral patterns of gifting encouragement are discovered: (i) requesting a certain gift for providing a particular service, (ii) creating a raffle, (iii) eliciting competition between individuals, and (iv) eliciting competition between groups. This research reveals the special behavioral patterns in live streaming, and thus increases our knowledge of social commerce practices. This research provides a systematic approach to discover online behavioral patterns, and provides practical implications in live streaming platforms, especially in marketing and platform design.
{"title":"Discovery of behavioral patterns in online social commerce practice","authors":"Xiaoyun Jia, Ruili Wang, James H. Liu, Chuntao Jiang","doi":"10.1002/widm.1433","DOIUrl":"https://doi.org/10.1002/widm.1433","url":null,"abstract":"Discovery of behavioral patterns in online social commerce practice becomes important in this digital era. In this article, we propose a systematic approach to behavioral pattern discovery, and apply it in an emerging online social commerce venue: live streaming. We investigate behavioral patterns in gifting encouragement in live streaming to understand online social commerce practice. Our proposed approach is based on multiple triangulation, including data source triangulation (i.e., streamers, viewers, and actual behavior) and data collection method triangulation (i.e., interviews, focus groups, and observations). Through multiple triangulation, four behavioral patterns of gifting encouragement are discovered: (i) requesting a certain gift for providing a particular service, (ii) creating a raffle, (iii) eliciting competition between individuals, and (iv) eliciting competition between groups. This research reveals the special behavioral patterns in live streaming, and thus increases our knowledge of social commerce practices. This research provides a systematic approach to discover online behavioral patterns, and provides practical implications in live streaming platforms, especially in marketing and platform design.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"53 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75309252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bei Luo, Raymond Y. K. Lau, Chunping Li, Yain-Whar Si
Chatbots are intelligent conversational agents that can interact with users through natural languages. As chatbots can perform a variety of tasks, many companies have committed numerous resources to develop and deploy chatbots to enhance various business processes. However, we lack an up‐to‐date critical review that thoroughly examines both state‐of‐the‐art technologies and innovative applications of chatbots. In this review, we not only critically analyze the various computational approaches used to develop state‐of‐the‐art chatbots, but also thoroughly review the usability and applications of chatbots for various business sectors. We also identify gaps in chatbot‐related studies and propose new research directions to address the shortcomings of existing studies and applications. Our review advances both academic research and practical business applications of state‐of‐the‐art chatbots. We provide guidance for practitioners to fully realize the business value of chatbots and assist in making sensible decisions related to the development and deployment of chatbots in various business contexts. Researchers interested in the design and development of chatbots can also gain useful insights from our critical review and identify fruitful research topics and future research directions based on the research gaps discussed herein.
{"title":"A critical review of state‐of‐the‐art chatbot designs and applications","authors":"Bei Luo, Raymond Y. K. Lau, Chunping Li, Yain-Whar Si","doi":"10.1002/widm.1434","DOIUrl":"https://doi.org/10.1002/widm.1434","url":null,"abstract":"Chatbots are intelligent conversational agents that can interact with users through natural languages. As chatbots can perform a variety of tasks, many companies have committed numerous resources to develop and deploy chatbots to enhance various business processes. However, we lack an up‐to‐date critical review that thoroughly examines both state‐of‐the‐art technologies and innovative applications of chatbots. In this review, we not only critically analyze the various computational approaches used to develop state‐of‐the‐art chatbots, but also thoroughly review the usability and applications of chatbots for various business sectors. We also identify gaps in chatbot‐related studies and propose new research directions to address the shortcomings of existing studies and applications. Our review advances both academic research and practical business applications of state‐of‐the‐art chatbots. We provide guidance for practitioners to fully realize the business value of chatbots and assist in making sensible decisions related to the development and deployment of chatbots in various business contexts. Researchers interested in the design and development of chatbots can also gain useful insights from our critical review and identify fruitful research topics and future research directions based on the research gaps discussed herein.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90828654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.
{"title":"Themes in data mining, big data, and crime analytics","authors":"G. Oatley","doi":"10.1002/widm.1432","DOIUrl":"https://doi.org/10.1002/widm.1432","url":null,"abstract":"This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"431 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79585296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The need for accurate and unbiased assessment of residential real property has always been important not only to financial institutions lending on or holding such assets but also to municipalities that rely on property taxes as their critical source of revenue. The common methodology for predicting residential property sale price is based on traditional multiple regression in spite of known issues. Machine learning methods have been proposed as an alternative approach but the results are far from satisfactory. A review of existing studies and relevant issues can help researchers better assess the pros and cons of the approaches in this important stream of research and move the field forward. This article provides such a review. In our review, we have noticed that common to both the regression‐based methods and machine learning methods are the use of batch‐mode learning. Thus in addition to providing a review of recent research on batch‐based residential property prediction models, this article also explores a new approach to constructing residential property price prediction models by treating past sale records as an evolving data stream. The results of our study show that the data stream approach outperforms the traditional regression method and demonstrate the potential of data stream methods in improving prediction models for residential property prices.
{"title":"Predicting home sale prices: A review of existing methods and illustration of data stream methods for improved performance","authors":"Donghui Shi, J. Guan, J. Zurada, Alan Levitan","doi":"10.1002/widm.1435","DOIUrl":"https://doi.org/10.1002/widm.1435","url":null,"abstract":"The need for accurate and unbiased assessment of residential real property has always been important not only to financial institutions lending on or holding such assets but also to municipalities that rely on property taxes as their critical source of revenue. The common methodology for predicting residential property sale price is based on traditional multiple regression in spite of known issues. Machine learning methods have been proposed as an alternative approach but the results are far from satisfactory. A review of existing studies and relevant issues can help researchers better assess the pros and cons of the approaches in this important stream of research and move the field forward. This article provides such a review. In our review, we have noticed that common to both the regression‐based methods and machine learning methods are the use of batch‐mode learning. Thus in addition to providing a review of recent research on batch‐based residential property prediction models, this article also explores a new approach to constructing residential property price prediction models by treating past sale records as an evolving data stream. The results of our study show that the data stream approach outperforms the traditional regression method and demonstrate the potential of data stream methods in improving prediction models for residential property prices.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"21 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87352821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide meaningful opportunities to understand process in many domains along time, but also challenges. A main challenge is the heterogeneity of the temporal variables due to the different types of data, whether a measurement or an event, and type of samplings: fixed or irregular. Other variables can be also events that may or not have duration. In this review, we discuss the various types of temporal data, and the various relevant analysis methods. Starting with fixed frequency variables, with forecasting and time series methods, and proceeding with sequential data, and sequential patterns mining, and time intervals mining for events having various time duration. Also the use of various deep learning based architectures for temporal data is discussed. The challenge of heterogeneous multivariate temporal data analysis and discuss various options to deal with it, focusing on an increasingly used option of transforming the data into symbolic time intervals through temporal abstraction and the use of time intervals related patterns discovery for temporal knowledge discovery, clustering, classification prediction, and more. Finally, we discuss the overview of the field, and areas in which more studies and contributions are needed.
{"title":"Multivariate temporal data analysis ‐ a review","authors":"Robert Moskovitch","doi":"10.1002/widm.1430","DOIUrl":"https://doi.org/10.1002/widm.1430","url":null,"abstract":"The information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide meaningful opportunities to understand process in many domains along time, but also challenges. A main challenge is the heterogeneity of the temporal variables due to the different types of data, whether a measurement or an event, and type of samplings: fixed or irregular. Other variables can be also events that may or not have duration. In this review, we discuss the various types of temporal data, and the various relevant analysis methods. Starting with fixed frequency variables, with forecasting and time series methods, and proceeding with sequential data, and sequential patterns mining, and time intervals mining for events having various time duration. Also the use of various deep learning based architectures for temporal data is discussed. The challenge of heterogeneous multivariate temporal data analysis and discuss various options to deal with it, focusing on an increasingly used option of transforming the data into symbolic time intervals through temporal abstraction and the use of time intervals related patterns discovery for temporal knowledge discovery, clustering, classification prediction, and more. Finally, we discuss the overview of the field, and areas in which more studies and contributions are needed.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"5 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80586892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Silva, Maria Eduarda Silva, P. Ribeiro, Fernando M. A. Silva
There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
{"title":"Time series analysis via network science: Concepts and algorithms","authors":"V. Silva, Maria Eduarda Silva, P. Ribeiro, Fernando M. A. Silva","doi":"10.1002/widm.1404","DOIUrl":"https://doi.org/10.1002/widm.1404","url":null,"abstract":"There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"36 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87801125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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