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Synopsis
This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.
- Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion;
- Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;
- Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Deepak P is currently a Lecturer (Assistant Professor) in Computer Science at Queen's University Belfast. His research interests lie across various sub-fields of data analytics such as natural language processing, information retrieval, data mining, machine learning and databases. He has authored more than 50 research papers in top avenues in data analytics, and has ten granted patents from USPTO. Prior to joining Queen's University in 2015, he was a researcher at IBM Research India for many years. He is a Senior Member of the IEEE and the ACM, and is a recipient of the Indian National Academy of Engineering Young Engineer Award.
Anna Jurek-Loughrey is currently a Lecturer (Assistant Professor) in Computer Science at Queen's University Belfast. Her work has spanned a diverse set of topics in the area of data analytics comprising supervised and unsupervised machine learning, record linkage, sensor-based activity recognition within smart environments, social media analytics with application to health and security. Before joining Queen's in 2015 she worked as a data scientist at Repknight Ltd for two years.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurSpringer Nature Switzerland AG
- Date d'édition2019
- ISBN 10 3030018717
- ISBN 13 9783030018719
- ReliureRelié
- Langueanglais
- Nombre de pages343
- ÉditeurP Deepak, Jurek-Loughrey Anna
- Coordonnées du fabricantnon disponible
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Linking and Mining Heterogeneous and Multi-view Data.
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VIII, 343 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Unsupervised and Semi-Supervised Learning. Sprache: Englisch. N° de réf. du vendeur 4506CB
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Linking and Mining Heterogeneous and Multi-view Data
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, sma. N° de réf. du vendeur 256051569
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Linking and Mining Heterogeneous and Multi-view Data (Unsupervised and Semi-Supervised Learning)
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Linking and Mining Heterogeneous and Multi-view Data
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Buch. Etat : Neu. Neuware -This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion;Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch. N° de réf. du vendeur 9783030018719
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Linking and Mining Heterogeneous and Multi-view Data
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ISBN 13 : 9783030018719
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion; Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field. N° de réf. du vendeur 9783030018719
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Linking and Mining Heterogeneous and Multi-view Data
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ISBN 13 : 9783030018719
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion; Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field. 352 pp. Englisch. N° de réf. du vendeur 9783030018719
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