Linking and Mining Heterogeneous and Multi-view Data

Anna Jurek-Loughrey

ISBN 10: 3030018717 / ISBN 13: 9783030018719

Published by Springer International Publishing, 2019

About this Item

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.

Seller Inventory # 9783030018719

Bibliographic Details

• Title: Linking and Mining Heterogeneous and Multi-view Data
• Publisher: Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3030018717
• ISBN 13: 9783030018719
• Binding: Buch
• Condition: Neu

About this title

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. 

About the Author

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.