Zustand: Sehr gut | Sprache: Englisch | Produktart: B�cher | This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display.

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Detalles bibliogr�ficos

T�tulo

Social Big Data Analytics : Practices, Techniques, and Applications

Editor

Springer Nature Singapore

Fecha de publicaci�n

2022

Idioma

Ingl�s

ISBN 10

9813366540

ISBN 13

9789813366541

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

Sehr gut

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B�cher

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Sinopsis

This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process.

This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities.

The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning.

This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display.

Acerca del autor

Dr Bilal Abu Salih holds a Ph.D. in Information Systems with a focus on Social Big Data Analytics from Curtin University since 2018. He is an Assistant Professor in the Computer Science Department in King Abdullah II School of Information Technology, The University of Jordan, Jordan. Bilal also a University Associate, Curtin University, Australia. He worked with cross-disciplinary funded research projects which are related to data analytics, machine learning, data mining of social media, and big data analysis.

Dr Pornpit Wongthongtham aka Dr Ponnie Clark is the recipient of a PhD in Information Systems, Master of Science in Computer Science, and Bachelor of Science in Mathematics. Her research areas of expertise include Ontology and Linked Data, Text Mining, Social Data Analytics, Trustworthiness, Artificial Intelligence, and the like. She has over two decades of academic experience including in research and development involving multi-disciplinary areas. She is currently a senior research fellow at School of Physics, Maths and Computing, the University of Western Australia.

Dr Dengya (Simon) Zhu is an adjunct research fellow at the School of Management, Curtin Business School. He has a broad range of experience in government, industry, and academic research. Dr Zhu’s research interests include big data, data mining, machine learning, natural language processing, information retrieval, sentiment analysis, open-source software, and software development. His research projects are usually practically oriented to address real-world issues.

Kit Yan Chan received his Ph.D. degree in Computing in 2006 from London South Bank University, United Kingdom, and is currently a Senior Lecturer at Curtin University, Australia. Dr Kit Yan Chan has worked as a Full-Time Researcher at Hong Kong Polytechnic University (2004–2009) and Curtin University (2009–2013). Dr Kit Yan Chan is an Associate Editor for IJFS, and the editorial board of the Journal of Engineering Design. His research interests are mainly in artificial intelligence and new product development.

Dr. Amit Rudra is a lecturer in the School of Management at Curtin University, Australia. He holds a BSc (Hons) in Mathematics and an MSc in Operational Research from the University of Delhi; a Post MSc Diploma in Computer Science from IIT, Delhi and a PhD in Computer Science from Curtin University, Australia. Dr. Amit Rudra has extensive experience in the areas of database, data warehousing, data mining, and ERP systems. Currently, he coordinates the Big Data Working group in the School of Management at Curtin Business School.

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