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<div>Chapter 1.&nbsp;Big data analytics of social network data: Who cares most about you on Facebook?.- Chapter 2.&nbsp;Characterizing and Predicting Yelp Users' Behavior Composite Visualization Features in.- Chapter 3.&nbsp;PEVNET: A Framework for Visualization of Criminal Networks.- Chapter 4. Effective Customer Relationship Managment at ATB Financial A Case Study on Industry-Academia Collaboration in Data Analytics.- Chapter 5. Effective Personnel Selection and Team Building using Intelligent Data Analytics.- Chapter 6. Extracting Relevant Information from Big Data to Anticipate Forced Migration.- Chapter 7. Implicit Social Networks for Social Recommendation of Scholarly Papers.- Chapter 8. The Effect of Corpora Size on Performance of Named Entity Recognition.- Chapter 9.&nbsp;Time Series Analysis for the most Frequently Mentioned Biomarkers in Breast Cancer Articles.- Chapter 10.&nbsp;Transportation Engineering on Social Question and Answer Websites: An Empirical Case Study.- Chapter 11. Understanding Health Service Delivery Using Spatio-Temporal Patient Mobility Data.- Chapter 12. WaterVis: GeoVisual Analytics for Exploring Hydrological Data.</div><p></p><p></p><p></p><p></p><p></p><p></p><p></p><p></p>

<p>Dr. Mohammad Moshirpour is an instructor of Software Engineering at the Schulich School of Engineering, University of Calgary. His research interests are the areas of software architecture, software requirements engineering, design and analysis of multi-agent systems, intelligent data analytics, data mining and machine learning, and software engineering and computer science education. He is the IEEE Chair of the Computer Chapter of the Southern Alberta Section.</p>

Dr. Behrouz Far’s is a Professor of Software Engineering at the Schulich School of Engineering, University of Calgary, since 2001. His research interest areas are engineering of intelligent, distributed and heterogeneous networked systems, implementation, testing and verification of agent-based software systems. He has established Intelligent Software Systems (ISS) and Multi-Object Localization and Multi-Modal Large Data Fusion Laboratory (MOL-MDFL) Laboratories of the Schulich School of Engineering, and is co-director of the Intelligent Transportation Lab, University of Calgary.<p></p>

<p>Reda Alhajj is a professor of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals, conferences and edited books. He is founding editor in chief of Springer premier SNAM and NetMAHIB journals, founding co-editor-in-chief of Springer LNSN Series, founding co-editor-in-chief of ESNAM, founding steering chair of the flagship conference “IEEE/ACM ASONAM”, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. His primary research interests focus on various aspects of data science and big data He received best graduate supervision award and community service award at the University of Calgary.&nbsp;</p><p></p>

This book addresses the impacts of various types of services such as infrastructure, platforms, software, and business processes that cloud computing and Big Data have introduced into business. Featuring chapters which discuss effective and efficient approaches in dealing with the inherent complexity and increasing demands in data science, a variety of application domains are covered. Various case studies by data management and analysis experts are presented in these chapters. Covered applications include banking, social networks, bioinformatics, healthcare, transportation and criminology. &nbsp;<i>Highlighting the Importance of Big Data Management and Analysis for Various Applications</i>&nbsp; will provide the reader with an understanding of how data management and analysis are adapted to these applications. This book will appeal to researchers and professionals in the field.&nbsp;<br>

Features state-of- the-art techniques for big data management and analysis Contains case studies from banking, social networks, bioinformatics, healthcare, transportation and criminology Includes practical test results from synthetic and real data