This book examines the latest developments in video analytics for business intelligence applications, assessing the state of the art and serving as a resource for potential applications and successful practice. Benefits academic and commercial practitioners.

Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed pervasively in public spaces including retail centres and shopping malls. Intelligent video analytics aims to automatically analyze content of massive amount of public space video data and has been one of the most active areas of computer vision research in the last two decades. Current focus of video analytics research has been largely on detecting alarm events and abnormal behaviours for public safety and security applications. However, increasingly CCTV installations have also been exploited for gathering and analyzing business intelligence information, in order to enhance marketing and operational efficiency. For example, in retail environments, surveillance cameras can be utilised to collect statistical information about shopping behaviour and preference for marketing (e.g., how many people entered a shop; how many females/males or which age groups of people showed interests to a particular product; how long did they stay in the shop; and what are the frequent paths), and to measure operational efficiency for improving customer experience. Video analytics has the enormous potential for non-security oriented commercial applications. This book presents the latest developments on video analytics for business intelligence applications. It provides both academic and commercial practitioners an understanding of the state-of-the-art and a resource for potential applications and successful practice.

Aligning business intelligence (BI) infrastructure with strategy processes not only improves your organization's ability to respond to change, but also adds significant value to your BI infrastructure and development investments. Until now, there has been a need for a comprehensive book on business analysis for BI that starts with a macro view and

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

“This book is a splendid and valuable addition to this subject. The whole book is well written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining.” Dr. Edi Shivaji, Des Moines, Iowa “As a complete novice to this area just starting out on a MBA course I found the book incredibly useful and very easy to follow and understand. The concepts are clearly explained and make it an easy task to gain an understanding of the subject matter.” -- Mr. Craig Domoney, South Africa. Business Intelligence and Data Mining is a conversational and informative book in the exploding area of Business Analytics. Using this book, one can easily gain the intuition about the area, along with a solid toolset of major data mining techniques and platforms. This book can thus be gainfully used as a textbook for a college course. It is also short and accessible enough for a busy executive to become a quasi-expert in this area in a couple of hours. Every chapter begins with a case-let from the real world, and ends with a case study that runs across the chapters.

Rapid technology change is impacting organizations large and small. Mobile and Cloud computing, the Internet of Things (IoT), and “Big Data” are driving forces in organizational digital transformation. Decision support and analytics are available to many people in a business or organization. Business professionals need to learn about and understand computerized decision support for organizations to succeed. This text is targeted to busy managers and students who need to grasp the basics of computerized decision support, including: What is analytics? What is a decision support system? What is “Big Data”? What are “Big Data” business use cases? Overall, it addresses 61 fundamental questions. In a short period of time, readers can “get up to speed” on decision support, analytics, and business intelligence. The book then provides a quick reference to important recurring questions.

This book examines the managerial dimensions of business intelligence (BI) systems. It develops a set of guidelines for value creation by implementing business intelligence systems and technologies. In particular the book looks at BI as a process – driven by a mix of human and technological capabilities – to serve complex information needs in building insights and providing aid in decision making. After an introduction to the key concepts of BI and neighboring areas of information processing, the book looks at the complexity and multidimensionality of BI. It tackles both data integration and information integration issues. Bodies of knowledge and other widely accepted collections of experience are presented and turned into lessons learned. Following a straightforward introduction to the processes and technologies of BI the book embarks on BI maturity and agility, the components, drivers and inhibitors of BI culture and soft BI factors like attention, sense and trust. Eventually the book attempts to provide a holistic view on business intelligence, possible structures and tradeoffs and embarks to provide an outlook on possible developments in BI and analytics.