Synopses & Reviews
Location-Aware Applications is a comprehensive guide to the technology and business of creating compelling location-based services and applications. The book walks you through the LBS landscape, from mapping technologies to available platforms; from toolkits to business questions like monetization and privacy. About the Book
Mobile customers want entertainment, business apps, and on-the-go services that recognize and respond to location. This book will guide you through the technology and business of mobile applications so you can create competitive and innovative apps based on location-based services. It is an engaging look at the LBS landscape, from choosing the right mobile platform, to making money with your application, to dealing with privacy issues. It provides insight into a wealth of ideas for LBS development so you can build the next killer app.
Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside
Who Should Read this Book
- Managing location-aware content
- Making money from location-based services
- Augmented reality and tablets
- Detailed examples for iPhone and Android
This book is written for developers and business pros - no prior knowledge of location-based services is assumed.
Table of Contents
PART 1 LBS, THE BIG PICTURE
- Location-based services: An overview
- Positioning technologies
- Content options
PART 2 TECHNOLOGY
- Consumer applications
- Mobile platforms
- Connectivity issues
- Server-side integration
PART 3 CREATING WINNING LBS BUSINESSES
- Monetization of location-based services
- The privacy debate
- Distributing your application
- Securing your business idea
As mobile device usage continues to expand at a rapid pace, applications that effectively utilize a userâ€™s location become increasingly valuable to businesses operating in the mobile world.
Location-Aware Applications aims to provide readers with a thorough background to the new era of location-aware applications that are set to change the mobile landscape and shape the very core of mobile services for this decade. It is designed to specifically address the growing segment of developer-entrepreneurs who wish to gain insights into specific aspects of establishing a viable LBS business.
Web 2.0 applications provide a rich user experience, but the parts you can't see are just as important-and impressive. They use powerful techniques to process information intelligently and offer features based on patterns and relationships in data. Algorithms of the Intelligent Web shows readers how to use the same techniques employed by household names like Google Ad Sense, Netflix, and Amazon to transform raw data into actionable information.
Algorithms of the Intelligent Web is an example-driven blueprint for creating applications that collect, analyze, and act on the massive quantities of data users leave in their wake as they use the web. Readers learn to build Netflix-style recommendation engines, and how to apply the same techniques to social-networking sites. See how click-trace analysis can result in smarter ad rotations. All the examples are designed both to be reused and to illustrate a general technique- an algorithm-that applies to a broad range of scenarios.
As they work through the book's many examples, readers learn about recommendation systems, search and ranking, automatic grouping of similar objects, classification of objects, forecasting models, and autonomous agents. They also become familiar with a large number of open-source libraries and SDKs, and freely available APIs from the hottest sites on the internet, such as Facebook, Google, eBay, and Yahoo.
'\'\\\"Algorithms of the Intelligent Web\\\" is an example-driven blueprint for creating applications that collect, analyze, and act on the massive quantities of data users leave in their wake as they use the Web. Readers learn to build Netflix-style recommendation engines, and how to apply the same techniques to social-networking sites.\\n
About the Author
'Dr. Haralambos (Babis) Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions, and also a world expert in supply management. He has about twenty years of experience in developing professional software. Currently, he is the director of R&D and chief architect, for expense management solutions, at Emptoris, Inc. Babis holds a Ph.D. in applied mathematics from Brown University, an M.S. degree in theoretical and applied mechanics from the University of Illinois at Urbana-Champaign, and B.S. and M.S. degrees in civil engineering from the Aristotle University of Thessaloniki in Greece. He was the recipient of the Sigma Xi award for innovative research in 2000, and he is the author of numerous publications in peer-reviewed international scientific journals, conferences, and technical periodicals.Dmitry Babenko is the lead for the data warehouse infrastructure at Emptoris, Inc. He is a software engineer and architect with 13 years of experience in the IT industry. He has designed and built a wide variety of applications and infrastructure frameworks for banking, insurance, supply-chain management, and business intelligence companies. He received a M.S. degree in computer science from Belarussian State University of Informatics and Radioelectronics.'