Synopses & Reviews
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.
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.
Whether you're canvassing a congressional district, managing a sales region, mapping city bus schedules, or analyzing local cancer rates, thinking spatially opens up limitless possibilities for database users. PostGIS, a freely available open-source spatial database extender, can help you answer questions that you could not answer using a mere relational database. Its feature set equals or surpasses proprietary alternatives, allowing you to create location-aware queries and features with just a few lines of SQL code.
PostGIS in Action is the first book devoted entirely to PostGIS. It will help both new and experienced users write spatial queries to solve real-world problems. For those with experience in more traditional relational databases, this book provides a background in vector-based GIS so you can quickly move to analyzing, viewing, and mapping data. Advanced users will learn how to optimize queries for maximum speed, simplify geometries for greater efficiency, and create custom functions suited specifically to their applications. It also discusses the new features available in PostgreSQL 8.4 and provides tutorials on using additional open source GIS tools in conjunction with PostGIS.
When computers harness prior experience to improve future performance, a type of artificial intelligence called machine learning has been applied. The Apache Mahout project is focused on three types of machine learning that are of particular interest to modern web developers "recommendation systems, classification, and clustering.
Through real-world examples, Mahout in Action introduces the sorts of problems that these techniques are appropriate for, and then illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability, and how to apply these techniques at very large scale with the Apache Hadoop framework.
As mobile device usage continues to expand at a rapid pace, applications that effectively utilize a users location become increasingly valuable to businesses operating in the mobile world.
Building 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.
"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.
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.