RFID Essentials (Theory in Practice (O'Reilly)) Review

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(More customer reviews)Wanting to get smarter about Radio Frequency Identification (RFID), I welcomed the opportunity to read some new titles on the topic. I started reading the first of these, RFID Essentials by Bill Glover and Himanshu Bhatt (2006, O'Reilly, 276 Pages, ISBN 0596009445), not knowing what to expect. What I walked away with was not only a high level understanding of the technical aspects of RFID, but also an excellent discussion of the compliance, governance, privacy and security issues that surround its expanded growth and use. If there is a title that truly matches its content, this would be it.

The authors write that they undertook this book because there was no title like it on the market: a book that could target readers in between senior management and electrical engineers. As the child of an old-school software engineer with minimal knowledge on the topic, I was eager to accept this as their goal.
The book begins with an introduction to RFID. In doing this, they break down the use of the technology into distinct eras, with the compliance era being the current time frame. Tracking back to the post-war 1940's, they walk through an overview of how RFID came to be with the birth of transistors. Fast-forwarding to the compliance era, driven by vendors such as Wal-Mart, they seek to explain how most RFID-based activities meet up with traditional compliance projects, with the emphasis being on meeting requirements with the lowest total cost of ownership (TCO). They then look at the "could be" as RFID-enabled enterprises come on line. They look at the various RFID application types, considerations for each of these types, and implementation of these types. They conclude this chapter wit an outline of the challenges, as well as some RFID adoption guidelines.
Chapter 2 of the book covers an overview of a "RFID Architecture". In this chapter, the authors walk through sequential items that need to be considered when looking at the implementation of an architecture. If there is an important lesson to be taken from this chapter, it would be the need to focus on business requirements.
Chapter 3 focuses on RFID Tags. They walk through basic tag capabilities, physical characteristics of tags, power sources, the "air interface", and more. They key in on the how and when to use various types of tags. A key understanding of this chapter is to look beyond the hype and at the realities. In chapter 4, they cover tag protocols. This discussion begins with a discussion of RFID Protocol terms and concepts. They then discuss how tags store data, as well as tag features that address security and privacy.
Starting in Chapter 5, the authors begin their discussion of readers and printers. This chapter includes important discussions of the types of readers that are available, and how to determine which ones make the best sense for a RFID implementation. Chapter 6 extends this discussion to include reader protocols.
From a business perspective, Chapter 7 offers insight into the important topic of data integration through middleware. A bit more technical in depth, this chapter covers issues associated with polling and managing the data provided by tags. Aside from the "commercial" for their employer (Sun), they do a good job covering high level discussions of middleware considerations, laced with technical content for systems architects to start their thinking. This line of thought continues in Chapter 8 in their coverage of the RFID Information Service.
Chapter 9 gets into the sensitive topic of manageability. Because RFID lives on the edge and the architecture has the potential to be massive in size, there are a number of areas that need to be planned for, including automation, The authors cover this with discussions of required capabilities, as well a standards and technologies.
Chapter 10 gets into a topic near and dear to my heart: privacy and security. The authors, while discussing the fact that public reaction to RFID is based on a great deal of speculation and misinformation, acknowledge that public perception will win, Without managing that perception with the realities of controls, the enterprise implementing RFID introduces additional risk into the environment. Unlike the authors of another RFID title I will be reviewing this week, they take a low-key, non-reactionary approach to this issue. This allows for the reader to think about the issues without being broad-sided by fear,uncertainty and doubt.
The book wraps up with a discussion of RFID futures in Chapter 11.
Who Should Read This Book?
This book should be read by anybody who needs to get up to speed quickly on RFID technology and issues. This includes business managers and systems architects. It is also an excellent resource for information technology auditors who need to gain in understanding of the technology (in fact, it can serve as the basis for developing the skeleton of a RFID audit plan, fleshed out with more details later.
The book is not designed for high-end tech heads or people who want to look at specific ERP-type applications. It was not written for this audience.
Scorecard
Eagle on a long par 5

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Radio Frequency Identification (RFID) or Near Field Communication (NFC) are rapidly changing the way businesses track inventory and assets. From Wal-Mart and Tesco to the U.S. Department of Defense, early efforts are already showing benefits, but software, integration, and data processing for RFID still present a challenge. If you are a developer or an architect charged with developing an RFID system, this book is for you. Drawing on extensive experience, Bill Glover and Himanshu Bhatt provide you with essential information on this emerging technology. With the knowledge you gain in these pages, you will possess the information and understanding you need to start designing, building, or integrating with RFID systems. In RFID Essentials you will find information on:
Tags and tag protocols, including the Electronic Product Code (EPC)
Readers and reader protocols
RFID middleware
Security and privacy
Managing RFID devices
RFID's impact on your architecture
"The Information Age is over. We're entering an era where network connectivity is almost ubiquitous - it's participate or perish." --Jonathan Schwartz, President and COO, Sun Microsystems, Inc."Unique competitive advantage erupts from enterprises that couple the RFID technologies laid out in RFID Essentials with modern business integration using service-oriented architectures. This is the book to read in order to understand this new landscape." --Mark Bauhaus, Senior Vice President, Sun Microsystems, Inc."This is a must read for RFID Software and Solution architects and is highly recommended for anyone needing to gain more insight into the myriad of components, standards and technologies that make up an RFID solutions environment." --Bryan Tracey, Chief Architect, GlobeRanger Corporation"The authors have done a commendable job of covering a lot of ground in the RFID space, including the infrastructure needed to share the volumes of data RFID will likely generate." --Graham Gillen, Senior Product Manager, VeriSign

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Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions Review

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(More customer reviews)This book is for professionals that must analyze data in their daily work. First off, if you are unfamiliar with the approach of the "Head First" series of books by O'Reilly, the approach was and is revolutionary in the field of technical writing. The authors of this series know that page after page of terse text will not easily penetrate the brain of the working professional who needs help rather quickly. Traditional textbook models work best on students in a traditional classroom setting who can slowly absorb material over a period of several months with the help of bi-weekly classroom sessions with a professor. The working professional does not have this luxury of time or of personal tutoring.
Thus the authors both penetrate your brain and hold your interest by serving information up in unusual ways - odd pictures and illustrations, Q&A sessions, repeating the same material in different ways, and interesting case studies in which you are asked at every step to give your input. They'll even lead you down the the wrong path every now and then so that you remember the right one all the better.
As for the subject matter, this is not a book on statistics and how to solve problems in statistics. Instead, it is how you use various statistical models and tools and visualization to analyze often confusing corporate data and come up with recommendations based on that data. Some mathematical methods will be presented as they are necessary to solving the underlying problems - optimization, hypothesis testing, bayesian statistics, subjective probabilities, heuristics, and histograms - these are all mentioned and even have their own chapters. However, this book is also about tools - R and the analysis tools of Excel specifically. In the appendix, this book even shows you how to install R.
However, I don't believe that you could get away with knowing nothing of statistics and really get the most out of this book. If you do happen to have the luxury of a little time I suggest the following. Read the excellent Head First Statistics as a tutorial, and then use the problems in Schaum's Outline of Statistics (Schaum's Outline Series) to test your knowledge. Then you should be more than ready for this book.
The author has a chapter entitled "leftovers" that tells you what this book does not cover. I include that here so that you don't waste your time if this is what you are looking for:
1 Everything else in statistics
2 Excel skills - (book assumes previous experience)
3 Edward Tufte and his principles of visualization
4 PivotTables
5 Nonlinear and multiple regression
7 Null-alternative hypothesis testing
8 Randomness
9 Google Docs
I highly recommend this book for the right audience with the right experience level.

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Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:



Determine which data sources to use for collecting information
Assess data quality and distinguish signal from noise
Build basic data models to illuminate patterns, and assimilate new information into the models
Cope with ambiguous information
Design experiments to test hypotheses and draw conclusions
Use segmentation to organize your data within discrete market groups
Visualize data distributions to reveal new relationships and persuade others
Predict the future with sampling and probability models
Clean your data to make it useful
Communicate the results of your analysis to your audience

Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

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R in a Nutshell: A Desktop Quick Reference Review

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(More customer reviews)'R in a Nutshell' is the essential introductory book on R. Do not try to learn R without it.
I made two attempts to learn R before purchasing this book. In both previous attempts, I had to abort and use another tool to solve my problem because it was taking me too long to accomplish very simple things in R.
The reason R is hard to learn is that its documentation is organized for statisticians that already know R, but have forgotten a detail or two. There are a few other books on learning R, but they are setup like a college course - complete the entire book and THEN you can actually accomplish something.
R in a Nutshell allows you to get working immediately. Simply lookup what you need to do. The firsts thing I did was load a file and make a histogram. I found that stuff in the section on "Loading Data" and the section on charts. In no time I was making stacked area charts for cohorts. Now R is an essential tool for me - and I haven't even taken the time to learn it well! With this book, I don't have to. I can learn as I go. So I actually use R.
Do not R without it.

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What people are saying about R in a Nutshell

"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians." --Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University

"R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks." --David M. Smith, Editor of the "Revolutions" blog at REvolution Computing

Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.

Understand the basics of the language, including the nature of R objects
Learn how to write R functions and build your own packages
Work with data through visualization, statistical analysis, and other methods
Explore the wealth of packages contributed by the R community
Become familiar with the lattice graphics package for high-level data visualization
Learn about bioinformatics packages provided by Bioconductor

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Handbook of Mathematical Models in Computer Vision Review

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(More customer reviews)When attending a general computer vision conference like xCCV, did you ever feel lost at certain sessions? Well, don't always blame the presenters! The field covered by Computer Vision has become so broad that it is almost impossible to understand what is going on and to keep track of the latest developments. To (partially) overcome this problem, the editors of the Handbook of Mathematical Models in Computer Vision have done a great job.
One can become a bit skeptical reading such a title. How complete can such a handbook be? However, going through the 33 chapters, indeed a wide breadth is treated. The focus of the book is on mathematical methods that both model and reproduce human visual abilities. This is the field of biological vision in which the editors have a strong background.
The editors chose three distinct categories of mathematical models, namely variational techniques (those attending Prof. Faugeras' talk at ICPR 2006 may remember his statement that they give the fundamental equations in computer vision!), statistical methods, and combinatorial approaches. The chapters are grouped in six sections that circle around these three categories. Although going through the book chapters by mentioning keyword may yield a rather boring list, it shows the wide variety of topics that are being dealt with.
The book starts with a section on low-level vision: Image Reconstruction. Here one can find information on diffusion filters and wavelets, total variation methods, and PDE based inpainting.
The second section is concerned with Boundary Extraction, Segmentation and Grouping. Here subjects like levelings, graph cuts, minimal paths and fast marching methods, deformable models, variational segmentation with shape priors, curve propagation, level set methods, and a stochastic model of geometric snakes are discussed.
Section three switches to high level vision. It deals with Shape Modeling & Registration, divided into topics concerning invariant processing and occlusion resistant recognition, image-based inferences, point matching and uncertainty-driven, point-based image registration.
In the fourth section, Motion Analysis, Optical Flow & Tracking, the concept of time is added and one encounters the topics of optical flow estimation, image warping, alignment and stitching, visual tracking, image and video segmentation, human motion capture, and dynamic textures.
Section five deals with 3D from Images, Projective Geometry & Stereo Reconstruction, treated by boundary detection, stereo, texture and color, shape from shading, calibration, motion and shape recovery, multi-view reconstruction, binocular stereo with occlusions, and modeling non-rigid dynamic scenes.
The last section may seem a bit odd: Applications: Medical Image Analysis. However, this is one of the most prominent areas in computer vision. Although here certain vision aspects do not occur, compared to natural images (just think of the influence of the sun), for many tasks the performance of the mathematical methods can be evaluated since a ground truth is often available - provided by humans whom the models are supposed to mimic. In this section, applications of interactive graph-based segmentation methods, 3D active shape and appearance models, characterization of diffusion anisotropy, segmentation, variational approaches, and statistical methods of registration are given.
The danger of publishing an edited volume is the difference in style and treatment of the topics among the various contributions. This is not the case here. Each chapter gives a general introduction to the topic, introduces the mathematical model, discusses the underlying ideas globally, and shows some results. For the full details the readers are referred to the extensive bibliography with 929 entries.
This book is a must-have for those interested in the full breadth of research done in the biological & computer vision community. As a bonus, the chapters can also be used in a seminar-based, advanced undergraduate course in mathematical based computer vision.


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This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the theoretical efforts of mathematicians and engineers. This marriage of mathematics and computer vision - computational vision - has resulted in a discrete approach to image processing that is more reliable when leveraging in practical tasks. This comprehensive volume provides a detailed discourse on the mathematical models used in computational vision from leading educators and active research experts in this field. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis.Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.

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Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Review

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(More customer reviews)A survey of remarkable breadth, listing hundreds of sources: 50-100 items per chapter, majority of them from conference proceedings. Evidently, this kind of volume does not let the author discuss any particular paper or topic in detail; at best, a reference is covered with a short paragraph. This does create a problem: the book's value is exhausted once the reader looks up the topic of interest, and moves on to the suggested references. "Temporal data mining" could do with a little more editing, but I am quite impressed with it the way it is, an authoritative and wide-ranging introduction to an interesting topic.

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Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today.From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining.Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa.wordpress.com/

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21 Recipes for Mining Twitter Review

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(More customer reviews)This is a very short, very practical way to get you started exploring the Twitter APIs on your own. It offers a decent amount of code no experienced developer should have much trouble understanding and applying to his or her own needs. The cost per page is not exactly attractive and some readers may want a more in-depth less cookbook-like experience. If you are in a hurry to extract data from Twitter, this book may be for you - for less than $20 for the electronic edition, it will spare you more than that in time spent figuring out libraries and APIs. Plus, it offers some intro on many other interesting libraries that can be applied to a lot of problems besides Twitter mining.

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Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to:

Use OAuth to access Twitter data
Create and analyze graphs of retweet relationships
Use the streaming API to harvest tweets in realtime
Harvest and analyze friends and followers
Discover friendship cliques
Summarize webpages from short URLs

This book is a perfect companion to O'Reilly's Mining the Social Web.

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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Review

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(More customer reviews)Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.

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A general framework for constructing and using probabilistic models ofcomplex systems that would enable a computer to use available information for makingdecisions.

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Healthcare Informatics: Improving Efficiency and Productivity Review

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(More customer reviews)With a real need for "healthcare informatics", this is an excellent book and a must read...
"As a 20+ year healthcare IT professional working specifically on electronic health record (EHR) applications as well as the ongoing analysis of this data, I have come to realize the immense problems the healthcare industry is facing today because of the ongoing proliferation of data. With the seemingly endless stream of patient, doctor, healthcare provider, insurance company, and drug manufacture data, the industry has created a real void as well as an opportunity to tap into the huge data store in an attempt to bring real positive change regarding needed efficiencies to the industry. I have found that being able to frame this real life problem has been a challenge until reading Dr. Stephen Kudyba's excellent text "Healthcare Informatics: Improving Efficiency and Productivity".
I was amazed at how quickly and precisely Dr. Kudyba's text not only framed the problem but discussed possible solutions to tackling the problem as well as provided real life examples of applications and processes in use today which are addressing the problem head-on. He does a great job of going beyond theory to actual practice by identifying informatics applications that have been incorporated by healthcare organizations. Two examples in particular from the book which resonated with me was the example of an informatics project that turned a healthcare system's paper-based resources into digital assets as well as the example of the E-commerce self-service patient check-in application which was implemented at a New Jersey hospital. Honestly its one thing to discuss problems and propose possible solutions, but Dr Kudyba's book goes beyond that by identifying "real" solutions in use today.
Lastly, I found that this text serves as a groundbreaking, foundational textbook for a potential graduate course in healthcare data informatics. We cannot ignore the fact that informatics is at the heart of correcting many of the "ills" in healthcare, and because of this, Dr Kudyba's book is a must read!


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Healthcare Informatics: Improving Efficiency and Productivity examines the complexities involved in managing resources in our healthcare system and explains how management theory and informatics applications can increase efficiencies in various functional areas of healthcare services. Delving into data and project management and advanced analytics, this book details and provides supporting evidence for the strategic concepts that are critical to achieving successful healthcare information technology (HIT), information management, and electronic health record (EHR) applications. This includes the vital importance of involving nursing staff in rollouts, engaging physicians early in any process, and developing a more receptive organizational culture to digital information and systems adoption.We owe it to ourselves and future generations to do all we can to make our healthcare systems work smarter, be more effective, and reach more people. The power to know is at our fingertips; we need only embrace it.-From the foreword by James H. Goodnight, PhD, CEO, SASBridging the gap from theory to practice, it discusses actual informatics applications that have been incorporated by various healthcare organizations and the corresponding management strategies that led to their successful employment. Offering a wealth of detail, it details several working projects, including:A computer physician order entry (CPOE) system project at a North Carolina hospitalE-commerce self-service patient check-in at a New Jersey hospitalThe informatics project that turned a healthcare system's paper-based resources into digital assetsProjects at one hospital that helped reduce excesses in length of stay, improved patient safety; and improved efficiency with an ADE alert systemA healthcare system's use of algorithms to identify patients at risk for hepatitis Offering the guidance that healthcare specialists need to make use of various informatics platforms, this book provides the motivation and the proven methods that can be adapted and applied to any number of staff, patient, or regulatory concerns.

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Data Analysis and Decision Making (with Online Content Printed Access Card) Review

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(More customer reviews)On the positive side, this book has many excellent case studies and examples. It is well written and interesting. However, I was disappointed, as I was expecting use of Excel to rigorously solve decision making and data analysis problems. The focus of the book is mostly traditional statistics solved using a group of commercial add-ins for Excel. If this is what you want, then the book would get five stars. However, for data analysis and decision making, I think a more thorough treatment using Excel without relying so much on the add-ins would have been appropriate.

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DATA ANALYSIS AND DECISION MAKING is a teach-by-example approach, learner-friendly writing style, and complete Excel integration focusing on data analysis, modeling, and spreadsheet use in statistics and management science. The Premium Online Content Website (accessed by a unique code with every new book) includes links to the following add-ins: the Palisade Decision Tools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); and SolverTable, allowing users to do sensitivity analysis. All of the add-ins is revised for Excel 2007 and notes about Excel 2010 are added where applicable.
About This Edition
New Features
Chapters 2 and 3 are completely rewritten and reorganized, focusing on the description of one variable at a time, and relationships between variables. Both chapters have more coverage of categorical variables, as well as new and more interesting data sets in the examples. Many of the problems in previous editions were deleted or updated, and a number of brand new problems were added for relevance to current statistical analysis. A problem guide is available to instructors showing the context of each of the "data" problems, and it also shows the correspondence between problems in this edition and problems in the previous edition.
The previous edition's Chapter 4 is renamed Chapter 17, "Importing Data into Excel," and is completely rewritten with its section on Excel tables located in Chapter 2. (Previous edition chapters 5-17 were renumbered 4-16.)
The book is still based on Excel 2007, but notes about changes in Excel 2010 have been added where they apply. Specifically, there is a small section on the new slicers for pivot tables, and there are several mentions of the new statistical functions (although the old functions still work).
Each chapter now has 10-20 more consistent and relevant "Conceptual Questions" in the end-of-chapter sections.
The first two linear programming examples in Chapter 13 (replacing the former Chapter 14) are replaced by two product mix models, where the second builds on the first. The previous "diet" model was overly complex as a first linear programming example.
Several chapter-opening vignettes are replaced with newer and more interesting ones and there are now many short "fundamental insights" throughout the chapters. These insights are designed to allow the students to step back from the details and see the really important ideas.
All updated screenshots and accompanying explanations to reflect Excel 2007 and updated add-ins. Tips on Excel 2010 have been added where applicable. This edition now provides access to Excel add-in software via an access code to a Premium Online Content Website for every student purchasing a new book.
Instructors' Resource CD: PowerPoints, and Solutions, Case, and Example files have been updated to reflect the core text 4th edition. The Test Bank has been updated with new questions and is now offered in ExamView testing software.

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Modeling Online Auctions (Statistics in Practice) Review

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(More customer reviews)This book can be read as a sequel to an earlier text, Statistical Methods in e-Commerce Research (Statistics in Practice). That looked at various types of websites, like Amazon and Wikipedia, while the current offering specialises to the key case of studying auction websites. Of these, eBay dominates the discussion, simply because it is the largest such website on the Internet. In a way, the exposition is simpler than it might have been just a few years ago, when Amazon and Yahoo also ran auctions. They were keying off eBay's success and hoped to take some of that business. But their efforts came to nought, so that now if you study online auctions, it is really only eBay and a handful of much smaller entities like uBid.
The screen captures in chapter 2 of typical web pages from an auction [on eBay] shows the complex spaghetti-like source code. Note that if you do decide to screen scrape, then this is brittle, since if the website makes just minor changes in the format of their pages, extensive changes to your parsing might be necessary, to extract the same information. But as the authors make clear, screen scraping has the advantage of being free. An alternative is to use a Web Service, if that is offered by the website. Much more efficient and robust. But not all websites have this, and those that do could require payment. Plus the information offered by their Web Service might not include data that you need.
Chapter 3 tackles the problem of how to simulate data using continuous distributions, when actual auction data often looks like a mixture of continuous and spiky inputs, where the latter are bids of high frequency, that stand significantly above the rest of the bid distribution.
Chapter 4 discusses various models - exponential, log, logistic and inverse logistic, that can be used to model a given auction. But the problem is that across a set of auctions, even for instances of the same item being offered, all such models might be observed. Where a specific auction could be best fitted by a log, say, while another auction looks like a logistic. The authors suggest a "Beta()" function that has only 2 parameters. This turns out to be easy to compute, and, depending on the choices of parameter values, can replicate each of the 4 earlier models. Just as importantly, the fitting of a Beta to a given auction can be automated, which gets around an earlier problem of having to make a manual choice between one of the earlier models.
Perhaps as interestingly, the chapter goes further, into studying what the book calls the spatial similarity between auctions. The spatial refers to auctions where items differ slightly. For example, a gaming computer that has different colours, and different disk sizes and different memory sizes installed. This is a multidimensional feature space, where the features might differ continuously [like memory] or discretely [like colour]. It reflects the well known attraction of eBay where if you search for a popular item, you can find hundreds [or even thousands] that differ in parameters like these. Modelling the behaviour of bidders when confronted by a surfeit of choices would be good. The authors show a way to tackle how to define a metric in the feature space.

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Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data
Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data.
The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include:
Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site
Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks
Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies
The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data
Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems

Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research.
Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes.
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The Multidimensional Data Modeling Toolkit: Making Your Business Intelligence Applications Smart with Oracle OLAP Review

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(More customer reviews)I thought this book was definitely worthwhile. It gave a good explanation of the multidimensional data framework and what can be done with it. Even though I don't use Oracle, I found the discussion interesting and the concepts useful. I do web marketing as a profession and have been curious about what kinds of analyses can be done with OLAP and business intelligence tools. I think this book did a good job of explaining that. I ran across the book at the office, I picked it up and found it very readable. The book included a lot of technical specifics about how to work with the Oracle system, which I glossed over mostly, but was still able to follow the rest of the book just fine. If you want specifics on Oracle (Analytic Workspaces, Oracle OLAP, etc), you'll find them here, if you want the concepts of OLAP and multidimensional data analysis, you'll find that too, all in a well-written volume with a few funny cartoons.

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The Multi-dimensional Data Modeling Toolkit provides an In-depth treatment of Oracle OLAP as well as a comprehensive examination of analytical techniques in a multidimensional OLAP framework. Application developers and database administrators will appreciate this book, packed with examples, tricks and techniques, and concrete illustrations of the programming elements needed to implement high-performance analytical applications. Data miners, statisticians, and power analysts will find the analysis techniques of particular interest.Written in a tutorial style, this book gives, in plain English, a step-by-step development of the defining principles of OLAP analysis through the lens of the programming language at the heart of Oracle's OLAP database option. This book represents over 15 years of hands-on experience developing multidimensional analytic applications for over a dozen companies.Whether you are new to business intelligence or a seasoned practitioner, The Multi-dimensional Data Modeling Toolkit will have something to offer.

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Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management Review

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(More customer reviews)Book is OK, but DON'T BUY THE CD-ROM! I dropped [a large amount] for what I thought would be a worth-while "self-learning" course on Data Mining programming in SAS. To my great disappointment, I found that while Olivia had included the code (which you can type in yourself), there was NO DATA PROVIDED, making the code all but useless (can't run the models with no data!). I e-mailed her asking for some kind of a sample data set. She agreed, but after months of begging she provided nothing whatsoever. Don't make the same mistake I did - STAY AWAY FROM THE CD-ROM!!!

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