Java Data Mining: Strategy, Standard, and Practice

Mark Hornick has lead the Java Data Mining (JSR-73) expert group since its inception in July of 2000, and now leads the JSR-247 expert group working towards JDM 2.0. Mr. Hornick brings nearly 20 years experience in the design and implementation of advanced distributed systems, including in-database data mining, distributed object management, and Java APIs. Mr. Hornick is a senior manager in Oracle’s Data Mining Technologies group.Mr. Hornick joined Oracle through Oracle’s acquisition of Thinking Machines Corporation in 1999. Prior to Thinking Machines, where he served as architect for TMC’s next generation data mining software, Mr. Hornick was a Principal Investigator at GTE Laboratories, involved in advanced telecommunications network management software, distributed transaction management research, and distributed object management research.Mr. Hornick has contributed to several other data mining standards, including the Data Mining Group’s PMML, ISO SQL/MM for Data Mining, and the Object Management Group’s Common Warehouse Metadata. He has given talks at the International Conference on Knowledge Discovery and Databases, JavaOne, JavaPro Live!, and The ServerSide Symposium on data mining standards and JDM. He has also published various papers and articles over his career.Mr. Hornick holds a bachelor degree from Rutgers University in Computer Science, and a masters degree from Brown University, also in Computer science where he specialized in distributed object databases. With over 17 years of experience in the neural network industry, Erik Marcade, founder and chief technical officer for KXEN, is responsible for software development and information technologies. Prior to founding KXEN, Mr. Marcade developed real-time software expertise at Cadence Design Systems, accountable for advancing real-time software systems as well as managing “system-on-a-chip” projects. Before joining Cadence, Mr. Marcade spearheaded a project to restructure the marketing database of the largest French automobile manufacturer for Atos, a leading European information technology services company.In 1990, Mr. Marcade co-founded Mimetics, a French company that processes and sells development environment, optical character recognition (OCR) products and services using neural network technology.Prior to Mimetics, Mr. Marcade joined Thomson-CSF Weapon System Division as a software engineer and project manager working on the application of artificial intelligence for projects in weapons allocation, target detection and tracking, geo-strategic assessment, and software quality control. He contributed to the creation of Thomson Research Laboratories in Palo Alto, CA (Pacific Rim Operation-PRO) as senior software engineer. There he collaborated with Stanford University on the automatic landing and flare system for Boeing, and Kestrel Institute, a non-profit computer science research organization. He returned to France to head Esprit projects on neural networks development.Mr. Marcade holds an engineering degree from Ecole de l’Aeronautique et de l’Espace, specializing in process control, signal processing, computer science, and artificial intelligence J2EE and XML group leader and Principal Member of Technical Staff at Oracle Data Mining Technologies. Expert group member of Java Data Mining (JDM) standard developed under JSR-73. More than five years experience in developing applications using predictive technologies available in the Oracle Database. More than seven years of experience in working with Java and Internet technologies. Authored JDM article in Java Developer Journal. Holds a B.S in Engineering and Masters in Industrial Management from Indian Institute Of Technology, Kanpur.

• Part I - Strategy 1. Overview of Data Mining 2. Solving Problems in Industry 3. Data Mining Process 4. Mining Functions and Algorithms 5. JDM Strategy 6. Getting Started Part II - Standard 7. Java Data Mining Concepts 8. Design of the JDM API 9. Using the JDM API 10. XML Schema 11. Web Services Part III - Practice 12. Practical Problem Solving 13. Building Data Mining Tools using JDM 14. Getting Started with JDM Web Services 15. Impacts on IT Infrastructure 16. Vendor implementations Part IV. Wrapping Up 17. Evolution of Data Mining Standards 18. Preview of Java Data Mining 2.0 19. Summary A. Further Reading B. Glossary

"This is not only a great introduction to JDM, but also a great introduction for a practitioner to data mining in general. This is a “must-have" for anyone developing large-scale data mining applications in Java." --Robert Grossman, Open Data Group and University of Illinois at Chicago"It pleases me that the Java Community ProcessSM(JCPSM) Program could host the development of the Data Mining standard, JSR 73, whose evolution and usability are presented so compellingly in Java Data Mining: Standard, Strategy, and Practice. The authors have taken a unique approach to describing a broad range of aspects from strategies to problem solving with data mining technology in a variety of industries. The book is a ”must-read” for those who want to introduce themselves to Java data mining (JDM) and fully realize the strategic importance of this technology in an ever competitive environment." --Onno Kluyt, senior director, JCP Program at Sun Microsystems, Inc., and chair of the JCP"Java is now ubiquitous and over the past few years the Java world has shifted focus on--among other things--new frameworks, such as the Java Data Mining (JDM) framework. JDM addresses a clear need for standardization in data mining operations, yet to those approaching both Java and data mining the mountain seems as Everest. Hornick, Marcadé, and Venkayala could not have written this book at a better time. To the expert it is reference and map of the landscape, and to the novice it will be a constant guide and companion to each journey in JDM. This book is approachable, usable, practical, and necessary for any Java data mining software architect, developer, or analyst." --Frank Byrum, Chief Scientist, CorMine Intelligent Data, LLC