About the Book

With the exponential growth of data comes an ever-increasing need to process and analyze the so-called Big Data. High performance computing architectures have been devised to address the needs for handling Big Data processing not only from a transaction processing viewpoint but also from an analytics perspective. This book provides a comprehensive view on the recent trend toward high performance computing architectures especially as it relates to analytics and data mining.Topics that are covered include: big data (and its characteristics), high performance computing for analytics, massively parallel processing (MPP) databases, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms, and analytics environments. However none gives a historical and comprehensive view of all these separate topics in a single document. Through the understanding of these topics corporations can create an ideal analytic environment that is better suited to the challenges of today's analytics demands.

About the Author

Jared Dean is Director of Research and Development at SAS Institute. He is rresponsible for all aspects of development in SAS' global data mining solutions. This includes customer engagements, new feature development, technical support, sales support, and product integration.

Table of Contents:
Forward Preface Acknowledgments Introduction Big Data Timeline Why This Topic Is Relevant Now Is Big Data a Fad? Where Using Big Data Makes a Big Difference Part One The Computing Environment Chapter 1 Hardware Storage (Disk) Central Processing Unit Memory Network Chapter 2 Distributed Systems Database Computing File System Computing Considerations Chapter 3 Analytical Tools Weka Java and JVM Languages R Python SAS Part Two Turning Data into Business Value Chapter 4 Predictive Modeling A Methodology for Building Models sEMMA Binary Classification Multilevel Classification Interval Prediction Assessment of Predictive Models Chapter 5 Common Predictive Modeling Techniques RFM Regression Generalized Linear Models Neural Networks Decision and Regression Trees Support Vector Machines Bayesian Methods Network Classification Ensemble Methods Chapter 6 Segmentation Cluster Analysis Distance Measures (Metrics) Evaluating Clustering Number of Clusters K]means Algorithm Hierarchical Clustering Profiling Clusters Chapter 7 Incremental Response Modeling Building the Response Model Measuring the Incremental Response Chapter 8 Time Series Data Mining Reducing Dimensionality Detecting Patterns Time Series Data Mining in Action: Nike+ FuelBand Chapter 9 Recommendation Systems What Are Recommendation Systems? Where Are They Used? How Do They Work? Assessing Recommendation Quality Recommendations in Action: SAS Library Chapter 10 Text Analytics Information Retrieval Content Categorization Text Mining Text Analytics in Action: Let's Play Jeopardy! Part Three Success Stories of Putting It All Together Chapter 11 Case Study of a Large U.S.]Based Financial Services Company Traditional Marketing Campaign Process High]Performance Marketing Solution Value Proposition for Change Chapter 12 Case Study of a Major Health Care Provider CAHPS HEDIS HOS IRE Chapter 13 Case Study of a Technology Manufacturer Finding Defective Devices How They Reduced Cost Chapter 14 Case Study of Online Brand Management Chapter 15 Case Study of Mobile Application Recommendations Chapter 16 Case Study of a High]Tech Product Manufacturer Handling the Missing Data Application beyond Manufacturing Chapter 17 Looking to the Future Reproducible Research Privacy with Public Data Sets The Internet of Things Software Development in the Future Future Development of Algorithms In Conclusion About the Author Appendix References Index