Multi-GPU volume rendering using MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
KESOSD: keyword search over structured data
KEYS '12 Proceedings of the Third International Workshop on Keyword Search on Structured Data
Scalable subspace logistic regression models for high dimensional data
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Scalable random forests for massive data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Performance comparisons of spatial data processing techniques for a large scale mobile phone dataset
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Chinese medicine formula network analysis for core herbal discovery
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Toward intersection filter-based optimization for joins in MapReduce
Proceedings of the 2nd International Workshop on Cloud Intelligence
Simplifying MapReduce data processing
International Journal of Computational Science and Engineering
Accelerate MapReduce on GPUs with multi-level reduction
Proceedings of the 5th Asia-Pacific Symposium on Internetware
A Study on Linear Elastic FEM by Cloud Computing
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
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Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs. The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework. This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples.