Handbook of Data Intensive Computing

PART I ARCHITECTURES AND SYSTEMS.- High Performance Network Architectures for Data Intensive Computing.- Architecting Data-Intensive Software Systems.- ECL: A High-Level Programming Language for Data-Intensive Supercomputing.- Scalable Storage for Data-Intensive Computing.- Computation and Storage Trade-off for Cost-Effective Storage of Scientific Datasets in the Cloud.- PART II TECHNOLOGIES AND TECHNIQUES.- Load Balancing Techniques for Data Intensive Computing.- Resource Management for Data Intensive Clouds Through Dynamic Federation: A Game Theoretic Approach.- SALT: Scalable Automated Linking Technology for Data Intensive Computing.- Parallel Processing, Multiprocessors and Virtualization in Data-Intensive Computing.- Challenges in Data Intensive Analysis and Visualization at Scientific Experimental User Facilities.- Large-Scale Data Analytics Using Ensemble Clustering.- Specification of Data Intensive Applications with Data Dependency and Abstract Clocks.- Ensemble Feature RankingMethods for Data Intensive Computing Applications.- Record Linkage Methodology and Applications.- Semantic Wrapper: Concise Semantic Querying of Legacy Relational Databases.- PART III SECURITY.- Security in Data Intensive Computing Systems.- Data Security and Privacy in Data-Intensive Supercomputing Clusters.- Information Security in Large Scale Distributed Systems.- Privacy and Security Requirements of Data Intensive Applications in Clouds.- PART IV APPLICATIONS.- On the Processing of Extreme Scale Datasets in the Geosciences.- Parallel Earthquake Simulations on Large-scale Multicore Supercomputers.- Data Intensive Computing in Bioinformatics: A Biomedical Case Study in Gene Selection and Filtering.- Design Space Exploration for Efficient Data Intensive Computing on SoCs.- Discovering Relevant Entities in Large-scale Social Information Systems.-Geospatial Data Management with Terrafly.- An Application for Processing Large and Non-uniform Media Objects on MapReduce-based Clusters.- Feature Selection Algorithms for Mining High-Dimensional DNA Microarray Data.- Application of Random Matrix Theory to Analyze Biological Data.- Keyword Search on Large Relational Databases: an OLAP-Oriented Approach.- A Distributed Publish/Subscribe System for Large Scale Sensor Networks.