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Using Technology to Manage Clinical Information Complexity within Hospitals

How to Influence Physicians to use Electronic Medical Records (ENR)? Social Influence Tactics and their Effects on EMR Implementation Effectiveness

A Knowledge Management Perspective of the Evolution of Successfully Implemented Teleconsultation projects

The Role of IT and Evidence based medicine in the Standardization of Medical Work

Electronic Health Records: Concepts, Theory and Issues

Personal Health Records and Consumer Informatics - Part 2

Telehealth Technologies - Part 2: The opportunities and challenges of Telehealth at Loma Linda

Although Petaflop systems have only recently become a reality, scientists and governments are eagerly anticipating Exascale capabilities. There is a significant effort to achieve an Exaflop within the decade. However, unlike Petascale systems, Exascale systems will not be straightforward extrapolations from predecessors, and success is not a foregone conclusion. The chief combatant is power consumption: systems must become enormously more energy-efficient to make Exascale practical. General-purpose cores are too power-hungry, and thus more specialized forms of parallelism must be exploited. However, it is highly likely that this will require changes in applications and programming models, especially considering that these systems will require tens of millions of cores. Complicating matters further, error detection and resiliency are already major issues for Petascale systems, and improving fault tolerance typically requires more energy. We will discuss innovations in technology, architecture, and software tha

Opening Session Remarks

Note: This is the part-1 of the tutorial. Please see Mapreduce Part-2 for the rest of the presentation. Abstract: Apache Hadoop has become the platform of choice for developing large-scale data-intensive applications. In this tutorial, we will discuss design philosophy of Hadoop, describe how to design and develop Hadoop applications and higher-level application frameworks to crunch several terabytes of data, using anywhere from four to 4,000 computers. We will discuss solutions to common problems encountered in maximizing Hadoop application performance. We will also describe several frameworks and utilities developed using Hadoop that increase programmer-productivity and application-performance.