A Hierarchical Distributed Processing Framework for Big Image Data

Real-time Big Data Analytical Architecture for Image Processing Framework

Big Data analytics and Hadoop architecture has significantly improved the real time data processing and storage Data with significant amount of velocity, veracity and variety was in the earlier processed by Data warehouse. The problem in the Data warehouse technique is that it always used to construct a sample out of the entire Data so, any given instant of time only part of the Data has used t...

Distributed Processing for Big Data Video Analytics

Intelligent Distributed Processing Methods for Big Data

Motivation Today, “Big Data” is a new information overloading problem in many different areas. Such areas include health cares (e.g., medical records, bioinformatics), e-sciences (e.g., physics, chemistry, and geology), and social sciences (e.g., politics). Thus, as we have various types of feasible data from a number of available sources, it is becoming increasingly more difficult to efficient...

A Data Colocation Grid Framework for Big Data Medical Image Processing - Backend Design

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be ...

A Distributed Phoenix++ Framework for Big Data Recommendation Systems

Recommendation systems are important big data applications that are used in many business sectors of the global economy. While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the highperformance shared-memory MapReduce system Phoenix++ to design a faster recommendation engine. In this paper, we design a distributed out-ofcore recommendation algor...