Randomized Primitives for Big Data Processing

Big Data Processing: Big Challenges and Opportunities

With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as...

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...

Real-time stream processing for Big Data

With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today’s Big Data repositories...

Safe Memory Regions for Big Data Processing

Recent work in high-performance systems written in man-aged languages (such as Java or C#) has shown that garbage-collection can be a significant performance bottleneck. Aclass of these systems, focused on big-data, create manyand often large data structures with well-defined lifetimes.In this paper, we present a language and a memory man-agement scheme based on user-man...