Cost Minimization for Big Data Processing in Geo-Distributed Data Centers

Cost Minimization for Big Data Processing in Geo-Distributed Data Centres

Demand on big data is being increasing day by day and also increasing heavy burden on computation, storage and communication in data centres, which lead to considerable expenditure to data centre providers. So, cost minimization became an issue for the upcoming bid data.one of the main feature of bid data is coupling of data and computation as computation task. This can be done only when that c...

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...

Distributed Data Processing Frameworks for Big Graph Data

Recently we create so much data (2.5 quintillion bytes every day) that 90% of the data in the world today has been created in the last two years alone [1]. This data comes from sensors used to gather traffic or climate information, posts to social media sites, photos, videos, emails, purchase transaction records, call logs of cellular networks, etc. This data is big data. In this report, we fir...

Parallel and Distributed GIS for Processing Geo-data: An Overview

Geographic Information System (GIS) is a collection of applications whose tasks include (collaborating with other systems and) gathering geographic data, store and process spatio–temporal data (geo-data) and share the derived geographic knowledge with the users and other applications. Some of the most important routine applications of GIS are spatial analysis, digital elevation model (DEM) anal...