In this survey paper, we have scrutinized and revealed the benefits of Hadoop in the Healthcare sector using data mining where the data flow was in massive volume. In developing countries like India with huge population, there exists various problems in the field of healthcare with respect to the expenses met by the economically underprivileged people, access to the hospitals and research in th...

Open-source software frameworks such as Apache Hadoop and Robot Operating System (ROS) are helped researchers to reduce arduous engineering work releasing them to concentrate more on the core research. The impact of extensive knowledge repositories of robot operating system interconnected with a large distributed data storage framework entails enormous developmental step in the future robotic s...

With the increasing popularity of cloud computing, Hadoop has become a widely used open source cloud computing framework for large scale data processing. However, few efforts have been made to demonstrate the applicability of Hadoop to various real-world application scenarios in fields other than server side computations such as web indexing, etc. In this paper, we use the Hadoop cloud computin...

In this paper, we explore the benefits and possibilities about the implementation of multi-agents simulation framework on a Hadoop cloud. Scalability, fault-tolerance and failure-recovery have always been a challenge for a distributed systems application developer. The highly efficient fault tolerant nature of Hadoop, flexibility to include more systems on the fly, efficient load balancing and ...

In today’s scenario as data is increasing day by day so text data mining approaches are playing a vital role in extracting many potential information and association from a large amount of text data. The term data mining is used for methods that analyze data and data mining deals with structured data, whereas text mining presents different formats that are unstructured or semi-structured data. ...

Problem statement: Features and challenges of the PACS server solutions are elaborated in the context of large scale computing. Approach: Hadoop is a pivotal piece of the data mining renaissance offers the ability to tackle large data sets in ways that weren’t previously feasible and clarifies certain functionalities such as MapReducer and Hadoop distributed file system. Results: The PACS serve...

Previously, we proposed a Hadoop-based Distributed Video Transcoding System that transcodes a large number of video data sets into specific video formats depending on user-requested options. In order to reduce the transcoding time exponentially, we apply a Hadoop Distributed File System (HDFS) and a MapReduce framework to our system. Hadoop and MapReduce are designed to process petabyte-scale t...

This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialH...

Nowadays most of the cloud applications process large amount of data to provide the desired results. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and analyzing information. Dealing with large data volumes requires two things: 1) Inexpensive, reliable storagee 2) New tools for analyzing unstruct...

Pre-stack Kirchhoff time migration (PKTM) is one of the most widely used migration algorithms in seismic imaging area. However, PKTM takes considerable time due to its high computational cost, which greatly affects the working efficiency of oil industry. Due to its high fault tolerance and scalability, Hadoop has become the most popular platform for big data processing. To overcome the shortcom...