313,565 research outputs found
PABED A Tool for Big Education Data Analysis
Cloud computing and big data have risen to become the most popular
technologies of the modern world. Apparently, the reason behind their immense
popularity is their wide range of applicability as far as the areas of interest
are concerned. Education and research remain one of the most obvious and
befitting application areas. This research paper introduces a big data
analytics tool, PABED Project Analyzing Big Education Data, for the education
sector that makes use of cloud-based technologies. This tool is implemented
using Google BigQuery and R programming language and allows comparison of
undergraduate enrollment data for different academic years. Although, there are
many proposed applications of big data in education, there is a lack of tools
that can actualize the concept into practice. PABED is an effort in this
direction. The implementation and testing details of the project have been
described in this paper. This tool validates the use of cloud computing and big
data technologies in education and shall head start development of more
sophisticated educational intelligence tools
A network approach for managing and processing big cancer data in clouds
Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data
CloudTree: A Library to Extend Cloud Services for Trees
In this work, we propose a library that enables on a cloud the creation and
management of tree data structures from a cloud client. As a proof of concept,
we implement a new cloud service CloudTree. With CloudTree, users are able to
organize big data into tree data structures of their choice that are physically
stored in a cloud. We use caching, prefetching, and aggregation techniques in
the design and implementation of CloudTree to enhance performance. We have
implemented the services of Binary Search Trees (BST) and Prefix Trees as
current members in CloudTree and have benchmarked their performance using the
Amazon Cloud. The idea and techniques in the design and implementation of a BST
and prefix tree is generic and thus can also be used for other types of trees
such as B-tree, and other link-based data structures such as linked lists and
graphs. Preliminary experimental results show that CloudTree is useful and
efficient for various big data applications
- …