2,466 research outputs found
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
Astronomy in the Cloud: Using MapReduce for Image Coaddition
In the coming decade, astronomical surveys of the sky will generate tens of
terabytes of images and detect hundreds of millions of sources every night. The
study of these sources will involve computation challenges such as anomaly
detection and classification, and moving object tracking. Since such studies
benefit from the highest quality data, methods such as image coaddition
(stacking) will be a critical preprocessing step prior to scientific
investigation. With a requirement that these images be analyzed on a nightly
basis to identify moving sources or transient objects, these data streams
present many computational challenges. Given the quantity of data involved, the
computational load of these problems can only be addressed by distributing the
workload over a large number of nodes. However, the high data throughput
demanded by these applications may present scalability challenges for certain
storage architectures. One scalable data-processing method that has emerged in
recent years is MapReduce, and in this paper we focus on its popular
open-source implementation called Hadoop. In the Hadoop framework, the data is
partitioned among storage attached directly to worker nodes, and the processing
workload is scheduled in parallel on the nodes that contain the required input
data. A further motivation for using Hadoop is that it allows us to exploit
cloud computing resources, e.g., Amazon's EC2. We report on our experience
implementing a scalable image-processing pipeline for the SDSS imaging database
using Hadoop. This multi-terabyte imaging dataset provides a good testbed for
algorithm development since its scope and structure approximate future surveys.
First, we describe MapReduce and how we adapted image coaddition to the
MapReduce framework. Then we describe a number of optimizations to our basic
approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table
An Efficient Transport Protocol for delivery of Multimedia An Efficient Transport Protocol for delivery of Multimedia Content in Wireless Grids
A grid computing system is designed for solving complicated scientific and
commercial problems effectively,whereas mobile computing is a traditional
distributed system having computing capability with mobility and adopting
wireless communications. Media and Entertainment fields can take advantage from
both paradigms by applying its usage in gaming applications and multimedia data
management. Multimedia data has to be stored and retrieved in an efficient and
effective manner to put it in use. In this paper, we proposed an application
layer protocol for delivery of multimedia data in wireless girds i.e.
multimedia grid protocol (MMGP). To make streaming efficient a new video
compression algorithm called dWave is designed and embedded in the proposed
protocol. This protocol will provide faster, reliable access and render an
imperceptible QoS in delivering multimedia in wireless grid environment and
tackles the challenging issues such as i) intermittent connectivity, ii) device
heterogeneity, iii) weak security and iv) device mobility.Comment: 20 pages, 15 figures, Peer Reviewed Journa
Visualizing Historical Book Trade Data: An Iterative Design Study with Close Collaboration with Domain Experts
The circulation of historical books has always been an area of interest for
historians. However, the data used to represent the journey of a book across
different places and times can be difficult for domain experts to digest due to
buried geographical and chronological features within text-based presentations.
This situation provides an opportunity for collaboration between visualization
researchers and historians. This paper describes a design study where a variant
of the Nine-Stage Framework was employed to develop a Visual Analytics (VA)
tool called DanteExploreVis. This tool was designed to aid domain experts in
exploring, explaining, and presenting book trade data from multiple
perspectives. We discuss the design choices made and how each panel in the
interface meets the domain requirements. We also present the results of a
qualitative evaluation conducted with domain experts. The main contributions of
this paper include: 1) the development of a VA tool to support domain experts
in exploring, explaining, and presenting book trade data; 2) a comprehensive
documentation of the iterative design, development, and evaluation process
following the variant Nine-Stage Framework; 3) a summary of the insights gained
and lessons learned from this design study in the context of the humanities
field; and 4) reflections on how our approach could be applied in a more
generalizable way
- โฆ