scholarly journals ESTemd: A Distributed Processing Framework for Environmental Monitoring based on Apache Kafka Streaming Engine

2020 ◽  
Author(s):  
Adeyinka Akanbi
Keyword(s):  
Environmental Monitoring ◽  
Distributed Processing ◽  
Processing Framework

scholarly journals Support for "cub" image format

2006 ◽  
Author(s):  
Pablo Burstein ◽  
Paul Yushkevich ◽  
James Gee
Keyword(s):  
Functional Mri ◽  
Functional Neuroimaging ◽  
Distributed Processing ◽  
3D Image ◽  
Subsequent Work ◽  
Anatomical Data ◽  
The University ◽  
Image Format ◽  
Processing Framework

VoxBo (www.voxbo.org) is a distributed processing framework, developed at the Center of Functional Neuroimaging at the University of Penssylvania, with the purpose of processing and analyzing brain functional MRI data. VoxBo is written in C++, and it is escentially a fair translation of some SPM functionality, originally written in Matlab. Along with the software, VoxBo defines two new image formats: 1) .cub, a 3D image format, used for anatomical data and single EPI frames. It is important to notice that this format defines a proper origin field, lacking, for instance, in Analyze. 2) .tes, a 4D image format, designed to contain the temporal EPI information. It is worth noticing that this format organizes the voxels in such a way to optimize longitudinal (temporal) voxel access. Furthermore, the tes format is compressed through encoding of the functional voxels alone, i.e., background voxels are not stored for each frame, but only for the first one.In this work we present the necessary IO classes that allow ImageFileReader and ImageFileWriter to properly identify, read and write .cub files only. We will provide .tes support in a subsequent work. This effort is carried out with the purpose of letting VoxBo implementors to take advantage of ITK’s power and overcome the limitations currently afflicting VoxBo.

scholarly journals A Hierarchical Distributed Processing Framework for Big Image Data

2016 ◽  
Vol 2 (4) ◽  
pp. 297-309 ◽  
Author(s):  
Le Dong ◽  
Zhiyu Lin ◽  
Yan Liang ◽  
Ling He ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Distributed Processing ◽  
Image Data ◽  
Processing Framework

Introduction

2019 ◽  
pp. 1-6
Author(s):  
Matteo Colombo ◽  
Liz Irvine ◽  
Mog Stapleton
Keyword(s):  
Distributed Processing ◽  
Extended Mind ◽  
Predictive Processing ◽  
Andy Clark ◽  
Cognitive Scientist ◽  
Mind And Brain ◽  
Opening Up ◽  
Intense Debate ◽  
Processing Framework

Andy Clark is a leading philosopher and cognitive scientist. His work has been wide-ranging and inspiring. The extended mind hypothesis, the power of parallel distributed processing, the role of language in opening up novel paths for thinking, the flexible interface between biological minds and artificial technologies, the significance of representation in explanations of intelligent behaviour, the promise of the predictive processing framework to unify the cognitive sciences: these are just some of the ideas illuminated by Clark’s work that have sparked intense debate across the sciences of mind and brain. This introduction puts into focus some of the major motifs running through Clark’s work and outlines the content and structure of the volume.

A Distributed Processing Framework of Incremental Text Clustering under the Background of Big Data

2014 ◽  
Vol 1049-1050 ◽  
pp. 1421-1426
Author(s):  
Zhen Tan ◽  
Yi Fan Chen ◽  
Zhong Lin Shi ◽  
Bin Ge ◽  
Yan Li Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Distributed Processing ◽  
Text Clustering ◽  
Rapid Expansion ◽  
Distributed Model ◽  
Sharp Decline ◽  
Model Framework ◽  
Data Volume ◽  
Processing Framework

In the era of big data, due to the rapid expansion of the data, the existing incremental text clustering algorithm has the drawback that the efficiency of algorithm will sharp decline with the time and data volume increasing. Because of poor timeliness and robustness, the algorithms are hard to be applied in practice. In this paper, we propose a distributed model framework of Single-Pass algorithm based on MapReduce, the experiments result of increment text cluster is accuracy, the algorithm effectively improve the computing efficiency of the algorithm and real-time of result. Algorithm has a great prospect under the background of big data.

Improving the performance of query processing using proposed resilient distributed processing technique

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
C. Lakshmi ◽  
K. UshaRani
Keyword(s):  
Query Processing ◽  
Design Methodology ◽  
Distributed Processing ◽  
Processing Technique ◽  
Distributed Environment ◽  
Content Type ◽  
Parallel Query ◽  
Parallel Query Processing ◽  
Mapreduce Model ◽  
Processing Framework

PurposeResilient distributed processing technique (RDPT), in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.Design/methodology/approachThe proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.FindingsQuery processing in Hadoop influences the distributed processing with the MapReduce model. MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers. Its results are valid for some extent size of the data.Originality/valuePig supports the required parallel processing framework with the following constructs during the processing of queries: FOREACH; FLATTEN; COGROUP.

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