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An efficient strategy for the collection and storage of large volumes of data for computation
2016
Journal of Big Data
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Acknowledgements The work by Uthayanath Suthakar was supported by a Brunel University London College of Engineering, Design and Physical Sciences Thomas Gerald Gray postgraduate research scholarship. ...
There exists a need for an efficient transfer technique that can move large amounts of data quickly and easily without impacting other users or applications [1] . ...
The core aims of the presented study were the following: • To propose and design efficient approaches for collecting and storing data for analytics that can also be integrated with other data pipelines ...
doi:10.1186/s40537-016-0056-1
fatcat:ytwsb4okzbhtxne3lahcvym3ba
An Intelligent Big Data Analytics System using Enhanced Map Reduce Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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An Intelligent Big Data Analytics System using Enhanced Map Reduce Techniques include a set of Methods, applications and strategy which helps the organization and industry to bring together the data and ...
on the enhanced the Map Reduce techniques, and then to avoid the unnecessary input and output data, optimize the data storage in order to achieve the best out sourcing of data privacy. ...
The traditional computing techniques that cannot be processed efficiently for example: face book, you tube ,which contains the large data sets on every day which comes under the concept of Big Data .volume ...
doi:10.35940/ijitee.b1105.1292s219
fatcat:a2yfmebjofdslgiobgnhxgujey
Efficient Data Management for Putting Forward Data Centric Sciences
[chapter]
2017
Communications in Computer and Information Science
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The novel and multidisciplinary data centric and scientific movement promises new and not yet imagined applications that rely on massive amounts of evolving data that need to be cleaned, integrated, and ...
This paper explores the key challenges and opportunities for data management in this new scientific context, and discusses how data management can best contribute to data centric sciences applications ...
The large spectrum of data persistence and management solutions are adapted for addressing workloads associated with Big Data volumes; and either simple read write operations or with more complex data ...
doi:10.1007/978-3-319-67162-8_25
fatcat:5xzhfomxbbdzdmhvniltnndijy
Survey on Big Data Mining Algorithms
2019
International Journal for Research in Applied Science and Engineering Technology
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Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. ...
The Big Data challenge is becoming one of the most exciting opportunities for the next years. ...
DATA MINING The explosive growth of available data volume is a result of the computerization of our society and the fast development of powerful data collection and storage tools. ...
doi:10.22214/ijraset.2019.6234
fatcat:dgcttowerjdkxhu334janvz5z4
EFFICIENT BIG DATA ANALYSIS USING HADOOP FRAMEWORK FOR SENSOR NETWORK DATA
2018
International Journal of Advanced Research in Computer Science
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So in an order to store, load and process the large scale sensory data we propose a pa per that enable the efficient and effective analysis of the Big Data and also handling the large volumes carried out ...
The major problem will be the amount of data collected, its storage and processing. ...
By the usage of the hadoop computing tool, the simultaneous or parallel processing of the data and efficient computation is possible. ...
doi:10.26483/ijarcs.v9i3.6105
fatcat:xz2znxn6yjbbrpxb2mlz2ey4ge
Toward a General I/O Layer for Parallel-Visualization Applications
2011
IEEE Computer Graphics and Applications
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We would also like to thank Kwan-Liu Ma for providing the jet dataset and the Argonne Leadership Computing Facility for computing resources and support. ...
This work is funded primarily through the Institute of Ultra-Scale Visualization (http://www.ultravis.org) under the auspices of the SciDAC program within the U.S. Department of Energy. ...
The partitioning strategy, which is important for scaling computation, conflicts with physical data storage. ...
doi:10.1109/mcg.2011.102
pmid:24808253
fatcat:qr4fzdperfebpjainnh23vxinq
Practical Guide to Storage of Large Amounts of Microscopy Data
2020
Microscopy Today
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This leads to limits on the collection of valuable data and slows data analysis and research progress. ...
Data storage strategy should be carefully considered and different options compared when designing imaging experiments. ...
Sandra Gesing, and Tom Morrell for valuable discussions. ...
doi:10.1017/s1551929520001091
fatcat:n2dgzodyubfztjf4qngktjv2za
Towards Exascale Distributed Data Management
2009
The international journal of high performance computing applications
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The large volume of data and the time needed to locate, access, analyze and visualize data will greatly impact on the scientific productivity of scientists and researchers in several domains. ...
Collections of data will be stored at different sites and made available to the users for further analysis and studies. ...
Storage and caching: storage technology is a vital part to manage large volume of data. Both hardware and software components will play a critical role in managing big scientific datasets. ...
doi:10.1177/1094342009347702
fatcat:kuaplw62indhhleuzwo3chywyu
An Iteration Aware Multidimensional Data Distribution Prototype for Computing Clusters
2006
2006 IEEE International Conference on Cluster Computing
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This system reduces both disk and network latency by transforming a large number of small requests into a small number of large requests that fill an ndimensional cache block on the cluster head node. ...
Disk and network latency must be taken into account when applying parallel computing to large multidimensional datasets because they can hinder performance by reducing the rate at which data can be fed ...
the dataset is too large to fit in the collective memory of the compute nodes. ...
doi:10.1109/clustr.2006.311863
dblp:conf/cluster/YanR06
fatcat:q6kcyfusrzczhdcff2fff2oxt4
A Strategy for Improving the Performance of Small Files in Openstack Swift
2018
International Journal of Computer Applications Technology and Research
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In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. ...
This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. ...
This work was supported by Science and Technology Department of Sichuan Province, Fund of Science and Technology Planning (No. 2018JY0290). ...
doi:10.7753/ijcatr0708.1006
fatcat:t4s3g3ml7jegxd54wfdgnmswxi
Guest Editors' Introduction: Special Issue on Storage for the Big Data Era
2018
Journal of Grid Computing
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TCGs represent the collected data and their dependencies thus providing efficient possibilities for their storage and management. ...
Privacy-preservation when dealing with large data sets is an important problem tackled by the study "A Privacy-preserving Compression Storage Method for Large Trajectory Data in Road Network" of Peipei ...
doi:10.1007/s10723-018-9439-1
fatcat:gqvgzigve5g2hn7pqpfxvhlqsu
PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets
2011
2011 IEEE International Conference on Cluster Computing
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The IDX data format provides efficient, cache oblivious, and progressive access to large-scale scientific datasets by storing the data in a hierarchical Z (HZ) order. ...
We then present a data model description and a novel aggregation strategy to enhance the scalability of the PIDX library. ...
and for efficient access to the underlying data files that make up an IDX dataset. ...
doi:10.1109/cluster.2011.19
dblp:conf/cluster/KumarVCSSPRCKG11
fatcat:acjkja3mxzeqljwjd4uir5tskq
Big Data in Capital Markets
2014
International Journal of Computer Applications
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These advancements are as diverse in the areas like collection, storage, aggregation, processing and analysis of financial data. ...
Moreover, with the advent of the Internet of things," there is an exponential rise in huge volumes of unstructured data from several different sources. ...
Compute Grids-Compute Grids offer a way of parallelizing processes across multiple servers for handling capacity, efficiency and failure issues. ...
doi:10.5120/18751-0008
fatcat:velxslknjzde7i4urlkmslhxby
Cloud big data application for transport
2016
International Journal of Agile Systems and Management
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Big data analytics brings new insights and useful correlations of large data collections providing undiscovered knowledge. ...
A cloud-oriented architecture opens new perspectives for providing efficient and personalised big data management and analytics services to (small) companies. ...
Acknowledgements The authors would like to thank the region Rhône-Alpes who finances the thesis work of Gavin Kemp by means of the ARC seven programs (http://www.arc7-territoiresmobilites.rhonealpes.fr ...
doi:10.1504/ijasm.2016.079940
fatcat:zvabrx65znc6hlrtl5d6vrcgda
NOSQL: A Very Dynamic Approach for Managing Big Data
2018
International Journal of Trend in Scientific Research and Development
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The Big Data is a global system and is considered a data collection that increases so large and can't be managed efficiently by traditional databases management systems. ...
The present digital world becomes more complex with Big Data means the volume, velocity and variety of data. ...
The NOSQL is a new open source, distributed data storage concept that is very efficient in terms of handling the huge volume of data. ...
doi:10.31142/ijtsrd12962
fatcat:j5zlbdkowffq7b4baysnjopqxa
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