scholarly journals Effect of Replica Placement on the Reliability of Large-Scale Data Storage Systems

2010 ◽  
Author(s):  
Vinodh Venkatesan ◽  
Ilias Iliadis ◽  
Xiao-Yu Hu ◽  
Robert Haas ◽  
Christina Fragouli
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Storage Systems ◽  
Replica Placement ◽  
Large Scale Data ◽  
Scale Data

Large-Scale Data Streaming in Fog Computing and Its Applications

2021 ◽  
pp. 50-65
Author(s):  
Oshin Sharma ◽  
Anusha S.
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Data Streaming ◽  
Cloud Data ◽  
Large Scale Data ◽  
Emerging Trends ◽  
Iot Devices ◽  
Data Centres ◽  
Scale Data

The emerging trends in fog computing have increased the interests and focus in both industry and academia. Fog computing extends cloud computing facilities like the storage, networking, and computation towards the edge of networks wherein it offloads the cloud data centres and reduces the latency of providing services to the users. This paradigm is like cloud in terms of data, storage, application, and computation services, except with a fundamental difference: it is decentralized. Furthermore, these fog systems can process huge amounts of data locally and can be installed on hardware of different types. These characteristics make fog suitable for time- and location-based applications like internet of things (IoT) devices which can process large amounts of data. In this chapter, the authors present fog data streaming, its architecture, and various applications.

scholarly journals DNA fingerprinting ofStreptococcus uberis: a useful tool for epidemiology of bovine mastitis

1989 ◽  
Vol 103 (1) ◽  
pp. 165-171 ◽  
Author(s):  
A. W. Hill ◽  
J. A. Leigh
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Fragment Size ◽  
Bovine Mastitis ◽  
Chromosomal Dna ◽  
Restriction Patterns ◽  
Large Scale Data ◽  
Streptococcus Uberis ◽  
Image Analyser ◽  
Scale Data

SUMMARYA simple and reproducible typing system based on restriction fragment size of chromosomal DNA was developed to compare isolates ofStreptococcus uberisobtained from the bovine mammary gland. The endonuclease giving the most useful restriction patterns wasHindIII, although seven other endonucleases (Bgl1,EcoR1,Not1,Pst1,Sfi1,Sma1,Xba1) were also tested in the system. An image analyser was used to obtain a densitometric scan and a graphic display of the restriction patterns. Such a system will allow large scale data storage for future computer-aided comparison.

scholarly journals A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues

2021 ◽  
Vol 14 (1) ◽  
pp. 19
Author(s):  
Zineddine Kouahla ◽  
Ala-Eddine Benrazek ◽  
Mohamed Amine Ferrag ◽  
Brahim Farou ◽  
Hamid Seridi ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Search Time ◽  
Large Data ◽  
Open Problems ◽  
Large Scale Data ◽  
Indexing Techniques ◽  
Efficient Retrieval ◽  
Data Collections ◽  
Scale Data

The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.

Large-Scale Data Storage Scheme in Blockchain Ledger Using IPFS and NoSQL

2021 ◽  
pp. 91-116
Author(s):  
Randhir Kumar ◽  
Rakesh Tripathi
Keyword(s):  
Data Storage ◽  
Real World ◽  
Large Scale ◽  
Original Data ◽  
Size Reduction ◽  
Storage Model ◽  
Large Scale Data ◽  
Efficient Storage ◽  
Storage Scheme ◽  
Scale Data

The future applications of blockchain are expected to serve millions of users. To provide variety of services to the users, using underlying technology has to consider large-scale storage and assessment behind the scene. Most of the current applications of blockchain are working either on simulators or via small blockchain network. However, the storage issue in the real world is unpredictable. To address the issue of large-scale data storage, the authors have introduced the data storage scheme in blockchain (DSSB). The storage model executes behind the blockchain ledger to store large-scale data. In DSSB, they have used hybrid storage model using IPFS and MongoDB(NoSQL) in order to provide efficient storage for large-scale data in blockchain. In this storage model, they have maintained the content-addressed hash of the transactions on blockchain network to ensure provenance. In DSSB, they are storing the original data (large-scale data) into MongoDB and IPFS. The DSSB model not only provides efficient storage of large-scale data but also provides storage size reduction of blockchain ledger.

A Survey of Cloud-Based Services Leveraged by Big Data Applications

Web Services ◽  
2019 ◽  
pp. 1706-1716
Author(s):  
S. ZerAfshan Goher ◽  
Barkha Javed ◽  
Peter Bloodsworth
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Large Scale ◽  
Future Trends ◽  
Advantages And Disadvantages ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Big Data Storage ◽  
Scale Data

Due to the growing interest in harnessing the hidden significance of data, more and more enterprises are moving to data analytics. Data analytics require the analysis and management of large-scale data to find the hidden patterns among various data components to gain useful insight. The derived information is then used to predict the future trends that can be advantageous for a business to flourish such as customers' likes/dislikes, reasons behind customers' churn and more. In this paper, several techniques for the big data analysis have been investigated along with their advantages and disadvantages. The significance of cloud computing for big data storage has also been discussed. Finally, the techniques to make the robust and efficient usage of big data have also been discussed.

scholarly journals Massive Data Management and Sharing Module for Connectome Reconstruction

2020 ◽  
Vol 10 (5) ◽  
pp. 314
Author(s):  
Jingbin Yuan ◽  
Jing Zhang ◽  
Lijun Shen ◽  
Dandan Zhang ◽  
Wenhuan Yu ◽  
...  
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Large Scale ◽  
Rapid Development ◽  
Massive Data ◽  
Storage And Retrieval ◽  
Server Side ◽  
Large Scale Data ◽  
Client Side ◽  
Scale Data

Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability.

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