scholarly journals Blockchain and novel coronavirus: Towards preventing COVID-19 and future pandemics

2020 ◽  
Vol 2 (3) ◽  
pp. 215-218
Author(s):  
Amirul Azim ◽  
◽  
Muhammad Nazrul Islam ◽  
Paul E. Spranger ◽  
◽  
...  
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Storage System ◽  
Data Management System ◽  
The Novel ◽  
Centralized Control ◽  
Novel Technology ◽  
Distributed Ledger ◽  
Novel Coronavirus ◽  
Present World

The present world has observed the SARS-CoV2 or COVID-19 spreading rapidly with a rising death toll and transmission rates with an absence of proper data management and information sharing. The current traditional database storage system has the limitations of a centralized control system and tampering of data, particularly when it is being shared with others. The Novel technology known as “Blockchain” is a distributed ledger technology that acts as a shared database, keeping all its copies synced and verified. The objective of this article is to study the concept of a Blockchain based pandemic data management system that would ensure unified patients’ data storage and reliable data management to trackdown coronavirus to combat against this and future pandemics.

scholarly journals PetaShare: A Reliable, Efficient and Transparent Distributed Storage Management System

2011 ◽  
Vol 19 (1) ◽  
pp. 27-43
Author(s):  
Tevfik Kosar ◽  
Ismail Akturk ◽  
Mehmet Balman ◽  
Xinqi Wang
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Management System ◽  
Distributed Storage ◽  
Storage System ◽  
Data Management System ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Data Movement ◽  
Reliability And Availability

Modern collaborative science has placed increasing burden on data management infrastructure to handle the increasingly large data archives generated. Beside functionality, reliability and availability are also key factors in delivering a data management system that can efficiently and effectively meet the challenges posed and compounded by the unbounded increase in the size of data generated by scientific applications. We have developed a reliable and efficient distributed data storage system, PetaShare, which spans multiple institutions across the state of Louisiana. At the back-end, PetaShare provides a unified name space and efficient data movement across geographically distributed storage sites. At the front-end, it provides light-weight clients the enable easy, transparent and scalable access. In PetaShare, we have designed and implemented an asynchronously replicated multi-master metadata system for enhanced reliability and availability, and an advanced buffering system for improved data transfer performance. In this paper, we present the details of our design and implementation, show performance results, and describe our experience in developing a reliable and efficient distributed data management system for data-intensive science.

scholarly journals The BNLBox Cloud Storage Service

2020 ◽  
Vol 245 ◽  
pp. 04011
Author(s):  
Ofer Rind ◽  
Hironori Ito ◽  
Guangwei Che ◽  
Tim Chou ◽  
Robert Hancock ◽  
...  
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Cloud Storage ◽  
Storage System ◽  
Data Access ◽  
Scientific Data ◽  
Data Management System ◽  
Easy Access ◽  
Cloud Storage Service ◽  
Storage Service

Large scientific data centers have recently begun providing a number of different types of data storage in order to satisfy the various needs of their users. Users with interactive accounts, for example, might want a POSIX interface for easy access to the data from their interactive machines. Grid computing sites, on the other hand, likely need to provide an X509-based storage protocol, like SRM and GridFTP, since the data management system is built upon them. Meanwhile, an experiment producing large amounts of data typically demands a service that provides archival storage for the safe keeping of their unique data. To access these various types of data, users must use specific sets of commands tailored to their respective storage, making access to their data complex and difficult. BNLBox is an attempt to provide a unified and easy to use storage service for all BNL users, to store their important documents, code and data. It is a cloud storage system with an intuitive web interface for novice users. It provides an automated synchronization feature that enables users to upload data to their cloud storage without manual intervention, freeing them to focus on analysis rather than data management software. It provides a POSIX interface for local interactive users, which simplifies data access from batch jobs as well. At the same time, it also provides users with a straightforward mechanism for archiving large data sets for later processing. The storage space can be used for both code and data within the compute job environment. This paper will describe various aspects of the BNLBox storage service.

Database Considerations for an ICU Patient Data Management System

1991 ◽  
Author(s):  
N. Fumai ◽  
C. Collet ◽  
M. Petroni ◽  
K. Roger ◽  
E. Saab ◽  
...  
Keyword(s):  
Data Management ◽  
Real Time ◽  
Data Storage ◽  
Relational Database ◽  
Management System ◽  
Patient Data ◽  
Patient Data Management System ◽  
Data Management System ◽  
Data Types ◽  
Management Structure

Abstract A Patient Data Management System (PDMS) is being developed for use in the Intensive Care Unit (ICU) of the Montreal Children’s Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Due to the large amounts of data involved, the data storage and data management processes are an important task of the PDMS. The data management structure must integrate varied data types and provide database support for different applications, while preserving the real-time acquisition of network data. This paper outlines a new data management structure which is based primarily on OS/2’s Extended Edition relational database. The relational database design is expected to solve the query shortcomings of the previous data management structure, as well as offer support for security and concurrency. The discussion will also highlight future advantages available from a network implementation.

scholarly journals The Grid Enabled Mass Storage System (GEMSS): the Storage and Data management system used at the INFN Tier1 at CNAF.

2012 ◽  
Vol 396 (4) ◽  
pp. 042051 ◽  
Author(s):  
Pier Paolo Ricci ◽  
Daniele Bonacorsi ◽  
Alessandro Cavalli ◽  
Luca Dell'Agnello ◽  
Daniele Gregori ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Storage System ◽  
Data Management System ◽  
Mass Storage ◽  
Mass Storage System

scholarly journals Data management pipeline for plant phenotyping in a multisite project

2012 ◽  
Vol 39 (11) ◽  
pp. 948 ◽  
Author(s):  
Kenny Billiau ◽  
Heike Sprenger ◽  
Christian Schudoma ◽  
Dirk Walther ◽  
Karin I. Köhl
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Data Exchange ◽  
Data Access ◽  
Data Management System ◽  
Plant Phenotyping ◽  
Data Types ◽  
File Server ◽  
Management Concepts ◽  
Term Basis

In plant breeding, plants have to be characterised precisely, consistently and rapidly by different people at several field sites within defined time spans. For a meaningful data evaluation and statistical analysis, standardised data storage is required. Data access must be provided on a long-term basis and be independent of organisational barriers without endangering data integrity or intellectual property rights. We discuss the associated technical challenges and demonstrate adequate solutions exemplified in a data management pipeline for a project to identify markers for drought tolerance in potato. This project involves 11 groups from academia and breeding companies, 11 sites and four analytical platforms. Our data warehouse concept combines central data storage in databases and a file server and integrates existing and specialised database solutions for particular data types with new, project-specific databases. The strict use of controlled vocabularies and the application of web-access technologies proved vital to the successful data exchange between diverse institutes and data management concepts and infrastructures. By presenting our data management system and making the software available, we aim to support related phenotyping projects.

Research on Data Management System for Live Detection

2015 ◽  
Vol 734 ◽  
pp. 22-26
Author(s):  
Ying Chen ◽  
Hui Fen Liao ◽  
Qian Peng ◽  
Wei Wang ◽  
Yong Xing Cao ◽  
...  
Keyword(s):  
Information System ◽  
Power System ◽  
Data Management ◽  
Data Storage ◽  
Management System ◽  
Secure Communication ◽  
Field Testing ◽  
Electric Power System ◽  
Data Management System ◽  
Secure Access

In order to raise the efficiency of on-site live detection, we proposed a new study of live detection data management system which based piconet and Electric Power System Secure Access Platform(EPSSAP). Piconet of comprehensive multi professional testing equipments with Bluetooth technology is established to wireless acquisite field testing data. Using mobile APN secure access technology, secure communication between the intranet information system and PC power system is realized. Application of the platform will provide basis for a unified multiple data acquisition system, extend functions of intranet information system, such as data storage, information processing regulate the process of on-site live detection work and improve the efficiency of the use of live detection data.

scholarly journals Data Management System for a Semiautonomous Shuttle Car for Underground Room and Pillar Coal Mines

Automation ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 153-172
Author(s):  
Vasilis Androulakis ◽  
Steven Schafrik ◽  
Joseph Sottile ◽  
Zach Agioutantis
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Real Time ◽  
Data Storage ◽  
Autonomous Navigation ◽  
Management System ◽  
Reliable Data ◽  
The Other ◽  
Data Management System ◽  
Shuttle Car

In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.

Design of Smart Healthcare Data Management System Based on Hadoop

2014 ◽  
Vol 998-999 ◽  
pp. 1121-1124 ◽  
Author(s):  
Min Zhang ◽  
Ren Zhang ◽  
Cheng Sheng Liu
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Management System ◽  
Cloud Storage ◽  
Low Cost ◽  
Structural Data ◽  
Data Management System ◽  
Traditional Management ◽  
Healthcare Data ◽  
Smart Healthcare

This paper describes a smart healthcare data management system based on hadoop. Aiming at the disadvantage of Traditional management of medical data such as the increasing cost of consumption and the limited availability of the data, the smart healthcare data management system in this paper introduces a hybrid storage architecture including designs of Structured data storage which supported by RDBMS and Non-structural data storage which supported by Hadoop. This smart healthcare data management system has the advantages of low-cost, high fault tolerance, and scalability, and builds a cloud storage platform applied in the system of smart healthcare.

scholarly journals RESEARCH DATA MANAGEMENT SYSTEM DESIGN (CASE ICT RESEARCH AND HUMAN RESOURCES DEVELOPMENT AGENCY MCIT)

2017 ◽  
Vol 7 (2) ◽  
pp. 143
Author(s):  
Onny Rafizan
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Research Data ◽  
Data Management System ◽  
Primary Data ◽  
Management Information ◽  
User Requirement ◽  
Research Activities ◽  
Data Credibility ◽  
Research Institute

<p class="JGI-AbstractIsi"><span lang="EN">Management of research data is important for every research institute. The absence of activity for centralized data management and data storage within the research institute has the potential to make the research data disappear or not reusable. Data management information system is needed by research institute in order to manage data centrally by paying attention to the business process of research and requirement to the process of storage and sharing of research data. Primary data collection by means of interviews to the technical sources with expertise and direct experience are related to the management of research data, as well as validation of data credibility are tiered to higher research positions (madya and main) and to structural officials who facilitate research activities to obtain business modeling and user requirement. The result of this study is the design of data management information systems in accordance with the conditions of the R &amp; D organizations to facilitate the activities of storage and sharing access the research data.</span></p>

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