High Performance Heterogeneous Data Storage System for High Frequency Sensor Data in a Landslide Laboratory

2017 ◽  
pp. 371-379
Author(s):  
Guntha Ramesh ◽  
Hariharan Balaji ◽  
T. Hemalatha
Keyword(s):  
Data Storage ◽  
High Frequency ◽  
High Performance ◽  
Storage System ◽  
Heterogeneous Data ◽  
Sensor Data ◽  
Frequency Sensor ◽  
Data Storage System

Overview of Big-Data-Intensive Storage and Its Technologies

2018 ◽  
pp. 33-74
Author(s):  
Richard S. Segall ◽  
Jeffrey S. Cook
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
Management Strategies ◽  
Sensor Data ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Future Challenges ◽  
Data Storage System

This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about data-intensive computing and types of parallel processing approaches. It also highlights the points that display how data-intensive computing systems differ from other forms of computing. A discussion on the importance of Big Data computing is put forth. The current and future challenges of storage in genomics are discussed in detail. Also, storage and data management strategies are given. The chapter's focus is then on the software challenges for storage. Storage use cases are provided like DataDirect Networks, SDSC, etc. The list of storage tools and their details are provided. A small section discusses the sensor data storage system. Then a table is provided that shows the top 10 cloud storage systems for data-intensive computing using Big Data in the world. Top 500 Big Data storage servers statistics are also displayed effectively by the images from Top500 website.

High-Performance Mixed PRML Architecture for Optical Data Storage System

2005 ◽  
Vol 44 (5B) ◽  
pp. 3436-3439 ◽  
Author(s):  
Junghyun Lee ◽  
Eun-Jin Ryu ◽  
Jae-Wook Lee ◽  
Eing-Seob Cho ◽  
Maxim Konakov ◽  
...  
Keyword(s):  
Data Storage ◽  
High Performance ◽  
Storage System ◽  
Optical Data Storage ◽  
Optical Data ◽  
Data Storage System

scholarly journals Enhanced dynamic data storage for enabling low cost space astronomy observations for capital constrained astronomy organizations

2017 ◽  
Vol 1 (4) ◽  
pp. 139-144
Author(s):  
Periola AA ◽  
Ohize H
Keyword(s):  
High Performance Computing ◽  
Data Storage ◽  
High Performance ◽  
Storage System ◽  
Small Satellite ◽  
Effective Manner ◽  
Data Storage System ◽  
Space Data ◽  
Performance Computing ◽  
Computing Infrastructure

Mechanisms that reduce the capital and operational costs are important for increased participation in astronomy. It is important that capital constrained organizations can engage in astronomy in cost effective manner. Approaches such as telescope conversion and using small satellites reduce the cost of astronomy observations. However, astronomy data observed by converted and small satellite telescopes require storage and processing by high performance computing infrastructure. High performance computing infrastructure acquisition is expensive for capital constrained astronomy organizations. The reduction in costs obtained by using converted and small satellite telescopes is not matched by a corresponding reduction in high performance computing. This paper addresses this challenge and proposes using a software defined space data storage system. The software defined space data storage system considers space telescopes as primary satellites and telecommunication and earth observation satellites as secondary satellites. The primary and secondary satellites are grouped in logical clusters. Secondary satellites are temporal data centers that store the astronomy data that cannot be held on primary satellites. The discussion in this paper presents algorithms that enable the identification of suitable secondary satellites and also influence the entry and exit of secondary satellite into dynamic clusters.

scholarly journals DATA STORAGE FOR PARALLEL COMPUTING IN INDIVIDUAL SOFTWARE ENVIRONMENTS WHEN SOLVING MATERIALS SCIENCE PROBLEMS

2021 ◽  
Author(s):  
Konstantin Volovich ◽  
Sergey Denisov
Keyword(s):  
Parallel Computing ◽  
High Performance Computing ◽  
Data Storage ◽  
High Performance ◽  
Materials Science ◽  
Storage System ◽  
Parallel Computations ◽  
Software Systems ◽  
Data Storage System ◽  
Performance Computing

The paper discusses methods of data storage when performing parallel computations in a multicomputer high-performance computing complex in virtual software environments. Approaches to building a data storage system using software systems designed to solve problems of materials science are proposed.

scholarly journals How Good Is the STW Sensor? An Account from a Larger Shipping Company

2021 ◽  
Vol 9 (5) ◽  
pp. 465
Author(s):  
Angelos Ikonomakis ◽  
Ulrik Dam Nielsen ◽  
Klaus Kähler Holst ◽  
Jesper Dietz ◽  
Roberto Galeazzi
Keyword(s):  
High Frequency ◽  
Additional Data ◽  
Empirical Method ◽  
Influential Factor ◽  
Sensor Data ◽  
Sea State ◽  
Shipping Company ◽  
Frequency Sensor ◽  
Semi Empirical

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that 26% of the ships of the dataset’s fleet warrant further investigation.

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