Data Storage, Processing, and Visualization for the Australia Telescope Compact Array

AbstractWe present three Virtual Observatory tools developed at the Australia Telescope National Facility (ATNF) for the storage, processing and visualization of Australia Telescope Compact Array (ATCA) data. These are the Australia Telescope Online Archive, a prototype data-reduction pipeline, and the Remote Visualization System. These tools were developed in the context of the Virtual Observatory and were intended to be both useful for astronomers and technology demonstrators. We discuss the design and implementation of these tools, as well as issues that should be considered when developing similar systems for future telescopes.

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Design and implementation of the contents visualization system for social networks

2013 IEEE Third International Conference on Information Science and Technology (ICIST) ◽

10.1109/icist.2013.6747595 ◽

2013 ◽

Author(s):

Jong-Hoon Lee ◽

Jung-Tae Kim ◽

Ik Kyun Kim ◽

Ki-Jun Han

Keyword(s):

Social Networks ◽

Visualization System ◽

Design And Implementation

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Design and Implementation of 3-Dimension Visualization System of Primary Wheat Diseases

2009 International Conference on Innovation Management ◽

10.1109/icim.2009.32 ◽

2009 ◽

Author(s):

Shijuan Li ◽

Yeping Zhu ◽

Zhanguang Hai

Keyword(s):

Visualization System ◽

Design And Implementation ◽

Wheat Diseases

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Design and Implementation of Automobile Industry Cluster Information Service System Based on GIS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.1872 ◽

2014 ◽

Vol 687-691 ◽

pp. 1872-1875

Author(s):

Ying Ying Weng

Keyword(s):

Data Storage ◽

Automobile Industry ◽

Information Service ◽

Analysis Data ◽

Industry Cluster ◽

Demonstration Analysis ◽

Design And Implementation ◽

Data Base Design ◽

Design Function ◽

Space Data

According to the automobile industry cluster situation at present, the paper introduces the method and train of thought of automobile industry cluster based on GIS from demand analysis, data base design, function design and so on. Also demonstration analysis in one automobile industry cluster. In construction, use client layer, web layer, business logic layer and data layer to design the system architecture. It improved the system maintenance performance, used Oracle and ArcSDE to access space data and attribute data, and mass data storage management. Research results maybe give some significance reference for design and implementation of automobile industry cluster.

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Development of Remote Visualization and Collaborative Visualization System in ITBL Grid Environment

Lecture Notes in Computer Science - High Performance Computing ◽

10.1007/978-3-540-39707-6_21 ◽

2003 ◽

pp. 269-277

Author(s):

Yoshio Suzuki ◽

Nobuko Matsumoto

Keyword(s):

Grid Environment ◽

Remote Visualization ◽

Visualization System ◽

Collaborative Visualization

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Managing Granularity in Design and Implementation of Geospatial Web Services

Advances in Geospatial Technologies - Geospatial Web Services ◽

10.4018/978-1-60960-192-8.ch002 ◽

2011 ◽

pp. 18-35 ◽

Cited By ~ 1

Author(s):

Elias Z. K. Ioup ◽

John T. Sample

Keyword(s):

Web Services ◽

Data Storage ◽

User Needs ◽

Design Decisions ◽

Design And Implementation ◽

Performance Requirements ◽

And Performance ◽

Geospatial Services ◽

Geospatial Web Services

Granularity is often ignored when designing geospatial Web services. Choices relating to granularity affect service interfaces, data storage and organization, and XML format design. This chapter highlights the importance of analyzing usage and performance requirements when deciding on granularity choices in the design of geospatial Web services. Often, instead of making design decisions based on these requirements, geospatial services are implemented using default, commonly used techniques which may reduce performance, increase complexity, or fail to fully meet user needs. This chapter discusses the importance of granularity in designing and implementing geospatial Web services and provides common examples that highlight the different approaches to granularity which are available.

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Instance Selection

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch161 ◽

2011 ◽

pp. 1041-1045

Author(s):

Huan Liu

Keyword(s):

Data Mining ◽

Data Storage ◽

Data Reduction ◽

Effective Means ◽

Digital Data ◽

Data Selection ◽

Instance Selection ◽

Redundant Data ◽

Performance Deterioration ◽

Digital Data Storage

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.

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Design and Implementation of Embedded Database Based on File System – The Design and Implementation of EDB

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.686.316 ◽

2014 ◽

Vol 686 ◽

pp. 316-320

Author(s):

Ye Zhao ◽

Guo Ying Lu ◽

Li Ying Wang

Keyword(s):

Data Storage ◽

Data Access ◽

Interface Layer ◽

Database System ◽

Limited Resources ◽

Design And Implementation ◽

Operating Level ◽

Actual Use ◽

Data Definition ◽

Database Engine

The architecture of the system is divided into some modules according to the operating level: interface layer, the database engine layer, and data access layer, database maintenance module and data storage module. This paper discusses in detail the data definition design and the important function of EDB and realizes the EDB management implementation details. The paper introduce in details the EDB system architecture, the design of physical storage structure and basic table structure. Through the actual use, the database system is suitable for embedded systems with limited resources, has good portability.

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