A Hierarchical 3D Data Rendering System Synchronized with HTML

We propose a new rendering system for large-scale, 3D geometic data that can be used with web-based content man-agement systems (CMS). To achieve this, we employed a geometry hierarchical encoding method "QSplat" and implemented this in a Java and JOGL (Java bindings of OpenGL) environment. Users can view large-scale geometric data using conventional HTML browsers with a non-powerful CPU and low-speed networks. Further, this system is independent of the platforms. We add new functionalities so that users can easily understand the geometric data: Annotations and HTML Synchronization. Users can see the geometric data with the associated annotations that describe the names or the detailed explanations of the particular portions. The HTML Synchronization enables users to smoothly and interactively switch our rendering system and HTML contents. The experimental results show that our system performs an interactive frame rate even for a large-scale data whereas other systems cannot render them

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WebViz: A Web-Based Collaborative Interactive Visualization System for Large-Scale Data Sets

Lecture Notes in Earth System Sciences - GPU Solutions to Multi-scale Problems in Science and Engineering ◽

10.1007/978-3-642-16405-7_37 ◽

2013 ◽

pp. 587-606 ◽

Cited By ~ 2

Author(s):

Yichen Zhou ◽

Robin M. Weiss ◽

Elizabeth McArthur ◽

David Sanchez ◽

Xiang Yao ◽

...

Keyword(s):

Large Scale ◽

Interactive Visualization ◽

Data Sets ◽

Web Based ◽

Visualization System ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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PsicquicGraph, a BioJS component to visualize molecular interactions from PSICQUIC servers

F1000Research ◽

10.12688/f1000research.3-44.v1 ◽

2014 ◽

Vol 3 ◽

pp. 44 ◽

Cited By ~ 4

Author(s):

Jose M. Villaveces ◽

Rafael C. Jimenez ◽

Bianca H. Habermann

Keyword(s):

Small Molecules ◽

Molecular Interactions ◽

Large Scale ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Web Based ◽

High Throughput Data ◽

Link Type ◽

Large Scale Data ◽

Scale Data

Summary: Protein interaction networks have become an essential tool in large-scale data analysis, integration, and the visualization of high-throughput data in the context of complex cellular networks. Many individual databases are available that provide information on binary interactions of proteins and small molecules. Community efforts such as PSICQUIC aim to unify and standardize information emanating from these public databases. Here we introduce PsicquicGraph, an open-source, web-based visualization component for molecular interactions from PSIQUIC services. Availability: PsicquicGraph is freely available at the BioJS Registry for download and enhancement. Instructions on how to use the tool are available here http://goo.gl/kDaIgZ and the source code can be found at http://github.com/biojs/biojs and DOI:10.5281/zenodo.7709.

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Research on Image Storage Based on 3D Point Cloud

Big Data and Cloud Innovation ◽

10.18063/bdci.v1i1.510 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Ma Xiangning

Keyword(s):

Large Scale ◽

3D Model ◽

Triangular Mesh ◽

Data Sets ◽

Large Scale Data ◽

3D Data ◽

3D Data Processing ◽

Order Of Magnitude ◽

Scale Data ◽

Large Scale Data Sets

3D technology has been widely used in the fields of architecture, medical image, cultural relic protection, filmproduction and 3D game. The polygon mesh is the most commonly used method for expressing the 3D model, itconnects the points on the surface of the 3D model into polygons as a unit, which can express complex surfaces andprovide strong adaptability. Especially in the use of triangular mesh the most widely used. But the triangular griddisplay, simplifi ed, progressive transmission algorithm is not suitable for large-scale data sets. The triangular mesh datafi le described in this paper with the boundary ball data fi le conversion system will represent and gradually compressthese triangular meshes. This representation is not only compact, but also can be quickly calculated, its biggest featureis the high compression ratio, saving a lot of time and space, which makes it suitable for large-scale data sets. Itsexecution program can serve large-scale 3D data processing projects. This article has demonstrated that this system canbe converted to include a large order of magnitude polygon model.

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Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

10.1101/117812 ◽

2017 ◽

Cited By ~ 1

Author(s):

Julie A McMurry ◽

Nick Juty ◽

Niklas Blomberg ◽

Tony Burdett ◽

Tom Conlin ◽

...

Keyword(s):

Best Practices ◽

Large Scale ◽

Data Science ◽

Life Science ◽

Core Component ◽

Science Data ◽

Web Based ◽

Large Scale Data ◽

Online Databases ◽

Scale Data

AbstractIn many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

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Implementasi Sistem Crowdsourced Labelling Berbasis Web dengan Metode Weighted Majority Voting

Jurnal ULTIMA InfoSys ◽

10.31937/si.v6i2.223 ◽

2016 ◽

Vol 6 (2) ◽

pp. 76-82

Author(s):

Antonius Rachmat ◽

Yuan Lukito

Keyword(s):

Supervised Learning ◽

Large Scale ◽

Majority Voting ◽

Training Data ◽

Web Based ◽

Weighted Majority ◽

Large Scale Data ◽

Labelling Process ◽

Weighted Majority Voting ◽

Scale Data

Crowdsourced Labelling is a large scale data labelling process, solicits a large group of people to label the data, usually via Internet. This paper discusses about design and implementation of Web-based Crowdsourced Labelling. Supervised learning classification methods need labelled training data for its training phase. Unfortunately, in many cases, there aren’t any already available labelled training data. Large scale data labelling is a tedious and time consuming work. This research develops a web-based crowdsourced labelling which able to solicit a large group of people as data labeler to speed up the data labelling process. This system also allows multiple labeler for every data. The final label is calculated using Weighted Majority Voting method. We grabbed and used Facebook comments from the two candidates’ Facebook Page of 2014 Indonesian Presidential Election as testing data. Based on the testing conducted we can conclude that this system is able to handle all the labelling steps well and able to handle collision occurred when multiple labeler labelling a same data in the same time. The system successfully produces final label in CSV format, which can be processed further with many sentiment analysis tools or machine learning tools. Index Terms - Crowdsources labeling, web-based system, supervised learning, weighted majority voting.

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