Big Data Visualization Tools

Bikakis, Nikos

doi:10.1007/978-3-319-63962-8_109-2

Cited by 3 publications

(1 citation statement)

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“…A visual representation of the numbers can provide the ideal basis for more easily identifying meaningful patterns and making decisions based on them [4]. Effective visualizations offer the possibility to explore the data to derive correlations and to gain spatio-temporal knowledge in planning processes [5]. However, enormous amounts of data often lead to confusion or incompleteness of important relationships.…”

Section: Introductionmentioning

confidence: 99%

Explanation and Analysis of Spatio-Temporal Correlations—Towards a Conceptual Approach of a Semantic Comparison Visualization in a Use Case of Carparks in Mainz, Germany

Rolwes,

Böhm

2023

IJGI

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Geospatial factors, because of their spatio-temporal correlations with demand-driven limited service, offer to improve urban planning decisions and expand the knowledge base in cities. Spatio-temporal analyses require an efficient and comprehensible visualization and explanation in order to analyze and understand geospatial relationships in urban areas. The aim of our research is to support domain experts in these analyses with user-oriented geovisualization. In this article, we propose concepts towards a semantic comparison visualization, which combines a visual analysis view and a visual explanation view. The visual analysis view is a knowledge-oriented view that focuses on analyzing resulting spatio-temporal correlations. The visual explanation view is an understanding-oriented view that focuses on explaining the underlying complex analysis process of geospatial factors and spatio-temporal correlations. We define general requirements for this aim and validate and evaluate our concepts related to these requirements. The results show the benefit of our concepts, but, at the same time, they also point to limitations and potential for optimization in further work.

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Section: Introductionmentioning

confidence: 99%

Explanation and Analysis of Spatio-Temporal Correlations—Towards a Conceptual Approach of a Semantic Comparison Visualization in a Use Case of Carparks in Mainz, Germany

Rolwes,

Böhm

2023

IJGI

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Visualization Analysis for 3D Big Data Modeling

Zhang

et al. 2015

Communications in Computer and Information Science

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Abstract. This paper describes an automatic system for 3D big data of face modeling using front and side view images taken by an ordinary digital camera, whose directions are orthogonal. The paper consists of four keys in 3D visualization. Firstly we study the 3D big data of face modeling including feature facial extraction from 2D images. The second part is to represent the technical from Computer Vision, Image Processing and my new method for extract information from images and create 3D model. Thirdly, 3D face modeling based on 2D image software is implemented by C# language, EMGU CV library and XNA framework. Finally, we design experiment, test and record results for measure performance of our method.Keywords: 3D big data face modeling, Mesh modeling, feature points extraction. IntroductionThere is an increasingly rich amount of 3D visual big data available from our daily life. Big Data in 3D computer model research become more and more important. 3D big data modeling is the process of developing a mathematical representation of any three-dimensional surface of an object via specialized software. The product is called a 3D model. It can be displayed as a two-dimensional image through a process called 3D rendering or used in a computer simulation of physical phenomena. A number of researchers have proposed to create face models from 2D images. Some approaches use two orthogonal views so that the 3D information of facial surface points can be measured [1], [2], [3] They require two cameras which must be carefully set up so that their directions are orthogonal. Zheng(1994)[4] developed a system to construct geometrical object models from image contours. The system requires a turn-table setup. Pighin et al.(2006)[5]developed a system to allow a user to manually specify correspondences across multiple images, and use computer vision techniques to compute 3D reconstructions of specified feature points. A 3D mesh model is then fitted to the reconstructed 3D points [6][7][8]. Mohamed D(2013) [8] of 3D mesh model with a manually intensive procedure, it was able to generate highly realistic face models.Because it is difficult to obtain a comprehensive and high quality 3D face database, other approaches have been proposed using the idea of "linear classes of face geometries". Kang and Jones(2002)[9]also use linear spaces of geometrical models to

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