Special Issue "Information Visualization for Massive Data"

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 August 2016)

Special Issue Editor

Guest Editor
Dr. Olga Kurasova

Vilnius University, Universiteto St. 3, Lithuania
Website | E-Mail
Interests: data mining; big data; machine learning; multidimensional data visualization; dimensionality reduction; artificial neural networks; multiple objective optimization; evolutionary algorithms; decision support system

Special Issue Information

Dear Colleagues,

This Special Issue of the Informatics journal welcomes submissions on the topic of information visualization which is a highly relevant issue in various fields. Data in technologies and sciences are usually high-dimensional, where objects are described by some features. However, it is difficult to understand these data without additional approaches. A visual insight into the data assists in comprehending the information. Visual mining involves visualization and graphical presentation of information. Visualization aims to provide data in a visual form that would facilitate better understanding, providing insight to the information, and directly influencing further decision making. The advantage of visual analysis is that it is much easier to detect or extract some useful information from the graphical representation of data than from raw numbers. Moreover, a problem arises when massive data are visualized. One is confronted not only with time- and computer resource-consuming problems, but also with effective ways of creating a graphical representation of a huge amount of data. All of this requires intensive scientific research. We encourage authors to submit their original research articles, work in progress, surveys, reviews, and viewpoint articles in this field. This Special Issue welcomes applications, theories, models, and frameworks that are concerned with (but not limited to) the following topics related to information visualization:

  • Dimensionality reduction based visualization
  • Graphical representation of massive data
  • Optimization of projection error
  • Estimating quality of information visualization
  • Big data visualization
  • Techniques of information vizualization
  • Development of software for information visualization
  • Application of information vizualization in various fields

Prof. Dr. Olga Kurasova
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Information visualization
  • Dimensionality reduction
  • Projection error
  • Quality of visualization
  • Big data
  • Massive data

Published Papers (2 papers)

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Research

Open AccessArticle Interactive Graph Layout of a Million Nodes
Informatics 2016, 3(4), 23; doi:10.3390/informatics3040023
Received: 30 August 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 20 December 2016
PDF Full-text (3208 KB) | HTML Full-text | XML Full-text
Abstract
Sensemaking of large graphs, specifically those with millions of nodes, is a crucial task in many fields. Automatic graph layout algorithms, augmented with real-time human-in-the-loop interaction, can potentially support sensemaking of large graphs. However, designing interactive algorithms to achieve this is challenging. In
[...] Read more.
Sensemaking of large graphs, specifically those with millions of nodes, is a crucial task in many fields. Automatic graph layout algorithms, augmented with real-time human-in-the-loop interaction, can potentially support sensemaking of large graphs. However, designing interactive algorithms to achieve this is challenging. In this paper, we tackle the scalability problem of interactive layout of large graphs, and contribute a new GPU-based force-directed layout algorithm that exploits graph topology. This algorithm can interactively layout graphs with millions of nodes, and support real-time interaction to explore alternative graph layouts. Users can directly manipulate the layout of vertices in a force-directed fashion. The complexity of traditional repulsive force computation is reduced by approximating calculations based on the hierarchical structure of multi-level clustered graphs. We evaluate the algorithm performance, and demonstrate human-in-the-loop layout in two sensemaking case studies. Moreover, we summarize lessons learned for designing interactive large graph layout algorithms on the GPU. Full article
(This article belongs to the Special Issue Information Visualization for Massive Data)
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Open AccessArticle AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets
Informatics 2016, 3(4), 18; doi:10.3390/informatics3040018
Received: 31 August 2016 / Revised: 27 September 2016 / Accepted: 28 September 2016 / Published: 7 October 2016
Cited by 2 | PDF Full-text (2138 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the Animated VISualization Tool (AVIST), an exploration-oriented data visualization tool that enables rapidly exploring and filtering large time series multidimensional datasets. AVIST highlights interactive data exploration by revealing fine data details. This is achieved through the use of animation and
[...] Read more.
This paper presents the Animated VISualization Tool (AVIST), an exploration-oriented data visualization tool that enables rapidly exploring and filtering large time series multidimensional datasets. AVIST highlights interactive data exploration by revealing fine data details. This is achieved through the use of animation and cross-filtering interactions. To support interactive exploration of big data, AVIST features a GPU (Graphics Processing Unit)-centric design. Two key aspects are emphasized on the GPU-centric design: (1) both data management and computation are implemented on the GPU to leverage its parallel computing capability and fast memory bandwidth; (2) a GPU-based directed acyclic graph is proposed to characterize data transformations triggered by users’ demands. Moreover, we implement AVIST based on the Model-View-Controller (MVC) architecture. In the implementation, we consider two aspects: (1) user interaction is highlighted to slice big data into small data; and (2) data transformation is based on parallel computing. Two case studies demonstrate how AVIST can help analysts identify abnormal behaviors and infer new hypotheses by exploring big datasets. Finally, we summarize lessons learned about GPU-based solutions in interactive information visualization with big data. Full article
(This article belongs to the Special Issue Information Visualization for Massive Data)
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