Visual analysis of contagion in networks

Contagion is a process whereby the collapse of a node in a network leads to the collapse of neighboring nodes and thereby sets off a chain reaction in the network. It thus creates a special type of time-dependent network. Such processes are studied in various applications, for example, in financial network analysis, infection diffusion prediction, supply-chain management, or gene regulation. Visual analytics methods can help analysts examine contagion effects. For this purpose, network visualizations need to be complemented with specific features to illustrate the contagion process. Moreover, new visual analysis techniques for comparison of contagion need to be developed. In this paper, we propose a system geared to the visual analysis of contagion. It includes the simulation of contagion effects as well as their visual exploration. We present new tools able to compare the evolution of the different contagion processes. In this way, propagation of disturbances can be effectively analyzed. We focus on financial networks; however, our system can be applied to other use cases as well.

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Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

BMC Genomics ◽

10.1186/s12864-021-07791-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ratanond Koonchanok ◽

Swapna Vidhur Daulatabad ◽

Quoseena Mir ◽

Khairi Reda ◽

Sarath Chandra Janga

Keyword(s):

Single Molecule ◽

Visual Analytics ◽

Visual Analysis ◽

Direct Sequencing ◽

Visual Exploration ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Rna Sequences ◽

Sequencing Technologies ◽

Signal Features

Abstract Background Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia.

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TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

Information Visualization ◽

10.1177/1473871619858937 ◽

2019 ◽

Vol 19 (1) ◽

pp. 3-23

Author(s):

Aurea Soriano-Vargas ◽

Bernd Hamann ◽

Maria Cristina F de Oliveira

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Multivariate Data ◽

Visual Exploration ◽

Data Sets ◽

Time Varying ◽

Domain Experts ◽

Data Mining Algorithms ◽

Temporal Relationships ◽

Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

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A Visual Analysis Approach to Understand and Explore Quality Problems of AIS Data

Journal of Marine Science and Engineering ◽

10.3390/jmse9020198 ◽

2021 ◽

Vol 9 (2) ◽

pp. 198

Author(s):

Wei He ◽

Jinyu Lei ◽

Xiumin Chu ◽

Shuo Xie ◽

Cheng Zhong ◽

...

Keyword(s):

Data Quality ◽

Visual Analytics ◽

Visual Analysis ◽

Visual Exploration ◽

Quality Analysis ◽

Automatic Identification ◽

Identification System ◽

Interaction Method ◽

Related Quality ◽

Ship Behavior

Low quality automatic identification system (AIS) data often mislead analysts to a misunderstanding of ship behavior analysis and to making incorrect navigation risk assessments. It is therefore necessary to accurately understand and judge the quality problems in AIS data before a further analysis of ship behavior. Outliers were filtered in the existing methods of AIS quality analysis based only on mathematical models where AIS data related quality problems are not utilized and there is a lack of visual exploration. Thus, the human brain’s ability cannot be fully utilized to think visually and for reasoning. In this regard, a visual analytics (VA) approach called AIS Data Quality visualization (ADQvis) was designed and implemented here to support evaluations and explorations of AIS data quality. The system interface is overviewed and then the visualization model and corresponding human-computer interaction method are described in detail. Finally, case studies were carried out to demonstrate the effectiveness of our visual analytics approach for AIS quality problems.

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Visual Analysis of Biomarkers Reveals Differences in Lipid Profiles and Liver Enzymes before and after Gastric Sleeve and Bypass

Obesity Facts ◽

10.1159/000510401 ◽

2021 ◽

pp. 1-11

Author(s):

Marijn Marthe Georgine van Berckel ◽

Saskia L.M. van Loon ◽

Arjen-Kars Boer ◽

Volkher Scharnhorst ◽

Simon W. Nienhuijs

Keyword(s):

Bariatric Surgery ◽

Visual Analytics ◽

Biochemical Markers ◽

Visual Analysis ◽

Large Population ◽

High Volume ◽

Cholesterol Concentration ◽

Health State ◽

Data Set ◽

Before And After

Introduction: Bariatric surgery results in both intentional and unintentional metabolic changes. In a high-volume bariatric center, extensive laboratory panels are used to monitor these changes pre- and postoperatively. Consecutive measurements of relevant biochemical markers allow exploration of the health state of bariatric patients and comparison of different patient groups. Objective: The objective of this study is to compare biomarker distributions over time between 2 common bariatric procedures, i.e., sleeve gastrectomy (SG) and gastric bypass (RYGB), using visual analytics. Methods: Both pre- and postsurgical (6, 12, and 24 months) data of all patients who underwent primary bariatric surgery were collected retrospectively. The distribution and evolution of different biochemical markers were compared before and after surgery using asymmetric beanplots in order to evaluate the effect of primary SG and RYGB. A beanplot is an alternative to the boxplot that allows an easy and thorough visual comparison of univariate data. Results: In total, 1,237 patients (659 SG and 578 RYGB) were included. The sleeve and bypass groups were comparable in terms of age and the prevalence of comorbidities. The mean presurgical BMI and the percentage of males were higher in the sleeve group. The effect of surgery on lowering of glycated hemoglobin was similar for both surgery types. After RYGB surgery, the decrease in the cholesterol concentration was larger than after SG. The enzymatic activity of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphate in sleeve patients was higher presurgically but lower postsurgically compared to bypass values. Conclusions: Beanplots allow intuitive visualization of population distributions. Analysis of this large population-based data set using beanplots suggests comparable efficacies of both types of surgery in reducing diabetes. RYGB surgery reduced dyslipidemia more effectively than SG. The trend toward a larger decrease in liver enzyme activities following SG is a subject for further investigation.

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Fraud Audit Based on Visual Analysis: A Process Mining Approach

Applied Sciences ◽

10.3390/app11114751 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4751

Author(s):

Jorge-Félix Rodríguez-Quintero ◽

Alexander Sánchez-Díaz ◽

Leonel Iriarte-Navarro ◽

Alejandro Maté ◽

Manuel Marco-Such ◽

...

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Process Mining ◽

Fraud Detection ◽

Financial Sector ◽

Contextual Analysis ◽

Use Case ◽

Hotel Management ◽

Domain Iii ◽

Audit Approach

Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.

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Implementing Visual Analytics Pipelines with Simulation Data

10.5772/intechopen.96152 ◽

2021 ◽

Author(s):

Taimur Khan ◽

Syed Samad Shakeel ◽

Afzal Gul ◽

Hamza Masud ◽

Achim Ebert

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Simulated Data ◽

Evaluation Study ◽

Ease Of Use ◽

Preliminary Evaluation ◽

Simulation Data ◽

Visual Data Analytics ◽

High Level ◽

Simulation Parameters

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.

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VESPER: Visual Exploration of Similarity and Performance Metrics for Computer-Aided Design Repositories

Volume 3: Manufacturing Equipment and Systems ◽

10.1115/msec2018-6527 ◽

2018 ◽

Cited By ~ 1

Author(s):

Devarajan Ramanujan ◽

William Z. Bernstein

Keyword(s):

Visual Analytics ◽

Computer Aided Design ◽

Performance Metrics ◽

External Validation ◽

Visual Exploration ◽

Similarity Metrics ◽

Report Generation ◽

Computer Aided ◽

And Performance ◽

Aided Design

VESPER (Visual Exploration of Similarity and PERformance) is a visual analytics system for exploring similarity metrics and performance metrics derived from computer-aided design (CAD) repositories. It consists of (1) a data processing module that allows analysts to input custom similarity metrics and performance metrics, (2) a visualization module that facilitates navigation of the design spaces through coordinated, interactive visualizations, and (3) a report generation module that allows analysts to export lifecycle data of selected repository items as well as the input metrics for further external validation. In this paper, we discuss the need, design rationale, and implementation details for VESPER. We then apply VESPER to (1) sustainability-focused exploration of parts, and (2) exploration of tool wear and surface roughness in machined parts.

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Visual exploration of classification models for various data types in risk assessment

Information Visualization ◽

10.1177/1473871611433715 ◽

2012 ◽

Vol 11 (3) ◽

pp. 237-251 ◽

Cited By ~ 6

Author(s):

Malgorzata Migut ◽

Marcel Worring

Keyword(s):

Risk Assessment ◽

Visual Analytics ◽

Ordinal Data ◽

Visual Exploration ◽

Classification Model ◽

Classification Models ◽

Data Types ◽

Suggested Approach ◽

Wrong Decision ◽

Data Elements

In risk assessment applications well-informed decisions need to be made based on large amounts of multi-dimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier’s performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.

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Visualization and recommendation of large image collections toward effective sensemaking

Information Visualization ◽

10.1177/1473871616630778 ◽

2016 ◽

Vol 16 (1) ◽

pp. 21-47 ◽

Cited By ~ 5

Author(s):

Yi Gu ◽

Chaoli Wang ◽

Jun Ma ◽

Robert J Nemiroff ◽

David L Kao ◽

...

Keyword(s):

Visual Analytics ◽

Graphics Processing Units ◽

User Study ◽

Detailed Comparison ◽

Visual Navigation ◽

Visual Exploration ◽

Graph Representation ◽

Daily Lives ◽

Central Processing ◽

Group Memberships

In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

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Visual Data Mining of Multimedia Data for Social and Behavioral Studies

Information Visualization ◽

10.1057/ivs.2008.32 ◽

2009 ◽

Vol 8 (1) ◽

pp. 56-70 ◽

Cited By ~ 13

Author(s):

Chen Yu ◽

Yiwen Zhong ◽

Thomas Smith ◽

Ikhyun Park ◽

Weixia Huang

Keyword(s):

Data Mining ◽

Visual Analysis ◽

Hybrid Approach ◽

Heterogeneous Data ◽

Multimedia Data ◽

Visual Exploration ◽

Visual Data Mining ◽

Flexible Tool ◽

Visualization System ◽

Behavioral Studies

With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.

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