Visual Analytics Tool Research Articles

Assessing the effect of sample orientation on dimensional X-ray computed tomography through experimental and simulated data

This paper evaluates the dimensional accuracy of industrial X-ray computed tomography (CT) measurements in function of the sample’s orientation within the measurement volume. Experimental measurements are contrasted against predictions from simulations that, based on Radon-space analysis and computation of X-ray traversal-path lengths through ray-casting, estimate the optimal orientation of samples that would minimize both cone-beam distortions and beam hardening artifacts in CT data reconstructions. The simulated data were obtained with the Dreamcaster, a tool for processing and visual analysis of industrial CT datasets. For the experimental data, the tilt angles from the CT rotation axis that resulted in the smallest deviations between dimensional CT data and reference measurements—obtained from contact coordinate measuring machines (CMMs)—were determined for each sample. Tilt angles that resulted in large dimensional measurement errors were also determined. In most of the cases, the data observed in the experimental measurements agree with the trends predicted by simulated data. For slender workpieces (with high aspect ratios), three basic zones of axial angular tilt were identified: a ‘green zone’ from 10 to 35° in which deviations between CT and CMM are minimum, a ‘red zone’ from 0 to 5° and from 65 to 90° in which the largest deviations are measured, and ‘yellow zones’ between the two. When optimal orientations from the ‘green zone’ are used for scanning, the dimensional deviations of CT data from reference measurements are under 10 µm.

An Instance Space Analysis of Regression Problems

The quest for greater insights into algorithm strengths and weaknesses, as revealed when studying algorithm performance on large collections of test problems, is supported by interactive visual analytics tools. A recent advance is Instance Space Analysis, which presents a visualization of the space occupied by the test datasets, and the performance of algorithms across the instance space. The strengths and weaknesses of algorithms can be visually assessed, and the adequacy of the test datasets can be scrutinized through visual analytics. This article presents the first Instance Space Analysis of regression problems in Machine Learning, considering the performance of 14 popular algorithms on 4,855 test datasets from a variety of sources. The two-dimensional instance space is defined by measurable characteristics of regression problems, selected from over 26 candidate features. It enables the similarities and differences between test instances to be visualized, along with the predictive performance of regression algorithms across the entire instance space. The purpose of creating this framework for visual analysis of an instance space is twofold: one may assess the capability and suitability of various regression techniques; meanwhile the bias, diversity, and level of difficulty of the regression problems popularly used by the community can be visually revealed. This article shows the applicability of the created regression instance space to provide insights into the strengths and weaknesses of regression algorithms, and the opportunities to diversify the benchmark test instances to support greater insights.

Visual analytics of COVID-19 dissemination in São Paulo state, Brazil

Visual analytics techniques are useful tools to support decision-making and cope with increasing data, particularly to monitor natural or artificial phenomena. When monitoring disease progression, visual analytics approaches help decision-makers to understand or even prevent dissemination paths. In this paper, we propose a new visual analytics tool for monitoring COVID-19 dissemination. We use k-nearest neighbors of cities to mimic neighboring cities and analyze COVID-19 dissemination based on comparing a city under consideration and its neighborhood. Moreover, such analysis is performed within periods, which facilitates the assessment of isolation policies. We validate our tool by analyzing the progression of COVID-19 in neighboring cities of São Paulo state, Brazil.

Probiotic Potential and Genomic Evaluation of Lactic Acid Bacteria Isolated from Fermented Sorghum-Based Gruel

Forty eight isolates were randomly isolated from fermented red sorghum (Sorghum bicolor) and white sorghum-based (Sorghum vulgare) gruel and evaluated for their probiotic abilities. Forty five out of the forty eight isolates were Gram-positive, catalase negative and non-sporulating, which were further evaluated. Twenty out of the forty five isolates showed tolerance to acid with their ability to survive extreme pH 2.0, 3.0 and 4.0 respectively at different levels. These were further screened for their in vitro inhibition ability against selected test pathogens (Escherichia coli, Salmonella Typhimurium, Bacillus sp., Pseudomonas aeruginosa, and Staphylococcus aureus). Fourteen out of the twenty isolates had antimicrobial activity against the test pathogens at different levels. They were further screened for their antibiotic susceptibility pattern, all the fourteen isolates showed susceptibility to different antibiotics at different levels. These isolates were screened for their bile tolerance ability, all the fourteen isolates showed tolerance to the bile salt concentration (0.3%, 0.5% and 1%) at different levels respectively. The fourteen isolates were also screened for their gastric transit tolerance ability; all the fourteen isolates were able to tolerate gastric transit at 0 minute, 90 minutes and 180 minutes respectively. They were further screened for their in vitro hydrophobicity ability, only six out of the fourteen isolates showed percentage hydrophobicity of 40% and above. These six isolates were identified at the sub-species level using 16S RRNA Sequencing for isolates identification and construction of phylogeny. They were identified as Lactobacillus plantarum strain ST1, Lactobacillus brevis strain SC4, Lactobacillus casei strain 0108, Lactobacillus plantarum strain HASOB9a, Pediococcus pentosaceus strain SH 740 and Lactobacillus plantarum ZJ316 respectively. Out of the six identified isolates, the complete genome of Lactobacillus plantarum ZJ316 had been sequenced and was retrieved alongside with the complete genome data of four other species of Lactobacillus plantarum of industrial importance available in public domain, which was used for the mini comparative genome analysis in this research, using bioinformatics and visual analytics tools (Tableau Desktop). It was revealed that the genome size of Lactobacillus plantarum ZJ316 is second to that of Lactobacillus plantarum 16 and higher than that of the others whose genome had been completely sequenced.

Beyond frame analysis: Formal analysis and genre typology in the communication study of short-form environmental video messaging

As connected platforms such as Twitter, Instagram and TikTok rise in popular use, communication strategies are forced to grow more condensed and to be transmitted primarily across digital screens. Online short-form video has consequently become a primary format for environmental communication, though research on digital communication remains largely limited to frame analysis and discussions of print and still images. Filling a need in current scholarship on environmental media and aiming to further bridge humanities and communication research, this article offers a model for assessing environmentally themed short-form videos that complements communication studies’ focus on messaging rhetoric with humanities-based film and visual culture analytic tools for assessing the connotative aspect of aesthetic and narrative elements. Intending to offer an applicable framework for environmental messengers, we develop a genre system of short-form videos along cognitive and emotional axes that can be quantitatively identified according to formal practices, demonstrated through an examination of four environmental short-form videos.

CrimAnalyzer: Understanding Crime Patterns in São Paulo.

São Paulo is the largest city in South America, with crime rates that reflect its size. The number and type of crimes vary considerably around the city, assuming different patterns depending on urban and social characteristics of each particular location. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban routine activities. Therefore, those studies and tools are more global in the sense that they are not designed to investigate specific regions of the city such as particular neighborhoods, avenues, or public areas. Tools able to explore specific locations of the city are essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passersby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. In this paper, we present CrimAnalyzer, a visual analytic tool that allows users to study the behavior of crimes in specific regions of a city. The system allows users to identify local hotspots and the pattern of crimes associated to them, while still showing how hotspots and corresponding crime patterns change over time. CrimAnalyzer has been developed from the needs of a team of experts in criminology and deals with three major challenges: i) flexibility to explore local regions and understand their crime patterns, ii) identification of spatial crime hotspots that might not be the most prevalent ones in terms of the number of crimes but that are important enough to be investigated, and iii) understand the dynamic of crime patterns over time. The effectiveness and usefulness of the proposed system are demonstrated by qualitative and quantitative comparisons as well as by case studies run by domain experts involving real data. The experiments show the capability of CrimAnalyzer in identifying crime-related phenomena.

Visual analytic based ship collision probability modeling for ship navigation safety

This study presents a tangible visual analytic tool to analyse maritime traffic in spatio-temporal basis using AIS data. This novel approach helps in understanding the macroscopic safety structure of both fairways and individual ships with evidences in microscopic level. Qualification of our system is demonstrated with 7-days AIS trajectory collected from Mexican Gulf. We find out that spatio-temporal position pattern of encountered ships in Port Houston varies over time, significantly. In addition, the spatial distribution of ship accidents coincide with proposed near-miss density areas. Furthermore, proposed tool is capable of capturing real accident cases. Field experiments with domain experts have demonstrated that our approach helps in making realistic inferences about navigational safety behaviour of both individual vessel and water area.

An analysis of the status quo of ground-level ozone pollution research

Objective: To analyze the research status of ground-level ozone pollution, explore research trends and hot spots, and provide references for future research on air pollution. Methods: Papers on ground-level ozone pollution research published before December 31, 2019 had been retrieved in SCI-E database of the "Web of Science Core Collection" in January 2020. The retrieval strategies were set as follows: TS= ( ("Tropospheric Ozone" OR "Low Level Ozone" OR "Ground Level Ozone") AND ("Air pollution*" OR "Air quality") ) . The survey included 2084 articles. By using bibliometric research and visual analysis tools, the research status of global ground-level ozone pollution was revealed from the aspects of time, discipline, journal, financing, institution, country and key words. Results: Cumulative publications increased in a cubic function of y=0.05x(3)+0.80x(2)+0.74x+4.55 (R(2)=0.999, P<0.01) . The most studied subject was Environmental sciences ecology (1401 articles, 67.23%) . Atmospheric Environment was the journal with the most articles (332 articles, 15.93%) . The United States was the country with the most publications (44.67%, 931/2084) , while China ranked second (17.13%, 357/2084) . 80.39% (287/357) of Chinese papers had funding information. Among the top 10 research institutions, 7 and 2 were affiliated to the United States and China respectively. Source apportionment and human health were high frequency keywords that had appeared in the last 5 years. Conclusion: The research on ground-level ozone pollution is in a good period of development. The United States has a leading position in this area, and China has a good prospect in this field. Pollution source apportionment and human health effects are new research directions.

Improving the Visual Interactive Analysis Method for Automation and Control of the Decision-Making Process in Multi-Criteria Design of Complex Mechanical Systems

For multi-criteria design problems of complex mechanical systems with a large number of control parameters, technical constraints, and quality criteria, the search for Pareto solution domain takes quite a lot of time varying from hours to days. In fact, the decision-maker (DM) desires to examine a small number of reasonable Pareto optimal solutions in order to understand the problem itself and control the decisionmaking in a simple manner. This paper presents the improvement of a visual interaction analysis method or VIAM developed by the authors with the aim of providing a tool for DM to define the optimal and mutually-agreed solutions in the multi-criteria decision making (MCDM). Indeed, VIAM allows for evaluating the distribution domain of the Pareto optimal solutions defined by the genetic algorithm, which supports the DM to set additional thresholds for the objectives to filter the desired solutions and suggest to shrink or expand the threshold to control the search. In case of mutually-agreed solution non-existence, VIAM allows for providing instruction to reestablish the multi-objective problem that new Pareto solution domains can be found as desired by the DM. Based on VIAM, a visual interaction analysis tool or VIAT was developed by means of Matlab. VIAT was then used for the multi-criteria design of slider-crank mechanism for an innovative fruit vegetable washer with three objectives. Comparative study on the obtained results from VIAT with the existing design option and the obtained solution from the traditional method "concession by priority" has shown the effectiveness of the method proposed in this paper. VIAT is actually a very user-friendly tool that makes the multi-criteria design more practical especially for the mechanical system.

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes.

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time. Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models. In this paper, we present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks. Based on a comprehensive requirement and task analysis, we employ descriptive visualization with performance measures and detailed inspections of model behaviors from the statistical, instance, feature, and model structure levels. We demonstrate our framework through two case studies on image classification by fine-tuning AlexNets to illustrate how analysts can utilize our framework.

HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models.

In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a "concept" or "feature" may benefit or hinder an ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing results are first transformed to analytical results using statistical and logical inferences, and then to a visual representation for rapid observation of the conclusions and the logical flow between the testing results and hypotheses. We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.

A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling.

An important approach for scientific inquiry across many disciplines involves using observational time series data to understand the relationships between key variables to gain mechanistic insights into the underlying rules that govern the given system. In real systems, such as those found in ecology, the relationships between time series variables are generally not static; instead, these relationships are dynamical and change in a nonlinear or state-dependent manner. To further understand such systems, we investigate integrating methods that appropriately characterize these dynamics (i.e., methods that measure interactions as they change with time-varying system states) with visualization techniques that can help analyze the behavior of the system. Here, we focus on empirical dynamic modeling (EDM) as a state-of-the-art method that specifically identifies causal variables and measures changing state-dependent relationships between time series variables. Instead of using approaches centered on parametric equations, EDM is an equation-free approach that studies systems based on their dynamic attractors. We propose a visual analytics system to support the identification and mechanistic interpretation of system states using an EDM-constructed dynamic graph. This work, as detailed in four analysis tasks and demonstrated with a GUI, provides a novel synthesis of EDM and visualization techniques such as brush-link visualization and visual summarization to interpret dynamic graphs representing ecosystem dynamics. We applied our proposed system to ecological simulation data and real data from a marine mesocosm study as two key use cases. Our case studies show that our visual analytics tools support the identification and interpretation of the system state by the user, and enable us to discover both confirmatory and new findings in ecosystem dynamics. Overall, we demonstrated that our system can facilitate an understanding of how systems function beyond the intuitive analysis of high-dimensional information based on specific domain knowledge.

Green Packaging from Consumer and Business Perspectives

Sustainable development is a global objective that aims to address the societal challenge of climate action, the environment, resource efficiency, and raw materials. In this sense, an important strategy is the promotion of green packaging, that is, the use of sustainable materials and designs for the packaging of goods. In recent years, many research works have been published in the specialised area covering the different perspectives and dimensions of green packaging. However, to our knowledge, no previous investigations have analysed the research activity on green packaging from business and consumer perspectives. The present study intends to fill this gap by analysing all of the publications found in the Scopus database with the help of visual analytic tools, including word clouds and Gephi network visualization software. More specifically, our study analyses the impact of green packaging from business and consumer viewpoints, including some specific issues such as the design and materials used in green packaging, green packaging costs, marketing strategies and corporate social responsibility related to green packaging, and the impact of green packaging in waste management, the circular economy, logistics, and supply chain management. The results obtained reveal the growing interest of scholars and researchers in all of these dimensions, as is made patently clear by the increasing number of journal publications in recent years. The practical implications of this study are significant, given the growing awareness among companies and consumers about the importance of the promotion of sustainable development through green packaging alternatives. More specifically, the results of this research could be very useful for all of those agents who are interested in learning about the main lines of research being developed in the field of green packaging.

Novel use of coactivators to enhance sensitivity of SEAP-based reporter assay system for visual monitoring and quantitation of androgens and antiandrogens in water

Every year thousands of chemicals get discharged into the waterbodies of the world. These chemicals cause endocrine disruption and induce adverse health effects in human and aquatic life. Global environmental protection agencies emphasise the need to develop rapid and specific tests for identification of these endocrine disruptive chemicals (EDCs) in water. Detection of chemicals that disrupt androgen signaling is especially important because androgen input at specific phases of life is critical for proper male development. Effect-based methods such as reporter assays are suitable tools for identification of EDCs in mixtures of unknown composition.The current study describes a stable, secreted alkaline protease (SEAP)-based reporter assay system, for visual detection of androgenic/antiandrogenic activity present in water samples. A novel feature of this system is the inclusion of coactivators, GRIP1, CARM1, p300 and mZac1b, in addition to an optimal combination of androgen response element (3× HRE), androgen receptor (AR) and the SEAP reporter gene. Incorporation of the coactivators resulted in a transcriptional fold change of 162 folds, enabling visual detection at much lower concentrations of androgen (1 picomolar) within 1 h of addition of test sample. Also, non-androgenic steroids such as estrogen, progesterone and Dexamethasone did not induce significant reporter activity, except at very high concentrations. This reporter assay can be readily converted into a high throughput format for investigation in multiple samples simultaneously, and reflects the changes that can be expected to occur inside a mammalian cell. The androgenic activity in six different water sources was evaluated using this assay. The results reveal significant androgenic activity in rivers and lakes close to Industrial areas, whereas the highest androgenic activity was observed in water containing paper and pulp mill effluents. This bioassay therefore provides a rapid, visual detection tool for effect-directed analysis of androgenic/antiandrogenic compounds in water. Impact statementThe current SEAP-based assay allows visual detection of androgens/antiandrogens in water, at concentrations as low as 1 picomolar, within a 1 h time period, in a high throughput format, providing a very useful technique for field users and regulatory bodies.

A Case Study- Visual Analysis of Sales Records Using TABLEAU

In this decade the new ecosystem of needs skills and tools for self-service analysis is developed for various business domains. So the need of maintaining the data records and performing data analysis tasks on it for further decisions to improvise business process operations. There is a need to gather various sales records that make it easy to measure monthly sales performance by product segment and by product category. The data gathered is stored with the help of an excel sheet which is a fast and reliable source to maintain sales records. BI analysts can use a wide variety of business intelligence BI tools such one is Tableau which gives them more flexibility in representing the results. Here as sales records are vast information to be stored and it needs to be formatted in some order. Tableau a visual analysis tool helps to measure sales targets from the whole dataset easily and gives the results in the form of desktop view very fast. These visual views can be used further to achieve the best sales targets in the future.

Analyzing the quality of local and global multidimensional projections using performance evaluation planning

Among the challenges of the big data era, the analysis of high-dimensional data is still an open research area. As a result, several multidimensional projection techniques have been developed to reduce data dimensionality, becoming important visualization and visual analytics tools. In order to ensure the quality of projections, it is necessary to assess the low-dimensional embeddings by using different dataset configurations as input and analyzing evaluation metrics. However, it is not clear to the user how factors such as the number of dimensions, instances, or clusters, can affect the projection mapping and its quality regarding different projection techniques and assessment metrics. The research reported in this paper aims to clarify how much these factors affect each response variable via performance evaluation planning. We present an evaluation approach, supported by factorial design, that carries out a complete analysis, in the sense of measuring all possible combinations of all the input factors. The results of the analyses of local and global structure preservation in the projections yield a better understanding of how distinct dataset properties can influence the choice of projections based on quality metrics results.

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data.

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

Visual Analysis of International Green Logistics Research in the Past Ten Years: Research Hotspots and Evolutionary Trends

In logistics-related research, green logistics is the top priority of modern circulation reform. This article describes the research hotspots and evolutionary trends of green logistics in the past ten years by analyzing relevant literature. This paper uses visual analysis tools (such as VosViewer, CiteSpace, etc.) to analyze the knowledge graph of 304 papers. This article analyzes the co- occurrence of keywords, article citations, timeline graphs, and emergent keywords. The results show that related research on green logistics has gradually penetrated management practices and technical applications. Generally speaking, these findings help describe the latest trend of green logistics, which concentrate on supply chain management, low-carbon logistics, and other hotspots.

The Role of a Facilitator in Co-Design Applications for Exploratory Analysis in Domains of High Complexity: The Case of MAHiCGO

Background: Robust and useful tools for exploratory analysis in biosciences are still lagging behind the size and complexity of the biological datasets produced since the completion of the human genome in 2000. A possible reason is that developers are unlikely to understand domain and case-specific requirements of existing research questions. Methods: We formed a design team comprising a visualization expert, a Human Computer Interaction (HCI) researcher, bioinformatics domain experts, and the Principal Investigator (PI) as a `facilitator' filling the communication gap between them. We implemented co-design methodology. Results: We identified the need for an interactive visual analytic tool for exploratory analysis of biological data. We describe the process of developing MAHiCGO, a novel tool for the simultaneous visualization of MA Gene Expression data, Hi-C data and Gene Ontology (GO) information in an interactive manner for the exploratory analysis of biological data. Conclusion: The key finding of this research to include a facilitator role in the co-design is useful in the evolving fields of design research and of bioinformatics, merging computational sciences with biosciences. The findings support in understanding new functional roles in the field of design, in particular design of computer applications in highly complex domains such as bioinformatics, and for highly complex tasks such as exploratory tasks. The findings also stress on the key role of visualization to expand user cognitive capabilities, and of co-design for constant engagement of domain experts in the creation process.

Big Data Research Infrastructure Collaboration Toward the SKA (BRICSKA)

Abstract Astronomy is entering an era of mega-data that will render conventional research methods as well as data and visual analytics tools ineffective. The Square Kilometre Array (SKA) drives one of the most significant big data challenges of the next decades. South Africa, China and India are partners in the global SKA collaboration and host recently completed, next generation radio astronomy facilities. South Africa, Brazil, China and India are involved in the Large Synoptic Survey Telescope (LSST), which represents a complementary mega-data challenge, vastly increasing the current data volume of optical surveys, and providing critical multi-wavelength data set for SKA analytics. Russian researchers are also engaged in radio astronomy and multi-wavelength, multi-messenger projects driving increasing volumes of observational data. This project brings together teams leading programs in data innovation in each partner country to collaborate on the development of new technologies and systems to meet the big data challenge of SKA pathfinder facilities and the multi-wavelength projects that are critical to the advance of astronomy. In so doing we will prototype and demonstrate scalable big data technologies for the new big data era, and establish a BRICS multinational federated data intensive cloud network for collaborative programs in data intensive astronomy.