Visual Analytics Tool Research Articles

Applying visual analytics to fraud detection using Benford's law

AbstractBenford's law has been examined as a useful tool for detecting potential accounting fraud. In this article, I provide an introduction to Benford's law and examine how the first digit, second digit, and first‐two digits tests in Benford's law can be employed to detect potential accounting fraud. In addition, I also highlight, through a worked example, how Tableau, a visual analytics tool, can be used to perform Benford's law's first‐digit test to detect potential fraud.

A Data-Driven Introduction to Authors, Readings, and Techniques in Visualization for the Digital Humanities.

The newly rediscovered frontier between data visualization and the digital humanities has proven to be an exciting field of experimentation for scholars from both disciplines. This fruitful collaboration is attracting researchers from other areas of science who may be willing to create visual analysis tools that promote humanities research in its many forms. However, as the collaboration grows in complexity, it may become intimidating for these scholars to get engaged in the discipline. To facilitate this task, we have built an introduction to visualization for the digital humanities that sits on a data-driven stance adopted by the authors. In order to construct a dataset representative of the discipline, we analyze citations from a core corpus on 300 publications in visualization for the humanities obtained from recent editions of the InfoVis Vis4DH workshop, the ADHO Digital Humanities Conference, and the specialized digital humanities journal Digital Humanities Quarterly. From here, we extract referenced works and analyze more than 1900 publications in search of citation patterns, prominent authors in the field, and other interesting insights. Finally, following the path set by other researchers in the visualization and Human-Computer Interaction (HCI) communities, we analyze paper keywords to identify significant themes and research opportunities in the field.

PeckVis: A Visual Analytics Tool to Analyze Dominance Hierarchies in Small Groups.

The formation of social groups is defined by the interactions among the group members. Studying this group formation process can be useful in understanding the status of members, decision-making behaviors, spread of knowledge and diseases, and much more. A defining characteristic of these groups is the pecking order or hierarchy the members form which help groups work towards their goals. One area of social science deals with understanding the formation and maintenance of these hierarchies, and in our work we provide social scientists with a visual analytics tool - PeckVis - to aid this process. While online social groups or social networks have been studied deeply and lead to a variety of analyses and visualization tools, the study of smaller groups in the field of social science lacks the support of suitable tools. Domain experts believe that visualizing their data can save them time as well as reveal findings they may have failed to observe. We worked alongside domain experts to build an interactive visual analytics system to investigate social hierarchies. Our system can discover patterns and relationships between the members of a group as well as compare different groups. The results are presented to the user in the form of an interactive visual analytics dashboard. We demonstrate that domain experts were able to effectively use our tool to analyze animal behavior data.

An experimental design on the SPEM 2.0 process model element classification algorithm of the AVISPA tool through ANOVA variance analysis

Introduction: This publication is the product of research: “SPEM 2.0 Process Model Metrics in the Reliability of its Visual Analysis” throughout 2019, which supports the work of a master’s degree in Systems Engineering at the University of Cauca.
 Objective: Rebase a process model metrics set in order to increase AVISPA reliability to support the visual analysis of SPEM 2.0 software process models.
 Methodology: In order to improve AVISPA, a systematic literature review had been performed to find software process model metrics that are potentially apt to be represented in AVISPA. Next, a set of assessments were performed in order to enhance visual analysis tool. Finally, an ANOVA statistical assessment was realized in order to find a variance differential between AVISPA versions by comparing their F1-Score process model elements classification values.
 Results: AVISPA significantly improved its general classification algorithm. Most of errors were found in SPEM 2.0 variability resolution feature and collections with duplicated elements. Multiple misclassifications still persists.
 Conclusion: General AVISPA process model elements classification is improved. However, some process model samples remain scattered according to ANOVA results.
 Originality: AVISPA is a recent solution for SPEM 2.0 software process model assessment. It's recent emergence carried to a lack of articles about software process model metrics and few works about AVISPA improvements. These are the main contributions of this paper.
 Limitations: The project has been widely expensive in terms of execution time, traceability with all software process model elements, and mainly to find experts in software process that can meet the research requirements

Role of absence in academic success: an analysis using visualization tools

Understanding the academic performance of students in colleges is an essential topic in Education research field. Educators, program coordinators and professors are interested in understanding how students are learning specific topics, how specific topics may influence the learning of other topics, how students’ grades/attendances in each course may represent important indicators to measure their performance, among other tasks. The use of data visualization and analytics is expanding in education institutions to perform a variety of tasks related to data processing and gaining into data-informed insights. In this paper, we present a visual analytic tool that combines data visualization and machine learning techniques to perform some visual analysis of students’ data from program courses. Two educational data collections were used to guide the creation of i) predictive models employing a variety of well known machine learning strategies, attempting to predict students’ future grade based on grade and attendance previous semesters and ii) a set interactive layouts that highlight the relationship between grades and attendance, also including additional variables such as gender, parents education level, among others. We performed several experiments, also using these data collections, to evaluate the layouts ability of highlighting interesting patterns, and we obtained promising results, demonstrating that such analysis may help the education experts to understand deficiencies on course structures.

Data-driven Analysis of Product State Propagation in Manufacturing Systems Using Visual Analytics and Machine Learning

The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed.

Hyperion: A Visual Analytics Tool for an Intrusion Detection and Prevention System

Intrusion detection and prevention systems (IDPSs) are at the core of protecting an enterprise's network. In general, IDPSs use pre-defined rules to detect potential attacks. As the size of an organization grows and new types of intrusions appear, the quantity and complexity of the rules also increase. Moreover, IDPSs generate an overwhelming number of logs that are challenging to handle and analyze. For a more effective and integrative analysis and management of the rules and logs, we propose a novel visual analytics tool, Hyperion. Hyperion interactively visualizes rules to help users understand how the IDPS rules are managed and applied to the enterprise's network entities. Hyperion also provides effective visualizations to enable users to visually analyze the type, period, traffic, and frequency of attacks in addition to a traditional count-based timeline visualization. Finally, Hyperion enables users to interactively simulate the effect of a change in parameters of a detection rule. These features can help streamline the security control cycle consisting of rule application, information collection, log analysis, and rule revision.

Nested Intellectual Data Grouping and Clusterization for the Interactive Visual Explorer

The Interactive Visual Explorer (InVEx) application is designed as a visual analytics tool for Big Data analysis. Visual analytics is an integral approach to data analysis, combining methods of intellectual data analysis with advanced interactive visualization. One of the main objectives of InVExis to process large data samples by decreasing their level of detail (LoD).The proposed approach includes clustering as well as flexible grouping by different parameters, providing the exploration of data from the lowest to the highest level of details. The results of grouping and clusterization arevisualized using interactive 3D scene and parallel coordinates, allowing the user to gain insight into data, to explore hidden correlations and trends of parameters.

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence.

Visualizing the Knowledge Domain of Project Governance: A Scientometric Review

Over the past two decades, project governance has attracted increasing attention from researchers and practitioners worldwide and has become an important research area of project management. However, an inclusive quantitative and systematic analysis of the state‐of‐the‐art recently available research in this field is still missing. This study attempts to map the global research on project governance through a state‐of‐the‐art review. A total of 285 bibliographic records were retrieved from the Web of Science Core Collection database and analyzed by the visual analytic tool—CiteSpace. The results indicated that there has been an increasing research interest in project governance. The most productive and the most highly cited author in the area of project governance is Müller R., and most of the existing project governance research achievements are from Australia, China, USA, and Norway. By synthetically analyzing the keywords, future research might focus on governance of megaprojects and project success. Additionally, 9 knowledge domains of project governance were identified, including conceptual framework, public projects, governance structure, governance context, megaproject governance, contractual and relational governance, sustainability, portfolio governance, and project success. This study contributes to the body of knowledge by mapping the existing project governance research. It is particularly helpful to new and early‐stage researchers who plan to do research on project governance, as it can provide them an overview of project governance research, including key authors, main institutions, hot topics, and knowledge domains. Moreover, the findings from the study are beneficial to industry practitioners as well, as they can help industry practitioners understand the latest development of governance theory and practice and thereby help them locate the best governance strategies for project management.

Research Status and Trends of Science and Technology Evaluation in China—Visual Analysis Based on Knowledge Mapping

In order to understand the current status and future trends of China’s science and technology evaluation research, we use the visual analysis tool CiteSpace to use the source journals included in the Chinese social science citations from 1998-2019 as the data source, and evaluate the domestic science and technology evaluation papers from the number of publications, the co-occurrence of authors, Institutional co-occurrence and subject clustering are analyzed. The study found that: the total amount of literature in the field of domestic science and technology evaluation is showing an overall upward trend; a relatively obvious author cooperation network has been formed, but the core author group has not yet been formed; institutional cooperation clusters have appeared, but the cooperation between institutions is still relatively scattered; The research topics focus on three aspects: science and technology evaluation index system, evaluation method and evaluation system.

Visualization of Blockchain Data: A Systematic Review.

We present a systematic review of visual analytics tools used for the analysis of blockchains-related data. The blockchain concept has recently received considerable attention and spurred applications in a variety of domains. We systematically and quantitatively assessed 76 analytics tools that have been proposed in research as well as online by professionals and blockchain enthusiasts. Our classification of these tools distinguishes (1) target blockchains, (2) blockchain data, (3) target audiences, (4) task domains, and (5) visualization types. Furthermore, we look at which aspects of blockchain data have already been explored and point out areas that deserve more investigation in the future.

EpidNews: Extracting, exploring and annotating news for monitoring animal diseases

In the recent years, there has been a massive increase in the amount of data published on the web about human and animal health events. Epidemiologists use this spatio-temporal information on a daily basis to detect and monitor disease outbreaks over time. While official sources such as the World Organization for Animal Health release formal outbreak notifications, unofficial sources such as online newspapers contain unstructured information with different levels of reliability. Manually retrieving the data from a website like Google News and then deriving sensible insights from the huge dataset takes a lot of time and effort. We present EpidNews, a new visual analytics tool that helps to visualize and explore epidemiological data for monitoring animal disease outbreaks. The tool uses several views depicting various levels of abstraction, which helps fulfill almost all the data analysis requirements of epidemiologists. EpidNews allows to visualize and compare data from both official and unofficial sources. We also present the use case of an epidemiology expert, wherein the expert assesses the usability and productivity of EpidNews by using the tool in her daily work.

Visualizing Hierarchical Performance Profiles of Parallel Codes Using CallFlow.

Calling context trees (CCTs) couple performance metrics with call paths, helping understand the execution and performance of parallel programs. To identify performance bottlenecks, programmers and performance analysts visually explore CCTs to form and validate hypotheses regarding degraded performance. However, due to the complexity of parallel programs, existing visual representations do not scale to applications running on a large number of processors. We present CallFlow, an interactive visual analysis tool that provides a high-level overview of CCTs together with semantic refinement operations to progressively explore CCTs. Using a flow-based metaphor, we visualize a CCT by treating execution time as a resource spent during the call chain, and demonstrate the effectiveness of our design with case studies on large-scale, production simulation codes.

Development of a Visual Analytics Tool for Polytrauma Patients: Proof of Concept for a New Assessment Tool Using a Multiple Layer Sankey Diagram in a Single-Center Database.

Early physiological assessment of multiple injured patients is crucial for decision making and has relied on personal experience of trauma experts. We have developed a new visual analytics tool (Sankey diagram, Watson Trauma Health care tool) that includes known prognostic parameters for polytrauma patients to help guide assessment and treatment decisions for physicians involved in trauma care. A prospectively collected trauma database of a single level I trauma center (3655 patients) was used. age >16years, an injury severity score (ISS) >16 and presence of a complete data set in the database. Data collected included admission values of patient age, injury scoring, shock classification, temperature, acid-base and hemostasis parameters. All of these parameters were collected daily as longitudinal parameters. Endpoints of the clinical course we considered were sepsis, SIRS and early in hospital mortality (<72h). A proof of concept of the visualization was developed over a 2-year period in a cooperation between physicians and engineers. Statistically, the most predictive parameters were selected by binary logistic regression and ROC analysis. A dynamic interactive multilayer Sankey diagram, based on cohort similarities, was developed in a collaboration between the University Hospital of Zurich, Department of Trauma and IBM, from August 2017 until January 2018. It is a modular tool and allows any user to add a new patient, or work with an existing case. The visualization used the data-driven documents (D3) interactive visualization library to create a responsive graphic. This application summarizes the experience of 3655 polytrauma patients and might serve as a guide for clinical decisions and educative purposes, as well as new scientific questions for the polytrauma patient. IV.

PerformanceVis: Visual analytics of student performance data from an introductory chemistry course

We present PerformanceVis, a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design. Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment, we consider the requirements and needs of students, instructors, and administrators in the design of PerformanceVis. We study the correlation between question items from assignments and exams, employ machine learning techniques for student grade prediction, and develop an interface for interactive exploration of student course performance data. PerformanceVis includes four main views (overall exam grade pathway, detailed exam grade pathway, detailed exam item analysis, and overall exam & homework analysis) which are dynamically linked together for user interaction and exploration. We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation. Finally, we conclude this work by pointing out future work in this direction of learning analytics research.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities.

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review.

BackgroundVisual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR).ObjectiveThe purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care.MethodsWe will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers.ResultsAs of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019.ConclusionsVA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally.International Registered Report Identifier (IRRID)DERR1-10.2196/14019

SilkDB 3.0: visualizing and exploring multiple levels of data for silkworm.

SilkDB is an open-accessibility database and powerful platform that provides comprehensive information on the silkworm (Bombyx mori) genome. Since SilkDB 2.0 was released 10 years ago, vast quantities of data about multiple aspects of the silkworm have been generated, including genome, transcriptome, Hi-C and pangenome. To visualize data at these different biological levels, we present SilkDB 3.0 (https://silkdb.bioinfotoolkits.net), a visual analytic tool for exploring silkworm data through an interactive user interface. The database contains a high-quality chromosome-level assembly of the silkworm genome, and its coding sequences and gene sets are more accurate than those in the previous version. SilkDB 3.0 provides a view of the information for each gene at the levels of sequence, protein structure, gene family, orthology, synteny, genome organization and gives access to gene expression information, genetic variation and genome interaction map. A set of visualization tools are available to display the abundant information in the above datasets. With an improved interactive user interface for the integration of large data sets, the updated SilkDB 3.0 database will be a valuable resource for the silkworm and insect research community.

A Provenance Task Abstraction Framework.

Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of 1) initializing a provenance task hierarchy, 2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and 3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. We describe a use case which exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The article concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework.