Challenges Of Visual Analytics Research Articles

Visual Analytics to Leverage Anesthesia Electronic Health Record.

Visual analytics is the science of analytical reasoning supported by interactive visual interfaces called dashboards. In this report, we describe our experience addressing the challenges in visual analytics of anesthesia electronic health record (EHR) data using a commercially available business intelligence (BI) platform. As a primary outcome, we discuss some performance metrics of the dashboards, and as a secondary outcome, we outline some operational enhancements and financial savings associated with deploying the dashboards. Data were transferred from the EHR to our departmental servers using several parallel processes. A custom structured query language (SQL) query was written to extract the relevant data fields and to clean the data. Tableau was used to design multiple dashboards for clinical operation, performance improvement, and business management. Before deployment of the dashboards, detailed case counts and attributions were available for the operating rooms (ORs) from perioperative services; however, the same level of detail was not available for non-OR locations. Deployment of the yearly case count dashboards provided near-real-time case count information from both central and non-OR locations among multiple campuses, which was not previously available. The visual presentation of monthly data for each year allowed us to recognize seasonality in case volumes and adjust our supply chain to prevent shortages. The dashboards highlighted the systemwide volume of cases in our endoscopy suites, which allowed us to target these supplies for pricing negotiations, with an estimated annual cost savings of $250,000. Our central venous pressure (CVP) dashboard enabled us to provide individual practitioner feedback, thus increasing our monthly CVP checklist compliance from approximately 92% to 99%. The customization and visualization of EHR data are both possible and worthwhile for the leveraging of information into easily comprehensible and actionable data for the improvement of health care provision and practice management. Limitations inherent to EHR data presentation make this customization necessary, and continued open access to the underlying data set is essential.

A survey of visual analytics for Explainable Artificial Intelligence methods

Deep learning (DL) models have achieved impressive performance in various domains such as medicine, finance, and autonomous vehicle systems with advances in computing power and technologies. However, due to the black-box structure of DL models, the decisions of these learning models often need to be explained to end-users. Explainable Artificial Intelligence (XAI) provides explanations of black-box models to reveal the behavior and underlying decision-making mechanisms of the models through tools, techniques, and algorithms. Visualization techniques help to present model and prediction explanations in a more understandable, explainable, and interpretable way. This survey paper aims to review current trends and challenges of visual analytics in interpreting DL models by adopting XAI methods and present future research directions in this area. We reviewed literature based on two different aspects, model usage and visual approaches. We addressed several research questions based on our findings and then discussed missing points, research gaps, and potential future research directions. This survey provides guidelines to develop a better interpretation of neural networks through XAI methods in the field of visual analytics.

Toward flexible visual analytics augmented through smooth display transitions

Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.

Incorporating visual analytics with knowledge construction in problem-based learning: a qualitative study

ABSTRACT Visual analytics (VA) technology has become a significant tool for business decision-makers to explore novel insights. One of its key features is analytical reasoning through interactive visualization that users have direct sensemaking in problem-solving processes. Few studies have examined how VA can be applied in business education for improving educational purpose. Social cognitive theory grounded in the current study as a framework to guide a qualitative exploration. The investigation was guided by two research questions: (1) What are the benefits and challenges of VA used in business education? (2). How do students interact with VA to improve knowledge construction in problem-solving? In-depth interviews and focus groups were conducted to derive data for thematic analysis from 25 informants. Three researchers conducted coding and data analysis through Nvivo 11.0. Five themes were identified in the corpus of data: (1). Perceived benefits to learning, (2). Prerequisite conditions, (3). Application context, (4). Motivational engagement, and (5). Knowledge construction. The study suggests that incorporating VA technology with problem-solving facilitates students’ knowledge organisation and acquisition.

Open challenges in visual analytics for security information and event management

Introduction: Visual analytics techniques support efficient analysis of the ever-growing amounts of data generated by security sensors and facilitate a timely and reasonable response to the threats. Modern security information and event management systems propose various solutions for processing large data streams and integrating heterogeneous sources which can be used as a framework to construct a visual analytics system for security tasks. Purpose: The analysis of visual analytics techniques implemented in security information and event management systems and designed to support the studies on security incidents in the context of the main visual analytics problems, including the validation of automatic analysis models. Results: A contradiction has been detected between the capabilities of security information and event management systems in the visual analysis of security data and the implementation of these capabilities. Techniques for visual correlation of the data from different security sensors and for visual validation of automatic analysis models which would allow you to evaluate their accuracy and adaptability to the changes in data streams are almost missing. A possible way to resolve this contradiction is using techniques which support a flexible mechanism for adjusting the analyzed attributes of the network device events. The article presents the main approaches to the development of such techniques, discussing their advantages and disadvantages. We propose a dashboard for monitoring the behavior of an automated network traffic analysis model used in a cloud computing infrastructure. It allows you to monitor the analysis model behavior, perform a visual correlation of the analyzed parameters, and track changes in the network flows. Practical relevance: The results of the research can be used when designing security visual analytics tools for monitoring data flows and the behavior of automated analysis models.

DNA sequences at a glance.

Data summarization and triage is one of the current top challenges in visual analytics. The goal is to let users visually inspect large data sets and examine or request data with particular characteristics. The need for summarization and visual analytics is also felt when dealing with digital representations of DNA sequences. Genomic data sets are growing rapidly, making their analysis increasingly more difficult, and raising the need for new, scalable tools. For example, being able to look at very large DNA sequences while immediately identifying potentially interesting regions would provide the biologist with a flexible exploratory and analytical tool. In this paper we present a new concept, the “information profile”, which provides a quantitative measure of the local complexity of a DNA sequence, independently of the direction of processing. The computation of the information profiles is computationally tractable: we show that it can be done in time proportional to the length of the sequence. We also describe a tool to compute the information profiles of a given DNA sequence, and use the genome of the fission yeast Schizosaccharomyces pombe strain 972 h− and five human chromosomes 22 for illustration. We show that information profiles are useful for detecting large-scale genomic regularities by visual inspection. Several discovery strategies are possible, including the standalone analysis of single sequences, the comparative analysis of sequences from individuals from the same species, and the comparative analysis of sequences from different organisms. The comparison scale can be varied, allowing the users to zoom-in on specific details, or obtain a broad overview of a long segment. Software applications have been made available for non-commercial use at http://bioinformatics.ua.pt/software/dna-at-glance.