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DOE OSTI2016

Streaming Visual Analytics Workshop Report

How can we best enable users to understand complex emerging events and make appropriate assessments from streaming data? This was the central question addressed at a three-day workshop on streaming visual analytics. This workshop was organized by Pacific Northwest National Laboratory for a government sponsor. It brought together forty researchers and subject matter experts from government, industry, and academia. This report summarizes the outcomes from that workshop. It describes elements of the vision for a streaming visual analytic environment and set of important research directions needed to achieve this vision. Streaming data analysis is in many ways the analysis and understanding of change. However, current visual analytics systems usually focus on static data collections, meaning that dynamically changing conditions are not appropriately addressed. The envisioned mixed-initiative streaming visual analytics environment creates a collaboration between the analyst and the system to support the analysis process. It raises the level of discourse from low-level data records to higher-level concepts. The system supports the analyst’s rapid orientation and reorientation as situations change. It provides an environment to support the analyst’s critical thinking. It infers tasks and interests based on the analyst’s interactions. The system works as both an assistant and a devil’s advocate, finding relevant data and alerts as well as considering alternative hypotheses. Finally, the system supports sharing of findings with others. Making such an environment a reality requires research in several areas. The workshop discussions focused on four broad areas: support for critical thinking, visual representation of change, mixed-initiative analysis, and the use of narratives for analysis and communication.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2014

Pathways to Identity. Using Visualization to Aid Law Enforcement in Identification Tasks

The nature of identity has changed dramatically in recent years and has grown in complexity. Identities are defined in multiple domains: biological and psychological elements strongly contribute, but biographical and cyber elements also are necessary to complete the picture. Law enforcement is beginning to adjust to these changes, recognizing identity’s importance in criminal justice. The SuperIdentity project seeks to aid law enforcement officials in their identification tasks through research of techniques for discovering identity traits, generation of statistical models of identity and analysis of identity traits through visualization. We present use cases compiled through user interviews in multiple fields, including law enforcement, and describe the modeling and visualization tools design to aid in those use cases.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.

Sensemaking, human-machine teaming, agency, artifi↗

DOE OSTI2015

Text Stream Trend Analysis using Multiscale Visual Analytics with Applications to Social Media Systems

In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing these contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.

interaction↗

DOE OSTI2018

Can You Verifi This? Studying Uncertainty and Decision-Making About Misinformation using Visual Analytics

We describe a novel study of decision-making processes around misinformation on social media. Using a custom-built visual analytic system, we presented users with news content from social media accounts from a variety of news outlets, including outlets engaged in distributing misinformation. We conducted controlled experiments to study decision-making regarding the veracity of these news outlets and tested the role of confirmation bias (the tendency to ignore contradicting information) and uncertainty of information on human decision-making processes. Our findings reveal that the presence of conflicting information, presented to users in the form of cues, impacts the ability to judge the veracity of news in systematic ways. We also find that instructing participants even to explicitly disconfirm given hypotheses does not significantly impact their decision-making regarding misinformation when compared to a control condition. Our findings have the potential to inform the design of visual analytics systems so that they may be used to mitigate the effects of cognitive biases and stymie the spread of misinformation on social media.

misinformation, decision making, social media, vis↗

DOE OSTI2015

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction, Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.

data visualization↗

DOE OSTI2022

An overview of visualization and visual analytics applications in water resources management

Recent advances in information, communication, and environmental monitoring technologies have increased the availability, spatiotemporal resolution, and quality of water-related data, thereby leading to the emergence of many innovative big data applications. Among these applications, visualization and visual analytics, also known as the visual computing techniques, empower the synergy of computational methods (e.g., machine learning and statistical models) with human reasoning to improve the understanding and solution toward complex science and engineering problems. These approaches are frequently integrated with geographic information systems and cyberinfrastructure to provide new opportunities and methods for enhancing water resources management. Here, we present a comprehensive review of recent hydroinformatics applications that employ visual computing techniques to (1) support complex data-driven research problems, and (2) support the communication and decision-makings in the water resources management sector. Then, we conduct a technical review of the state-of-the-art web-based visualization technologies and libraries to share our experiences on developing shareable, adaptive, and interactive visualizations and visual interfaces for water resources management applications. We close with a vision that applies the emerging visual computing technologies and paradigms to develop the next generation of hydroinformatics applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

Human-centered computing↗

DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗

DOE OSTI2024

A Visual Analytic Platform for Interactive Validation of Human Mobility Simulations

Human mobility insights guide domain experts in an array of decisions, including critical infrastructure design, disaster response, epidemic modeling, national security, and policy making. Due to the inherent noise and privacy concerns in real-world individual-level mobility data, it is often preferred to leverage simulators that generate synthetic mobility data instead. However, it is critical to inspect and validate the output of such simulators to ensure the synthetic data is aligned with the characteristics of the population and the area of interest known to domain experts. While there exist many quantitative approaches for validating synthetic data, we argue it is also important to also validate such data qualitatively to capture aspects that are known to domain experts but difficult to quantify. In this work, we demonstrate a visual analytic platform that empowers domain experts to interact with their simulation outputs along spatial and temporal dimensions. By augmenting automated techniques and human skills, our visual analytic platform is a step towards interactive capabilities for model steering and quality control of mobility simulators.

Monadjemi, Shayan↗

DOE OSTI2023

Vis-SAGA: Visual Analytics for Situational Awareness of Grid Anomalies: Preprint

We describe supporting near real-time situational awareness of the electric distribution system by visualizing novel data from voltage sensors deployed on existing broadband cable television network equipment. Our scalable web-based visual analytics platform supports interactive geospatial exploration, time-series analysis, and summarization of grid behavior during potentially anomalous events. The broadband cable television sensor network provides observability of the electrical distribution system at a higher local spatial resolution than is typically available to most utilities, revealing the operational state of the network and aiding in the detection of abnormal behaviors or deviations from expected patterns, particularly across electric utility service areas. We outline the design and development of interactive geospatial and time-series visualization components and the scalable data services that supply metadata, historical, and real-time streams of sensor data across the network. We evaluate our platform during periods of extreme weather, demonstrating its ability to assist in detecting patterns of operation that affect power availability, quality, resiliency, and service restoration.

cable television↗

DOE OSTI2023

Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics—Near-Real-Time Cyber-Physical Resiliency Through Machine Learning

The Situational Awareness of Grid Anomalies (SAGA) project built upon foundational power system tools developed at the National Renewable Energy Laboratory (NREL) integrated with an ever-increasing set of Gridmetrics data extracted from the cable television (CATV) broadband network infrastructure while assimilating other time-series geospatial data and information, such as weather and cyber-physical phenomena, to demonstrate a disruptive technology for power system data analytics relying on existing infrastructure. Three research thrusts supported (1) visual analytics, (2) cyber-physical power system simulation, and (3) anomaly detection. SAGA created technology that leverages, couples, and fortifies two vastly different realms - power and broadband - to increase the resiliency of the power grid in the face of increasing cyberattacks and operational challenges related to integrating DERs. The exploration of potential synergies of broadband-enabled grids resulted in identifying a mutually beneficial symbiosis that can increase the resiliency of both power and broadband services. Broadband networks perform better with reliable power and are good at providing real-time measurements that identify where the grid is under attack, is failing, or is weak. Likewise, sensor-starved distribution grids perform better and can be more reliable when their operation is buttressed with observations of broadband-detected anomalies. Future research can explore broadband's contribution to continuing to improve grid resiliency, reliability, and cost-effective operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Online Interactive Platform for COVID-19 Literature Visual Analytics: Platform Development Study

Background Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. Objective We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. Methods We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher’s query. Results PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. Conclusions The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.</:p>

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗

DOE OSTI2014

Visual Analytics for Power Grid Contingency Analysis

Contingency analysis is the process of employing different measures to model scenarios, analyze them, and then derive the best response to remove the threats. This application paper focuses on a class of contingency analysis problems found in the power grid management system. A power grid is a geographically distributed interconnected transmission network that transmits and delivers electricity from generators to end users. The power grid contingency analysis problem is increasingly important because of both the growing size of the underlying raw data that need to be analyzed and the urgency to deliver working solutions in an aggressive timeframe. Failure to do so may bring significant financial, economic, and security impacts to all parties involved and the society at large. The paper presents a scalable visual analytics pipeline that transforms about 100 million contingency scenarios to a manageable size and form for grid operators to examine different scenarios and come up with preventive or mitigation strategies to address the problems in a predictive and timely manner. Great attention is given to the computational scalability, information scalability, visual scalability, and display scalability issues surrounding the data analytics pipeline. Most of the large-scale computation requirements of our work are conducted on a Cray XMT multi-threaded parallel computer. The paper demonstrates a number of examples using western North American power grid models and data.

contingency analysis↗

NASA NTRS2024

Multidisciplinary Analytics, Visualization, and Reporting Interface for Integrated Countermeasures

Exploration class missions will have communication latency requiring crew members to make decisions more autonomously, with less support from ground personnel. Therefore, new software is needed to provide crew the ability to not only visualize their countermeasures data, but to also derive comprehensive, nuanced, and multidisciplinary insights regarding the health and performance informatics throughout a mission. The Multidisciplinary Analytics, Visualization, and Reporting Interface for Integrated Countermeasures (MAVRIIC) software seeks to establish a centralized approach to countermeasure data visualization that will facilitate a more holistic understanding of crew members' well-being and performance and enable the development of informed and autonomous decision-making support systems aligned with the evolving requirements of Exploration missions. The culmination of MAVRIIC phase 1 (end of FY23) marked the release of a full-stack, cloud-based ground tool displaying visualizations of in-flight exercise data, exercise ground testing, and exercise MEDB reports. Phase 2 of MAVRIIC (FY24) focuses on beginning the expansion of data contents to domains outside of exercise, including functional fitness, sensorimotor, food/nutrition, radiation, bone, and cardio/vision. Phase 3 and phase 4 will comprise enhanced analytics integration and initiation of flight tool development for infusion into the Artemis Program and eventually the Mars Transit Habitat.

Kent Lawrence Kalogera↗

DOE OSTI2013

Big Data Visual Analytics for Exploratory Earth System Simulation Analysis

Rapid increases in high performance computing are feeding the development of larger and more complex data sets in climate research, which sets the stage for so-called big data analysis challenges. However, conventional climate analysis techniques are inadequate in dealing with the complexities of today s data. In this paper, we describe and demonstrate a visual analytics system, called the Exploratory Data analysis ENvironment (EDEN), with specific application to the analysis of complex earth system simulation data sets. EDEN represents the type of interactive visual analysis tools that are necessary to transform data into insight, thereby improving critical comprehension of earth system processes. In addition to providing an overview of EDEN, we describe real-world studies using both point ensembles and global Community Land Model Version 4 (CLM4) simulations.

big data↗