Interactive Visual Exploration Research Articles

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations

Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this article, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present the Visualization of Embedding Representations for deBiasing (VERB) system, an open-source web-based visualization tool that helps users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties. In particular, VERB offers easy-to-follow examples that explore the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, VERB decomposes each technique into interpretable sequences of primitive transformations and highlights their effect on the word vectors using dimensionality reduction and interactive visual exploration. VERB is designed to target natural language processing (NLP) practitioners who are designing decision-making systems on top of word embeddings and researchers working with the fairness and ethics of machine learning systems in NLP. It can also serve as a visual medium for education, which helps an NLP novice understand and mitigate biases in word embeddings.

KGScope: Interactive Visual Exploration of Knowledge Graphs with Embedding-based Guidance.

Knowledge graphs have been commonly used to represent relationships between entities and utilized in the industry to enhance service qualities. As knowledge graphs integrate data from a variety of sources, they can also be useful references for data analysts. However, there is a lack of effective tools to make the most of the rich information in knowledge graphs. Existing knowledge graph exploration systems are ineffective because they didn?t consider various users? needs and the characteristics of knowledge graphs. Exploratory approaches specifically designed for uncovering and summarizing insights in knowledge graphs have not been well studied yet. In this paper, we propose KGScope that supports interactive visual explorations and provides embedding-based guidance to derive insights from knowledge graphs. We demonstrate KGScope with usage scenarios and assess its efficacy in supporting knowledge graph exploration with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and aiding comprehensive exploration.

ConceptThread: Visualizing Threaded Concepts in MOOC Videos.

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread, a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, speech recognition and topic modeling, to extract core knowledge concepts and construct the hierarchical and temporal relations among them. Then, by using a metaphor of thread, we present a novel visualization to intuitively display the concepts based on video sequential flow, and enable learners to perform interactive visual exploration of concepts. We conducted a quantitative study, two case studies, and a user study to extensively evaluate ConceptThread. The results demonstrate the effectiveness and usability of ConceptThread in providing online learners with a quick understanding of the knowledge content of MOOC videos.

An efficient visual exploration approach of geospatial vector big data on the web map

The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual exploration approach of GVBD on the web map. In this approach, we propose the display-driven computing model and combine the traditional data-driven computing method to design an adaptive real-time visualization algorithm. At the same time, we design a pixel-quad-R tree spatial index structure. Finally, we realize the multilevel real-time interactive visual exploration of GVBD in a single machine by constructing the index offline to support the online computation for visualization, and all the visualization results can be calculated in real-time without the external cache occupation. The experimental results show that the approach outperforms current mainstream visualization methods and obtains the visualization results at any zoom level within 0.5 s, which can be well applied to multilevel real-time interactive visual exploration of the billion-scale GVBD.

Visual Exploration of Repetitive Patterns on Ancient Peruvian Pottery

The analysis and understanding of artefact properties and their relationships is a key goal in archaeological analysis of cultural heritage objects. There are many aspects of concern, including shape properties of the objects, but also appearance properties stemming from paintings and ornamentations on the object surfaces. To date, these are considered by experts mostly holistically and on a per-object basis. We present an approach for the interactive visual exploration and correlation of shape- and ornament-based properties of a large collection of ancient vessels. Our approach allows to group objects by properties, and to relate them in side-by-side and bipartite graph displays. To this end, we define an encompassing set of feature descriptors, which are leveraged to cluster the objects by properties, selected by the user. Case studies show that a comparative overview of all objects effectively supports the discovery of interesting co-occurrences of shape and ornament properties. This way, our tool opens new possibilities for the domain analysis of cultural heritage object collections by data-driven visual exploration.

Improved insights into the SABIO-RK database via visualization

SABIO-RK is a database for biochemical reactions and their kinetics. Data in SABIO-RK are inherently multidimensional and complex. The complex relationships between the data are often difficult to follow or even not represented when using standard tabular views. With an increasing number of data points the mismatch between tables and insights becomes more obvious, and getting an overview of the data becomes harder. Such complex data benefit from being presented using specially adapted visual tools. Visualization is a natural and user-friendly way to quickly get an overview of the data and to detect clusters and outliers. Here, we describe the implementation of a variety of visualization concepts into a common interface within the SABIO-RK biochemical reaction kinetics database. For that purpose, we use a heat map, parallel coordinates and scatter plots to allow the interactive visual exploration of general entry-based information of biochemical reactions and specific kinetic parameter values. Database URL https://sabiork.h-its.org/.

Tinycubes: A modular technology for interactive visual analysis of historical and continuously updated spatiotemporal data

Technological advances allow sending data collected, like IoT data and Social Network data, immediately to computational centers. However, state-of-the-art solutions for interactive visual exploration and analysis of spatiotemporal multidimensional using a single computer are focused on historical datasets. Also, most of these solutions utilize a monolithic architecture, with a restricted set of information extraction techniques and rigid internal data structures, which leads to the creation of forks to handle different usage needs. Tinycubes is a technology that supports the interactive exploration and visual analysis of spatiotemporal multidimensional continuously collected data in real-time combined with data from historical datasets, consistently producing up-to-date information using a single computer. The technology’s core is a novel and efficient data structure based on data cubes that allow data insertion and removal on-the-fly. The Tinycubes Technology is modular, allowing the usage of different information extraction algorithms and efficient data storage methods. Our experiments indicate that Tinycubes will enable the exploration of newly generated data combined with historical data while presenting performance similar or superior to the state-of-the-art solutions that use a single computer.

Seeing is believing: Towards interactive visual exploration of data privacy in federated learning

Federated learning (FL), as a popular distributed machine learning paradigm, has driven the integration of knowledge in ubiquitous data owners under one roof. Although designed for privacy-preservation by nature, the supposed well-sanitized parameters still convey sensitive information (e.g., reconstruction attack), while existing technical countermeasures provide weak explainability for privacy understanding and protection practices of general users. This work investigates these privacy concerns with an exploratory study and elaborates on data owners’ expectations in FL. Based on the analysis, we design the first interactive visualization system for FL privacy that supports intelligible privacy inspection and adjustment for data owners. Specifically, our proposal facilitates sample recommendation for joint privacy–performance training at cold start. Then it provides visual interpretation and attention rendering of privacy risks in view of multiple attacking channels and a holistic view. Further it supports interactive privacy enhancement involving both user initiative and differential privacy technique, and iterative trade-off with real-time inference accuracy estimation. We evaluate the effectiveness of the system and collect qualitative feedbacks from users. The results demonstrate that 96.7% of users acknowledge the benefits to privacy inspection and adjustment and 90.3% are willing to use our system. More importantly, 87.1% increase the willingness of contributing data for FL.

E-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

Exploring time series of hyperspectral images for cold water coral stress response analysis.

Hyperspectral imaging (HSI) is a promising technology for environmental monitoring with a lot of undeveloped potential due to the high dimensionality and complexity of the data. If temporal effects are studied, such as in a monitoring context, the analysis becomes more challenging as time is added to the dimensions of space (image coordinates) and wavelengths. We conducted a series of laboratory experiments to investigate the impact of different stressor exposure patterns on the spectrum of the cold water coral Desmophyllum pertusum. 65 coral samples were divided into 12 groups, each group being exposed to different types and levels of particles. Hyperspectral images of the coral samples were collected at four time points from prior to exposure to 6 weeks after exposure. To investigate the relationships between the corals’ spectral signatures and controlled experimental parameters, a new software tool for interactive visual exploration was developed and applied, the HypIX (Hyperspectral Image eXplorer) web tool. HypIX combines principles from exploratory data analysis, information visualization and machine learning-based dimension reduction. This combination enables users to select regions of interest (ROI) in all dimensions (2D space, time point and spectrum) for a flexible integrated inspection. We propose two HypIX workflows to find relationships in time series of hyperspectral datasets, namely morphology-based filtering workflow and embedded driven response analysis workflow. With these HypIX workflows three users identified different temporal and spatial patterns in the spectrum of corals exposed to different particle stressor conditions. Corals exposed to particles tended to have a larger change rate than control corals, which was evident as a shifted spectrum. The responses, however, were not uniform for coral samples undergoing the same exposure treatments, indicating individual tolerance levels. We also observed a good inter-observer agreement between the three HyPIX users, indicating that the proposed workflow can be applied to obtain reproducible HSI analysis results.

Satellite image search in AgoraEO

The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives. MiLaN is a deep hashing network based on metric learning that encodes high-dimensional image features into compact binary hash codes. We use these codes as keys in a hash table to enable real-time nearest neighbor search and highly accurate retrieval. In this demonstration, we showcase the efficiency of MiLaN by integrating it with EarthQube, a browser and search engine within AgoraEO. EarthQube supports interactive visual exploration and Query-by-Example over satellite image repositories. Demo visitors will interact with EarthQube playing the role of different users that search images in a large-scale remote sensing archive by their semantic content and apply other filters.

Carbonic: A Framework for Creating and Visualizing Complex Compound Graphs

Advances in data generation and acquisition have resulted in a volume of available data of such magnitude that our ability to interpret and extract valuable knowledge from them has been surpassed. Our capacity to analyze data is hampered not only by their amount or their dimensionality, but also by their relationships and by the complexity of the systems they model. Compound graphs allow us to represent the existing relationships between nodes that are themselves hierarchically structured, so they are a natural substrate to support multiscale analysis of complex graphs. This paper presents Carbonic, a framework for interactive multiscale visual exploration and editing of compound graphs that incorporates several strategies for complexity management. It combines the representation of graphs at multiple levels of abstraction, with techniques for reducing the number of visible elements and for reducing visual cluttering. This results in a tool that allows both the exploration of existing graphs and the visual creation of compound graphs following a top-down approach that allows simultaneously observing the entities and their relationships at different scales. The results show the applicability of the developed framework to two use cases, demonstrating the usefulness of Carbonic for moving from information to knowledge.

Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams

Today’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.

BrainEx: Interactive Visual Exploration and Discovery of Sequence Similarity in Brain Signals

Technology advances and lower equipment costs are enabling non-invasive, convenient recording of brain data outside of clinical settings in more real-world environments, and by non-experts. Despite the growing interest in and availability of brain signal datasets, most analytical tools are made for experts in the specific device technology, and have rigid constraints on the type of analysis available. We developed BrainEx to support interactive exploration and discovery within brain signals datasets. BrainEx takes advantage of algorithms that enable fast exploration of complex, large collections of time series data, while being easy to use and learn. This system enables researchers to perform similarity search, explore feature data and natural clustering, and select sequences of interest for future searches and exploration, while also maintaining the usability of a visual tool. In addition to describing the distributed architecture and visual design for BrainEx, this paper reports on a benchmark experiment showing that it outperforms other existing systems for similarity search. Additionally, we report on a preliminary user study in which domain experts used the visual exploration interface and affirmed that it meets the requirements. Finally, it presents a case study using BrainEx to explore real-world, domain-relevant data.

Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering

AbstractWe present a pipeline for the interactive visual analysis and exploration of molecular electronic transition ensembles. Each ensemble member is specified by a molecular configuration, the charge transfer between two molecular states, and a set of physical properties. The pipeline is targeted towards theoretical chemists, supporting them in comparing and characterizing electronic transitions by combining automatic and interactive visual analysis. A quantitative feature vector characterizing the electron charge transfer serves as the basis for hierarchical clustering as well as for the visual representations. The interface for the visual exploration consists of four components. A dendrogram provides an overview of the ensemble. It is augmented with a level of detail glyph for each cluster. A scatterplot using dimensionality reduction provides a second visualization, highlighting ensemble outliers. Parallel coordinates show the correlation with physical parameters. A spatial representation of selected ensemble members supports an in‐depth inspection of transitions in a form that is familiar to chemists. All views are linked and can be used to filter and select ensemble members. The usefulness of the pipeline is shown in three different case studies.

Automatic assessment of adverse drug reaction reports with interactive visual exploration

A large number of adverse drug reaction (ADR) reports are collected yearly through the spontaneous report system (SRS). However, experienced experts from ADR monitoring centers (ADR experts, hereafter) reviewed only a few reports based on current policies. Moreover, the causality assessment of ADR reports was conducted according to the official approach based on the WHO-UMC system, a knowledge- and labor-intensive task that highly relies on an individual’s expertise. Our objective is to devise a method to automatically assess ADR reports and support the efficient exploration of ADRs interactively. Our method could improve the capability to assess and explore a large volume of ADR reports and aid reporters in self-improvement. We proposed a workflow for assisting the assessment of ADR reports by combining an automatic assessment prediction model and a human-centered interactive visualization method. Our automatic causality assessment model (ACA model)—an ordinal logistic regression model—automatically assesses ADR reports under the current causality category. Based on the results of the ACA model, we designed a warning signal to indicate the degree of the anomaly of ADR reports. An interactive visualization technique was used for exploring and examining reports extended by automatic assessment of the ACA model and the warning signal. We applied our method to the SRS report dataset of the year 2019, collected in Guangdong province, China. Our method is evaluated by comparing automatic assessments by the ACA model to ADR reports labeled by ADR experts, i.e., the ground truth results from the multinomial logistic regression and the decision tree. The ACA model achieves an accuracy of 85.99%, a multiclass macro-averaged area under the curve (AUC) of 0.9572, while the multinomial logistics regression and decision tree yield 80.82%, 0.8603, and 85.39%, 0.9440, respectively, on the testing set. The new warning signal is able to assist ADR experts to quickly focus on reports of interest with our interactive visualzation tool. Reports of interest that are selected with high scores of the warning signal are analyzed in details by an ADR expert. The usefulness of the overall method is further evaluated through the interactive analysis of the data by ADR expert. Our ACA model achieves good performance and is superior to the multinomial logistics and the decision tree. The warning signal we designed allows efficient filtering of the full ADR reports down to much fewer reports showing anomalies. The usefulness of our interactive visualization is demonstrated by examples of unusual reports that are quickly identified. Our overall method could potentially improve the capability of analyzing ADR reports and reduce human labor and the chance of missing critical reports.

Interactive and iterative visual exploration of knowledge graphs based on shareable and reusable visual configurations

Knowledge graphs denote structured data which represent entities and relationships between them in a form of a graph, often expressed in the RDF data model. It may be hard for lay users to explore existing knowledge graphs, especially when graphs from different data sources need to be integrated. In this paper, we present an approach to knowledge graph visual exploration based on the concept of shareable and reusable visual configurations. A visual configuration comprises domain specific views on a knowledge graph which define operations such as node detail or expansion. These operations are easy to understand for lay users who can use them to explore a graph while complexities unnecessary in a given application context remain hidden. We introduce an ontology which enables to express and publish visual configurations and reuse their components in other configurations. We also provide an experimental implementation called KGBrowser. We evaluate the proposed approach with real users. We also compare our implementation KGBrowser with other existing tools for knowledge graph visualization and exploration.

A learning-based approach for efficient visualization construction

We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions. Instead, LVI directly predicts aggregates of interest for the user’s data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.

Interactive Visual Analysis of Structure-borne Noise Data.

Numerical simulation has become omnipresent in the automotive domain, posing new challenges such as high-dimensional parameter spaces and large as well as incomplete and multi-faceted data. In this design study, we show how interactive visual exploration and analysis of high-dimensional, spectral data from noise simulation can facilitate design improvements in the context of conflicting criteria. Here, we focus on structure-borne noise, i.e., noise from vibrating mechanical parts. Detecting problematic noise sources early in the design and production process is essential for reducing a product's development costs and its time to market. In a close collaboration of visualization and automotive engineering, we designed a new, interactive approach to quickly identify and analyze critical noise sources, also contributing to an improved understanding of the analyzed system. Several carefully designed, interactive linked views enable the exploration of noises, vibrations, and harshness at multiple levels of detail, both in the frequency and spatial domain. This enables swift and smooth changes of perspective; selections in the frequency domain are immediately reflected in the spatial domain, and vice versa. Noise sources are quickly identified and shown in the context of their neighborhood, both in the frequency and spatial domain. We propose a novel drill-down view, especially tailored to noise data analysis. Split boxplots and synchronized 3D geometry views support comparison tasks. With this solution, engineers iterate over design optimizations much faster, while maintaining a good overview at each iteration. We evaluated the new approach in the automotive industry, studying noise simulation data for an internal combustion engine.