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Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a university can be deemed less prestigious if not featured in a top-k list, and consumers might not even explore products that do not get recommended to buyers. At the heart of most of these decisions are opaque ranking schemes, which dictate the ordering of data entities, but their internal logic is inaccessible or proprietary. Drawing inferences about the ranking differences is like a guessing game to the stakeholders, like, the rankees (i.e., the entities who are ranked, like product companies) and the decision-makers (i.e., who use the rankings, like buyers). In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods. To realize this aim, we leverage the exploration–explanation paradigm of human–data interaction to let human stakeholders explore subsets and groupings of complex multi-attribute ranking data using visual explanations of model fit and attribute influence on rankings. We realize this explanation paradigm for transparent ranking interpretation in TRIVEA, a visual analytic system that is fueled by: (i) visualizations of model fit derived from algorithmic rankers that learn the associations between attributes and rankings from available data and (ii) visual explanations derived from XAI methods that help abstract important patterns, like, the relative influence of attributes in different ranking ranges. Using TRIVEA, end users not trained in data science have the agency to transparently reason about the global and local behavior of the rankings without the need to open black-box ranking models and develop confidence in the resulting attribute-based inferences. We demonstrate the efficacy of TRIVEA using multiple usage scenarios and subjective feedback from researchers with diverse domain expertise. [ABSTRACT FROM AUTHOR]

Data visualization is an essential task during the lifecycle of any Data Science (DS) project, particularly during the Exploratory Data Analysis (EDA) for a correct data preparation and understanding. In classification problems, data visualization is useful for revealing the existence of class separability patterns within the dataset. This information is very valuable and can be later used during the process of building a Machine Learning (ML) model. High-Dimensional Data (HDD) arise as one of the biggest challenges in DS. HDD require special treatment since traditional visualization techniques, such as the scatterplot matrix (SPLOM), have limitations when dealing with them due to space restrictions. Other visualization methods involve dimensionality reduction techniques, which can lead to losing important information and reducing the interpretability of the data. In this paper, the Class Separability Visualization (CSViz) method is introduced as a new Visual Analytics (VA) approach to address the challenge of visualizing labeled HDD through subspaces. The proposed method enables an overview of the class separability offering a series of 2-Dimensional subspaces visualizations containing exclusive subsets of points of the original variables that encompass the most valuable and significant separable patterns. The proposed method is tested over 50 datasets with different characteristics providing promising results. In all cases, more than 90% of the data observations are shown with three plots or less. Hence, the presented CSViz significantly eases the EDA by reducing the number of plots to be inspected in a SPLOM and thus, the amount of time invested in it. CSViz graphical abstract [ABSTRACT FROM AUTHOR]

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team's performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling. [ABSTRACT FROM AUTHOR]

Scientific data visualization is a critical aspect of fully understanding data patterns and trends. To date, the majority of data visualizations in nursing research – as with other biomedical fields – have been static. The availability of electronic scientific journal articles (which are quickly becoming the norm) has created new opportunities for dynamic and interactive data visualization which carry added cognitive benefits and support the ability to understand data more fully. Therefore, here we highlight the benefits of R, an open-source programming language, for scientific data visualization, with a specific focus on creating dynamic, interactive figures using the R shiny package. For R users, we have included a tutorial with example code to create three increasingly complex shiny applications. For individuals more interested in understanding the potential of R shiny as an innovative tool to interact with research data, we have included links to online versions of the examples that do not require any programming or R experience. We believe that widespread adoption of dynamic and interactive scientific data visualization will further support nurse scientists' higher-level mission of advancing our understanding of health and wellness of individuals and communities. [ABSTRACT FROM AUTHOR]

Access to corporate information systems by consumers via the Internet has increased dramatically over the past several decades. In a separate organization, extensive research has been conducted on the free flow of information generated by both external and internal keywords. Research on transparency should aid the audience in making informed decisions. Few have, however, created clear and compelling visual representations of transparency requirements (stakeholders, data, process, policy, and their relationships) utilizing current information visualization and visual analytics methodologies. Maintaining both the quality and visual representation of transparency requirements is a difficult challenge. In this paper, we propose TranspVis, a new visual analytics tool designed for transparency analytics. It consists of multiple views that aid domain experts in efficiently analyzing, updating, and saving application transparency datasets. TranspVis is an interactive tool for displaying TranspLan (i.e., Transparency Language) representations manually generated by experts utilizing the Shield, Infolet, and SitReq forms. In addition to the new circle view, TranspVis generates and synchronizes these latter representations automatically. TranspVis is evaluated using AWS and WhatsApp policy datasets as two case studies. Results show that TranspVis extends the initial TranspLan representation and significantly improves transparency requirement analytics in terms of visual encoding, interactions, and insight extraction. [ABSTRACT FROM AUTHOR]

The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure as powerful tools for transforming multidimensional data into visual information according to similarity features. Integrating this class of methods into a framework devoted to data sciences can contribute to generating more expressive means of visual analytics. Although the Principal Component Analysis (PCA) is a well-known method in this context, it is not the only one, and, sometimes, its abilities and limitations are not adequately discussed or taken into consideration by users. Therefore, knowing in-depth multidimensional projection techniques, their strengths, and the possible distortions they can create is of significant importance for researchers developing knowledge-discovery systems. This research presents a comprehensive overview of current state-of-the-art multidimensional projection techniques and shows example codes in Python and R languages, all available on the internet. The survey segment discusses the different types of techniques applied to multidimensional projection tasks from their background, application processes, capabilities, and limitations, opening the internal processes of the methods and demystifying their concepts. We also illustrate two problems, from a genetic experiment (supervised) and text mining (non-supervised), presenting solutions through multidimensional projection application. Finally, we brought elements that reverberate the competitiveness of multidimensional projection techniques towards high-dimension data visualization, commonly needed in data sciences solutions. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the model, inability to reconcile human-ML disagreement, and ethical concerns about oversimplification of complex problems to a single algorithm output. In this paper, we investigate the ML usability challenges that present in the domain of child welfare screening through a series of collaborations with child welfare screeners. Following the iterative design process between the ML scientists, visualization researchers, and domain experts (child screeners), we first identified four key ML challenges and honed in on one promising explainable ML technique to address them (local factor contributions). Then we implemented and evaluated our visual analytics tool, Sibyl, to increase the interpretability and interactivity of local factor contributions. The effectiveness of our tool is demonstrated by two formal user studies with 12 non-expert participants and 13 expert participants respectively. Valuable feedback was collected, from which we composed a list of design implications as a useful guideline for researchers who aim to develop an interpretable and interactive visualization tool for ML prediction models deployed for child welfare screeners and other similar domain experts. [ABSTRACT FROM AUTHOR]

Biologists often perform clustering analysis to derive meaningful patterns, relationships, and structures from data instances and attributes. Though clustering plays a pivotal role in biologists’ data exploration, it takes non-trivial efforts for biologists to find the best grouping in their data using existing tools. Visual cluster analysis is currently performed either programmatically or through menus and dialogues in many tools, which require parameter adjustments over several steps of trial-and-error. In this article, we introduce Geono-Cluster, a novel visual analysis tool designed to support cluster analysis for biologists who do not have formal data science training. Geono-Cluster enables biologists to apply their domain expertise into clustering results by visually demonstrating how their expected clustering outputs should look like with a small sample of data instances. The system then predicts users’ intentions and generates potential clustering results. Our study follows the design study protocol to derive biologists’ tasks and requirements, design the system, and evaluate the system with experts on their own dataset. Results of our study with six biologists provide initial evidence that Geono-Cluster enables biologists to create, refine, and evaluate clustering results to effectively analyze their data and gain data-driven insights. At the end, we discuss lessons learned and implications of our study. [ABSTRACT FROM AUTHOR]

Access to corporate information systems by consumers via the Internet has increased dramatically over the past several decades. In a separate organization, extensive research has been conducted on the free flow of information generated by both external and internal keywords. Research on transparency should aid the audience in making informed decisions. Few have, however, created clear and compelling visual representations of transparency requirements (stakeholders, data, process, policy, and their relationships) utilizing current information visualization and visual analytics methodologies. Maintaining both the quality and visual representation of transparency requirements is a difficult challenge. In this paper, we propose TranspVis, a new visual analytics tool designed for transparency analytics. It consists of multiple views that aid domain experts in efficiently analyzing, updating, and saving application transparency datasets. TranspVis is an interactive tool for displaying TranspLan (i.e., Transparency Language) representations manually generated by experts utilizing the Shield, Infolet, and SitReq forms. In addition to the new circle view, TranspVis generates and synchronizes these latter representations automatically. TranspVis is evaluated using AWS and WhatsApp policy datasets as two case studies. Results show that TranspVis extends the initial TranspLan representation and significantly improves transparency requirement analytics in terms of visual encoding, interactions, and insight extraction.

In March 2019, the first ever act of terrorist violence in New Zealand was live-streamed on social media, making many social media users unwitting witnesses to the massacre on their devices. The Christchurch mosque attacks revealed a particular digital and emotional vulnerability embedded in the digital media infrastructure. The last words of the first victim soon transmorphed into #hellobrother that, as a digital artefact, participated in shaping the emotional landscape. Combining real-time digital media ethnography on Twitter with data science and computational tools, this multi-method study has two aims: first and foremost, to develop and apply new methodology for the study of unexpected, mediated events as they unfold in real time; second, to explore post-death digital artefacts through the concept of digital afterlife that we approach through two complementary perspectives, data afterlife (the technological) and data as afterlife (the emotional). Adopting a relational perspective, we further develop the concept, and highlight the constitutive role of data in the emotional dimension of digital afterlife arising from its capacity to enter affective arrangements. The methodological contributions include development of a conceptual and technological framework for conducting data science as ethnography and the introduction of Tweetboard, a novel artefact for investigating digital afterlife. [ABSTRACT FROM AUTHOR]

Purpose: A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising. Design/methodology/approach: Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions. Findings: Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams. Research limitations/implications: The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper. Originality/value: Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making. [ABSTRACT FROM AUTHOR]

In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to generate end-to-end ML pipelines. While these techniques facilitate the creation of models, given their black-box nature, the complexity of the underlying algorithms, and the large number of pipelines they derive, they are difficult for developers to debug. It is also challenging for machine learning experts to select an AutoML system that is well suited for a given problem. In this paper, we present the Pipeline Profiler, an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning (ML) pipelines produced by AutoML systems. PipelineProfiler is integrated with Jupyter Notebook and can be combined with common data science tools to enable a rich set of analyses of the ML pipelines, providing users a better understanding of the algorithms that generated them as well as insights into how they can be improved. We demonstrate the utility of our tool through use cases where PipelineProfiler is used to better understand and improve a real-world AutoML system. Furthermore, we validate our approach by presenting a detailed analysis of a think-aloud experiment with six data scientists who develop and evaluate AutoML tools. [ABSTRACT FROM AUTHOR]

Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work. [ABSTRACT FROM AUTHOR]

The ability to integrate, manage, and analyze large amounts of data extracted from different sources is becoming a key asset for businesses, organizations, and research institutions that deal with the cultural heritage domain. Nowadays, it is well known that modern technologies and the massive use of mobile devices can contribute to generate an enormous flow of data, whose collection, analysis, and interpretation allows for real-time analysis related to the behaviors, preferences, and opinions of users. In this paper, we present and discuss a data analytics approach relying on an Internet of Things framework. The main goal is to assess how the collection of behavioral IoT data coming from the cultural heritage domain can be opportunely exploited by means of data science and data analytics techniques in order to produce useful insights. Experimental results performed in a real case study demonstrate how the cultural heritage domain, and the related stakeholders, can benefit from these kind of applications. [ABSTRACT FROM AUTHOR]

Persona is a common human-computer interaction technique for increasing stakeholders' understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question. [ABSTRACT FROM AUTHOR]

Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and social- information of 14 million citizens over 22 days. [ABSTRACT FROM AUTHOR]

Years ago, data visualizations were almost exclusively used for decision making and data analysis. However, recently visualizations are more utilized for marketing sales tools and many other tasks; and therefore more memorable visualizations are desirable. Thus, studying memorability and automatically assessing visualizations or images memorability is becoming a challenging topic in computer vision. This topic purpose is to list and analyze the characteristics of memorable visualizations/images, in order to establish a predictive model for memorability. Previous methods mainly use techniques to analyze visualizations memorability. In this paper, we use data related to visual attention combined with image features to significantly study visualizations memorability and construct a representative model of visualization memorability. We test our proposed method on MASSVIS data set, by using different classification methods and we significantly reach performed classification rates. [ABSTRACT FROM AUTHOR]

Data scientists and other analytic professionals often use interactive visualization in the dissemination phase at the end of a workflow during which findings are communicated to a wider audience. Visualization scientists, however, hold that interactive representation of data can also be used during exploratory analysis itself. Since the use of interactive visualization is optional rather than mandatory, this leaves a “visualization gap” during initial exploratory analysis that is the onus of visualization researchers to fill. In this paper, we explore areas where visualization would be beneficial in applied research by conducting a design study using a novel variation on contextual inquiry conducted with professional data analysts. Based on these interviews and experiments, we propose a set of interactive initial exploratory visualization guidelines which we believe will promote adoption by this type of user. [ABSTRACT FROM PUBLISHER]