Visual Analytics Process Research Articles

11. Modern Teaching Methods in Creative Workshop Fashion Design Courses

Abstract The aim of this article is to specify and exemplify the stages of the creative process in fashion design courses and workshops, with a focus on a few learning methods exploring the creative potential through content and visual analysis processes as well as more practical techniques and methods in the area of fashion design and applied arts. From scientific documentation to pedagogical experiments, a range of methods have been used to enable students fulfill their learning objectives and reach the apex of professional competence.

An Analysis of Machine- and Human-Analytics in Classification.

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.

A systematic view on data descriptors for the visual analysis of tabular data

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.

Electrodermal Activity Analysis for Training of Military Tactics

This study examined the differences in the electrodermal activity (EDA) of 19 ROTC cadets viewing a 2D or 3D perspective display. Skin conductance responses (SCRs) were analyzed using a multistep visual analytic process and coded according to participant and condition. Results show non-significant differences SCRs between conditions, however since it is only an initial study, more research needs to be done before definitive conclusions can be made. Future investigations involving mixed method research design are discussed with a specific emphasis on user engagement and workload. The results of this initial study will be incorporated into future research with the United States Military Academy at West Point.

A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process.

Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system.

Characterizing Guidance in Visual Analytics.

Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.

BELIV 2014 Special Issue

The position and research papers submitted to BELIV 2014 were selected according to their novelty, quality, and relevance for visualization evaluation. We were inclusive in our selection and accepted 23 papers with an acceptance rate of 76.6%. The best reviewed articles of this selection have been invited to submit an extended paper to this special issue, leading to five accepted articles in this special issue that discuss a variety of concerns and methodologies dedicated to evaluating visualization and visual analytics systems and processes. These are summarized below.

Modeling and evaluating user behavior in exploratory visual analysis

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This article presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.

Kansei: The Creative Mind In theVisual Analyzing Process

Emotions are complex conscious states that play an essential role in our daily lives. They have a great influence on human perception, particularly concerning our think, learn and decisions. In design, product emotion plays an important role in encouraging product quality and sustainability. Several methods and tools exist and used to assist the process of evaluating users ‘emotional experience with the products. This paper aims to develop the process of evaluating users’ emotional experiences in more accurate semantic dimension through Kansei. Image feature extraction is a key issue for concept recognition in images, and particularly emotions. In analyzing the product images, KJ Methods based on Affective Model used to develop the full range of emotional keywords and their affinity cluster. This method shows the potentiality, limitation and building a new framework in understanding the semantic structure used by the designers in the visual analysis process. The result shows that this method will be a good reference to design studies that involved with users’ subjective emotional experience with product design.

Constructive Visual Analytics for Text Similarity Detection

AbstractDetecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th‐century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.

Temporal Branching Approach for Visual Exploration of Simulation Process in Dynamic Networks

This paper presents a concept for visualization of simulation processes in temporal networks. Core principles are based on interactive real-time visualization of complex networks and dynamic processes. Any modifications in simulation parameters result in division of a timeline into branches. Described concept was integrated into an extended tool for visual analysis and tested on a model of interbank interaction. Proposed developments significantly improve process of visual analysis: pattern detection, search of distinctions and etc.

Dimensions reordering for visual mining of association rules using parallel set

Mining the interesting association rules from data and clusters presents high demands in many application fields, such as telephonic operator, social networks, marketing especially in decision-making process. Exploring the set of extracted rules from the high dimensional data and clusters presents a serious challenge of visual analytic process. This problem becomes more challenging when the data items increase. To deal with this challenge, the use of the interactive multidimensional visualisation technique is necessary. In our work, we base on parallel set visualisation technique as a tool of knowledge discovery tool to mining the set of rules from categorical data. In parallel set, the order and arrangement of dimensions have a major influence on the analytic process, therefore we need to find an effective measure, which can generate an expressive order. This last helps the user to explore and analyse the visual display of data. In this paper, we are interested in analysing data to detect the association rules between dimensions based on our application of entropy measures. These dimensions are reordered while the high number of rules with a high confidence is displayed. It allows the users to visualise easily the important rules in a short time.

Dimensions reordering for visual mining of association rules using parallel set

Mining the interesting association rules from data and clusters presents high demands in many application fields, such as telephonic operator, social networks, marketing especially in decision-making process. Exploring the set of extracted rules from the high dimensional data and clusters presents a serious challenge of visual analytic process. This problem becomes more challenging when the data items increase. To deal with this challenge, the use of the interactive multidimensional visualisation technique is necessary. In our work, we base on parallel set visualisation technique as a tool of knowledge discovery tool to mining the set of rules from categorical data. In parallel set, the order and arrangement of dimensions have a major influence on the analytic process, therefore we need to find an effective measure, which can generate an expressive order. This last helps the user to explore and analyse the visual display of data. In this paper, we are interested in analysing data to detect the association rules between dimensions based on our application of entropy measures. These dimensions are reordered while the high number of rules with a high confidence is displayed. It allows the users to visualise easily the important rules in a short time.

Layers of narratives, images, and analysis

Purpose– In this paper, the authors offer a methodological discussion and examples of visual analysis processes. The purpose of this paper is to illustrate a data analysis method that the authors named layered textural analysis, which brought together images and texts in ways that changed existing and decontextualized understandings. The authors used layered textural analysis to interpret photo-narratives from a photo elicitation study about the development of intercultural sensitivity in US study abroad students.Design/methodology/approach– Layered textural analysis, as carried out in this study, consisted of structural analysis of narratives present in interview text, visual analysis of the photos, and guiding questions related to the content and relationship of the photos and narratives.Findings– Individual experiences, images, and texts reflect complex connections between matter and thought. The multilayered analysis led to complex understandings and representations of participants’ learning and interpretation of cultural differences, and allowed us to examine photo-narrative events and participants’ individual meaning-making processes.Originality/value– Visual researchers rarely write about their processes of analysis in sufficient epistemological and methodological detail. Transparency about data analysis may inspire other scholars to experiment with data analysis approaches. The authors share layered textural analysis so that qualitative researchers can gain ideas for their own reflexive analytic techniques and to exemplify how multilayered analysis methods can change understandings of data and work against simplified knowledges.

Understanding variations in pediatric asthma care processes in the emergency department using visual analytics.

Health care delivery processes consist of complex activity sequences spanning organizational, spatial, and temporal boundaries. Care is human-directed so these processes can have wide variations in cost, quality, and outcome making systemic care process analysis, conformance testing, and improvement challenging. We designed and developed an interactive visual analytic process exploration and discovery tool and used it to explore clinical data from 5784 pediatric asthma emergency department patients.

Topological visual analysis of clusterings in high-dimensional information spaces

Abstract In many applications, domain-specific entities are easily compared and categorized if they are represented as high-dimensional feature vectors. To detect object similarities and to quantify coherent groups, analysts often visualize the vectors directly, aiming to identify clusters visually. However, common visualizations for high-dimensional data often suffer from information loss, occlusions and visual clutter for large and noisy data. In this case, structure is misleading and false insights are derived. We use topological concepts to provide a structural view of the points. We analyze them in their original space and depict their clustering structure using intuitive landscapes. We describe the visual analysis process to define and simplify the structural view and to perform local analysis by linking individual features to other visualizations.

The Effects of Interactive Latency on Exploratory Visual Analysis.

To support effective exploration, it is often stated that interactive visualizations should provide rapid response times. However, the effects of interactive latency on the process and outcomes of exploratory visual analysis have not been systematically studied. We present an experiment measuring user behavior and knowledge discovery with interactive visualizations under varying latency conditions. We observe that an additional delay of 500 ms incurs significant costs, decreasing user activity and data set coverage. Analyzing verbal data from think-aloud protocols, we find that increased latency reduces the rate at which users make observations, draw generalizations and generate hypotheses. Moreover, we note interaction effects in which initial exposure to higher latencies leads to subsequently reduced performance in a low-latency setting. Overall, increased latency causes users to shift exploration strategy, in turn affecting performance. We discuss how these results can inform the design of interactive analysis tools.

Visual Analysis of Public Utility Service Problems in a Metropolis.

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.

Visualizing Partially Ordered Sets for Socioeconomic Analysis

In this paper, we develop a visualization process for partial orders derived from considering many numerical indicators on a statistical population. The issue is relevant, particularly in the field of socio-economic evaluation, where explicitly taking into account incomparabilities among individuals proves much more informative than adhering to classical aggregative and compensative approaches, which collapse complexity into unidimensional rankings. We propose a process of visual analysis based on a combination of tools and concepts from partial order theory, multivariate statistics and visual design. We develop the process through a real example, based on data pertaining to regional competitiveness in Europe.

Visual Analytics for Model Selection in Time Series Analysis

Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles.