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The availability of devices that can record locations and are connected to the Internet creates a huge amount of geospatial data that are continuously streamed. The informative visualization of such data is a challenging problem, given their sheer volume, and the real-time nature of the incoming stream. A simple approach like plotting all datapoints would generate visual noise, and not scale well. To tackle this problem, we have developed a visualization technique based on graceful degradation along three overlaid time periods (ongoing, recent, and history), each with a different visual idiom. A usability test of the proposed technique showed promising results., EuroVis 2021 - Short Papers, Analytics and Applications, 73, 77, João Rafael, João Moreira, Daniel Mendes, Mário Alves, and Daniel Gonçalves, CCS Concepts: Human-centered computing--> Visualization; Visual analytics; Geographic visualization

Ensemble methods are widely used to simulate complex non-linear systems and to estimate forecast uncertainty. However, visualizing and analyzing ensemble data is challenging, in particular when multimodality arises, i.e., distinct likely outcomes. We propose a graph-based approach that explores multimodality in univariate ensemble data from weather prediction. Our solution utilizes clustering and a novel concept of life span associated with each cluster. We applied our method to historical predictions of extreme weather events and illustrate that our method aids the understanding of the respective ensemble forecasts., Machine Learning Methods in Visualisation for Big Data, Papers, 7, 11, Natacha Galmiche, Helwig Hauser, Thomas Spengler, Clemens Spensberger, Morten Brun, and Nello Blaser, CCS Concepts: Applied computing --> Earth and atmospheric sciences; Human-centered computing --> Visual analytics; Computing methodologies --> Unsupervised learning

Techniques for presenting objects spatially via density maps have been thoroughly studied, but there is lack of research on how to display this information in the presence of several classes, i.e., multiclass density maps. Moreover, there is even less research on how to design an interactive visualization for comparison tasks on multiclass density maps. One application domain which requires this type of visualization for comparison tasks is crime analytics, and the lack of research in this area results in ineffective visual designs. To fill this gap, we study four types of techniques to compare multiclass density maps, using car theft data. The interactive techniques studied are swipe, translucent overlay, magic lens, and juxtaposition. The results of a user study (N=32) indicate that juxtaposition yields the worst performance to compare distributions, whereas swipe and magic lens perform the best in terms of time needed to complete the experiment. Our research provides empirical evidence on how to design interactive idioms for multiclass density spatial data, and it opens a line of research for other domains and visual tasks., EuroVis 2021 - Short Papers, Analytics and Applications, 79, 83, Lukas Svicarovic, Denis Parra, and María Jesús Lobo, CCS Concepts: Human-centered computing--> Empirical studies in visualization; Visual analytics

Analyzing body movement as a means of expression is of interest in diverse areas, such as dance, sports, films, as well as anthropology or archaeology. In particular, in choreography, body movements are at the core of artistic expression. Dance moves are composed of spatial and temporal structures that are difficult to address without interactive visual data analysis tools. We present a visual analytics solution that allows the user to get an overview of, compare, and visually search dance move features in video archives. With the help of similarity measures, a user can compare dance moves and assess dance poses. We illustrate our approach through three use cases and an analysis of the performance of our similarity measures. The expert feedback and the experimental results show that 75% to 80% of dance moves can correctly be categorized. Domain experts recognize great potential in this standardized analysis. Comparative and motion analysis allows them to get detailed insights into temporal and spatial development of motion patterns and poses., EuroVis 2021 - Short Papers, Analytics and Applications, 67, 71, Vasiliki Arpatzoglou, Artemis Kardara, Alexandra Diehl, Barbara Flueckiger, Sven Helmer, and Renato Pajarola, CCS Concepts: Human-centered computing--> Visual analytics

We present an analysis of the impact of hyper-parameters for an ensemble of neural networks using tailored visualization techniques to understand the complicated relationship between hyper-parameters and model performance. The high-dimensional error surface spanned by the wide range of hyper-parameters used to specify and optimize neural networks is difficult to characterize - it is non-convex and discontinuous, and there could be complex local dependencies between hyper-parameters. To explore these dependencies, we make use of a large number of sampled relations between hyper-parameters and end performance, retrieved from thousands of individually trained convolutional neural network classifiers. We use a structured selection of visualization techniques to analyze the impact of different combinations of hyper-parameters. The results reveal how complicated dependencies between hyper-parameters influence the end performance, demonstrating how the complete picture painted by considering a large number of trainings simultaneously can aid in understanding the impact of hyper-parameter combinations., Machine Learning Methods in Visualisation for Big Data, Papers, 13, 17, Daniel Jönsson, Gabriel Eilertsen, Hezi Shi, Jianmin Zheng, Anders Ynnerman, and Jonas Unger, CCS Concepts: Computing methodologies --> Neural networks; Human-centered computing --> Visual analytics

Memory leaks occur when no longer needed objects are unnecessarily kept alive. They can have a significant negative performance impact, leading to a crash in the worst case. Thus, tool support for heap evolution analysis, especially memory leak analysis, is essential. Unfortunately, most memory analysis tools lack advanced visualizations to facilitate this task. In this paper, we present an approach to use well-known tree visualization techniques for memory growth visualization. Our approach groups heap objects into memory trees based on a user-defined set of properties such as their types or their allocation sites at multiple points in time. We present two novel approaches to inspect how these trees evolve over time: In our time-travelbased visualization, a single space-filling tree visualization shows the monitored application's heap memory at a given point in time. Users can step back and forth in time, causing the visualization to update itself. In our timeline-based visualization, a time-series chart depicts the overall memory consumption over time. Above this chart, multiple memory tree visualizations are shown side-by-side for a number of user-selected points in time. Using these techniques to visually inspect the evolution of the heap over time should enable users to gain new insights and to detect (problematic) memory trends in their applications. To demonstrate the feasibility and applicability of the presented approach, we integrated it into AntTracks, a trace-based memory monitoring tool and applied it in two memory leak case studies., Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference, Tools, 63, 75, Markus Weninger, Lukas Makor, and Hanspeter Mössenböck, CCS Concepts: General and reference --> Performance;Software and its engineering --> Software performance; Software maintenance tools;Information systems --> Data analytics; Information extraction;Human-centered computing --> Interactive systems and tools; Visualization techniques; Visual analytics; Information visualization

Visual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts' expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15,000 pairs of scatterplots with human annotations on the perceived correlation between the data dimensions. Afterwards, we trained two different Convolutional Neural Networks (CNN), one extracts features from scatterplot images and the other directly from data vectors. We evaluated both CNNs on our test set and compared them to previous visual quality metrics. The experiments show that our new metric is able to represent human perception more accurately than previous methods., Vision, Modeling and Visualization, Machine Learning in Vision and Analysis, 55, 62, Leslie Wöhler, Yuxin Zou, Moritz Mühlhausen, Georgia Albuquerque, and Marcus Magnor, CCS Concepts: General and reference --> Metrics; Human-centered computing --> Visual analytics; Computing methodologies --> Perception; Neural networks

International audience; We introduce IATK, the Immersive Analytics Toolkit, a software package for Unity that allows interactive authoring and exploration of data visualisation in immersive environments. The design of IATK was informed by interdisciplinary expert-collaborations as well as visual analytics applications and iterative refinement over several years. IATK allows for easy assembly of visualisations through a grammar of graphics that a user can configure in a GUI— in addition to a dedicated visualisation API that supports the creation of novel immersive visualisation designs and interactions. IATK is designed with scalability in mind, allowing visualisation and fluid responsive interactions in the order of several million points at a usable frame rate. This paper outlines our design requirements, IATK’s framework design and technical features, its user interface, as well as application examples.

Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. We aim to support production data exploration in the aluminum industry. To this end, we developed the first version of the interactive visual analytics tool ADAM. The main aspect of concern is product quality, which is obtained from the quality inspection of aluminum plates at the end of the production process. A set of tightly linked views of production parameters with cross-filtering capability support the inspection of factors possibly influencing the product quality. ADAM allows highly responsive forward and backward search in the quality and production parameter space, leading to an understanding of important parameters, and supporting production planning and process improvement. Our approach was designed in an iterative development cycle guided by domain requirements from a major aluminum producer. We introduce the domain problem, propose a visual analytics design to support the problem, and demonstrate by application to real production data the usefulness and possible insights which can be obtained., EuroVis 2019 - Posters, Posters, 49, 51, Nikolina Jekic, Belgin Mutlu, Mario Faschang, Steffen Neubert, Stefan Thalmann, and Tobias Schreck, CCS Concepts: Human-centered computing --> Visual Analytics; Data Exploration

A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases., EuroVis Workshop on Visual Analytics (EuroVA), Visual Analytics Methods, 25, 29, Marco Angelini, Thorsten May, Giuseppe Santucci, and Hans-Jörg Schulz, CCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Progressive computation

Clinicians and hygienists need to know how an infection of one patient could be transmitted among other patients in the hospital (e.g., to prevent outbreaks). They need to analyze how many and which patients will possibly be infected, how fast the infection could spread, and which contacts are likely to transfer the infections within the hospital. Currently, infection contagion is modeled and visualized for populations only on an aggregate level, without identification and exploration of possible infection between individuals. We present a novel visual analytics approach that simulates the contagion in a contact graph of patients in a hospital. We propose a clustering approach to identify probable contagion scenarios in the simulation ensemble. Furthermore, our novel visual design for detailed assessment of transmission shows the temporal development of contagion per patient in one view. We demonstrate the capability of our approach to a real-world use case in a German hospital., Vision, Modeling and Visualization, Visualization and Visual Analytics, 143, 150, Marcel Wunderlich, Isabelle Block, Tatiana von Landesberger, Markus Petzold, Michael Marschollek, and Simone Scheithauer, CCS Concepts: Human-centered computing --> Visualization; Graph drawings; Visual analytics

The automatic recognition of natural structures is a challenging task in the supervised learning field. Complex morphologies are difficult to detect both from the networks, that may suffer from generalization issues, and from human operators, affecting the consistency of training datasets. The task of manual annotating biological structures is not comparable to a generic task of detecting an object (a car, a cat, or a flower) within an image. Biological structures are more similar to textures, and specimen borders exhibit intricate shapes. In this specific context, manual labelling is very sensitive to human error. The interactive validation of the predictions is a valuable resource to improve the network performance and address the inaccuracy caused by the lack of annotation consistency of human operators reported in literature. The proposed tool, inspired by the Yes/No Answer paradigm, integrates the semantic segmentation results coming from a CNN with the previous human labeling, allowing a more accurate annotation of thousands of instances in a short time. At the end of the validation, it is possible to obtain corrected statistics or export the integrated dataset and re-train the network., Eurographics 2019 - Short Papers, Learning and Networks, 57, 60, Gaia Pavoni, Massimiliano Corsini, Marco Palma, and Roberto Scopigno, CCS Concepts: Human-centered computing --> Graphical user interfaces; Visual analytics; Computing methodologies --> Image segmentation

This paper attempts to explain the mechanics of writing a survey paper in data visualization or visual analytics. It serves as a useful starting point for those who have never written a survey paper or have very little experience. A literature review or survey paper is often considered the starting point of a PhD candidate's scientific degree. However, there are no dedicated papers that focus on guidelines for the planning or writing of a survey paper or literature review in visualization or visual analytics. We provide guidelines and our recommendations for a foundational structure on which to build a survey paper, whilst also considering intermediate goals, and offer helpful advice to improve the survey process and literature analysis. The result is a useful starting point for those wishing to write a survey paper or state-of-the-art (STAR) review in visualization or visual analytics. The guidelines and recommendations we make can also be generalized to other areas of computing and science. An abstract is a required feature of a survey paper and should identity the topic of the literature review. A good abstract addresses why the given topic is interesting and why it is helpful. A good abstract features the following elements: (1) topic introduction, (2) the motivation, (3) the goal of the review, and the benefits the review provides to the reader. A good literature survey offers a helpful classification of the literature, mature areas of research, and open, unsolved problems in visualization or visual analytics., Eurographics 2019 - Education Papers, Tool up for CG Education, 29, 39, Liam McNabb and Robert S. Laramee, CCS Concepts: General and reference --> Surveys and overviews; Reference works; Human-centered computing --> Visual analytics; Information visualization

The research on Massive Open Online Course (MOOC) has mushroomed worldwide due to the technical revolution and its unprecedented enrollments. Existing work mainly focuses on performance prediction, content recommendation, and learning behavior summarization. However, finding anomalous learning activities in MOOC data has posed special challenges and requires providing a clear definition of anomalous behavior, analyzing the multifaceted learning sequence data, and interpreting anomalies at different scales. In this paper, we present a novel visual analytics system, MOOCad, for exploring anomalous learning patterns and their clustering in MOOC data. The system integrates an anomaly detection algorithm to cluster learning sequences of MOOC learners into staged-based groups. Moreover, it allows interactive anomaly detection between and within groups on the basis of semantic and interpretable group-wise data summaries. We demonstrate the effectiveness of MOOCad via an in-depth interview with a MOOC lecturer with real-world course data., EuroVis 2019 - Short Papers, Web Interfaces and Learning, 91, 95, Xing Mu, Ke Xu, Qing Chen, Fan Du, Yun Wang, and Huamin Qu, CCS Concepts: Human-centered computing --> Visual analytics; Applied computing --> E-learning

It is essential to assess the trustworthiness of the machine learning models when deploying them to real-world applications, such as healthcare and risk management, in which domain experts need to make critical decisions. We propose a visual analysis method for supporting domain experts to understand and improve a given machine learning model based on a model-agnostic interpretable explanation technique. Our visualization method provides a heat map matrix as an overview of the model explanation and helps efficient feature engineering and data cleaning. We demonstrate our visualization method on a text classification task., EuroVis 2019 - Posters, Posters, 37, 39, Shoko Sawada and Masashi Toyoda, CCS Concepts: Human-centered computing --> Heat maps; Visual analytics; Computing methodologies --> Machine learning

Traffic congestion causes major economic, environmental and social problems in modern cities. We present an interactive visualization tool to assist domain experts on the identification and analysis of traffic patterns at a city scale making use of multivariate empirical urban data and fundamental diagrams. The proposed method combines visualization techniques with an improved local principle curves method to model traffic dynamics and facilitate comparison of traffic patterns - resorting to the fitted curve with a confidence interval - between different road segments and for different external conditions. We demonstrate the proposed technique in an illustrative real-world case study in the city of Porto, Portugal., Machine Learning Methods in Visualisation for Big Data, Papers, 13, 17, Carla Silva, Pedro M. d'Orey, and Ana Aguiar, CCS Concepts: Human-centered computing --> Visual analytics; Empirical studies in visualization; Computing methodologies --> Machine learning approaches; Modeling and simulation; Shape modeling"

This work presents a new approach to visually summarize large micro-document collections such as tweets. We extract frequent patterns of phrases as shortened quotes to present analysts an overview of popular snippets and statements, enabling more specific insights into large text collections compared to keyword-based visualizations. In our hierarchical structure, each quote can be the starting point to extract more fine-grained patterns on a subset of sentences that match the parent pattern. We show that our approach is scalable by applying it to millions of tweets., EuroVis 2019 - Posters, Posters, 13, 15, Johannes Knittel, Steffen Koch, and Thomas Ertl, CCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Information extraction

The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics., EuroVis 2019 - Short Papers, Design and Evaluation, 19, 23, Luana Micallef, Hans-Jörg Schulz, Marco Angelini, Michaël Aupetit, Remco Chang, Jörn Kohlhammer, Adam Perer, and Giuseppe Santucci, CCS Concepts: Human-centered computing --> Visual analytics; Social and professional topics --> User characteristics

In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to be quantified initially. We address this challenge by formalizing the quantification of uncertainty for multivariate time series preprocessing. To tackle the large design space, we elaborate key considerations for quantifying and aggregating uncertainty. We provide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess the effectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty., EuroVis Workshop on Visual Analytics (EuroVA), Visual Analytics Methods, 31, 35, Christian Bors, Jürgen Bernard, Markus Bögl, Theresia Gschwandtner, Jörn Kohlhammer, and Silvia Miksch, CCS Concepts: Mathematics of computing --> Time series analysis; Information systems --> Uncertainty; Human-centered computing --> Visualization theory, concepts and paradigms; Visual analytics; Computing methodologies --> Uncertainty quantification

Clinicians and hygienists need to identify outbreaks and transmission patterns of pathogen infections among hospital patients. Such analysis requires the combination of the microbiological laboratory results with the location and contacts among patients. Currently, this is a cumbersome manual and time-consuming task that includes reading multiple textual reports. We present a visual-interactive interface that offers a set of linked visualizations for the identification of outbreaks and patient contacts. The evaluation of our interface with clinicians and hygienists has shown a high applicability for the task and ease of use., EuroVis 2019 - Posters, Posters, 9, 11, Tatiana von Landesberger, Marcel Wunderlich, Tom Baumgartl, Markus Höhn, Michael Marschollek, and Simone Scheithauer, CCS Concepts: Human-centered computing --> Information visualization; Visual analytics

We usually use a text-based search engine while surveying research papers. Such search systems have difficulties for novice researchers in case they do not know appropriate keywords or do not understand the positions of papers. Many visualization tools of citation networks have been proposed to help this task. These tools demonstrated that not only text information of papers but citation relationships and co-author relationships also are helpful clues for research survey. We propose CoCoa, a linked network visualization of co-citation and co-author relationships for surveying research papers. Our system visualizes both citation and co-author networks at the same time. To make comparison and grasp of correspondence between co-citation and co-author networks easier, the system treats both a paper and an author as bags of words and cluster them into topics applying LDA (Latent Dirichlet Allocation) at the same time. Based on the clustering result, it places the clusters of a citation network by a hybrid force-directed and space-filling algorithm. The position of topic clusters in the networks would have an influence on the correspondence of a particular topic in the networks. Our system extracts the clusters which consist of the common combinations of topics in two networks. Then it reuses the positions of the clusters in a citation network as the initial cluster positions of a co-author network, supposing there are a large number of authors., EuroVis 2019 - Short Papers, Text Visualization and Applications, 109, 113, Rina Nakazawa, Takayuki Itoh, and Takafumi Saito, CCS Concepts: Human-centered computing --> Visual analytics; Visualization; Information visualization

We present an interactive visual environment for linked analysis of brain imaging and clinical measurements. The environment is developed in an iterative participatory design process involving neuroscientists investigating the causes of brain-related complex diseases. The hypotheses formation process about correlations between active brain regions and physiological or psychological factors in studies with hundreds of subjects is a central part of the investigation. Observing the reasoning patterns during hypotheses formation, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration, thus limiting the scientific scope and preventing extraction of knowledge from available data. Based on these observations, we designed methods that support neuroscientists by integrating their existing statistical analysis of multivariate subject data with interactive visual exploration to enable them to better understand differences between patient groups and the complex bidirectional interplay between clinical measurement and the brain. These exploration concepts enable neuroscientists, for the first time during their investigations, to interactively move between and reason about questions such as 'which clinical measurements are correlated with a specific brain region?' or 'are there differences in brain activity between depressed young and old subjects?'. The environment uses parallel coordinates for effective overview and selection of subject groups, Welch's t-test to filter out brain regions with statistically significant differences, and multiple visualizations of Pearson correlations between brain regions and clinical parameters to facilitate correlation analysis. A qualitative user study was performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights, and is an effective support tool for hypothesis formation., Eurographics Workshop on Visual Computing for Biology and Medicine, Visual Analytics in Medicine and Biology, 57, 68, Daniel Jönsson, Albin Bergström, Camilla Forsell, Rozalyn Simon, Maria Engström, Anders Ynnerman, and Ingrid Hotz, CCS Concepts: Human-centered computing --> Scientific visualization; Visual analytics; Applied computing --> Imaging

Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very different domains (electronic health records and cybersecurity) to investigate how researchers can gain breakthrough insights by combining multiple event mining methods in a visual analytics workflow. The aim of the health case study was to identify patterns of missing values, which was daunting because the 615 million missing values occurred in 43,219 combinations of fields. However, a workflow that involved exclusive set intersections (ESI), frequent itemset mining (FIM) and then two more ESI steps allowed us to identify that 82% of the missing values were from just 244 combinations. The cybersecurity case study's aim was to understand users' behavior from logs that contained 300 types of action, gathered from 15,000 sessions and 1,400 users. Sequential frequent pattern mining (SFPM) and ESI highlighted some patterns in common, and others that were not. For the latter, SFPM stood out for its ability to action sequences that were buried within otherwise different sessions, and ESI detected subtle signals that were missed by SFPM. In summary, this paper demonstrates the importance of using multiple perspectives, complementary set mining methods and a diverse workflow when using visual analytics to analyze complex event data., EuroVis Workshop on Visual Analytics (EuroVA), Analyzing Movement and Events, 61, 65, Muhammad Adnan, Phong H. Nguyen, Roy A. Ruddle, and Cagatay Turkay, CCS Concepts: Human-centered computing --> Visual analytics

We contribute a systematic review of online visualizations of the Bitcoin blockchain. Bitcoin is currently the most active cryptocurrency with the largest market share among other cryptocurrencies. It has attracted a large user base and more and more businesses are beginning to accept Bitcoin as payment. While there are still relatively few visualization research papers on Bitcoin, a growing number of online tools visualize data about the Bitcoin blockchain. We provide a first systematic assessment of these online tools to inform future research efforts on making the Bitcoin blockchain more accessible., EuroVis 2019 - Posters, Posters, 69, 72, Natkamon Tovanich, Nicolas Heulot, Jean-Daniel Fekete, and Petra Isenberg, CCS Concepts: Human-centered computing --> Visual analytics; Information visualization

We present a time-scalable approach for visualizing dynamic graphs. By adopting bipartite graph layouts known from parallel edge splatting, individual graphs are horizontally stacked by drawing partial edges, leading to stacked edge splatting. This allows us to uncover the temporal patterns together with achieving time-scalability. To preserve the graph structural information, we introduce the representative graph where edges are aggregated and drawn at full length. The representative graph is then placed on the top of the last graph in the (sub)sequence. This allows us to obtain detailed information about the partial edges by tracing them back to the representative graph. We apply sequential temporal clustering to obtain an overview of different temporal phases of the graph sequence together with the corresponding structure for each phase. We demonstrate the effectiveness of our approach by using real-world datasets., Vision, Modeling and Visualization, Information and Geographic Visualization, 127, 134, Moataz Abdelaal, Marcel Hlawatsch, Michael Burch, and Daniel Weiskopf, CCS Concepts: Human-centered computing --> Information visualization; Visual analytics

Decoration in Ancient Egyptian funerary monuments represents an important part of the monuments layout. Beside scenes displaying the owner, scenes of daily life, fabrication, offerings and hieroglyphic texts are found. Not only topics of self-presentation but also decoration for the translation to the afterlife were predominant. Therefore, this paper suggests approaches to analyze distribution and occurrence of decorations of several ancient monuments of the same style, with data visualization and quantitative methods. As a case study ancient Egyptian funerary monuments of High Officials from the Late Period, twenty-fifth to twenty-sixth dynasty, were studied. The decorative scenes were categorized and tagged in terms of their content. The positions in the monument were highlighted and included in abstracted 3-dimensional models. This computational implementation offers users to search for decoration categories, highlight, locate and finally compare the position of a scene between the monuments. The visualized data include the position and orientation of a categorized scene in the monument, their occurrence and distribution among the analyzed monuments. In a further step the analysis data was studied statistically in order to be able to query detailed results of the prevalence, distribution and preservation of decorations and specific scenes. Both introduced solutions provide a user friendly information interface to visualize, compare and request quantitative data., Eurographics Workshop on Graphics and Cultural Heritage, Visualization and Visual Analytics for CH, 113, 117, Anja Wutte, CCS Concepts: Mathematics of computing --> Statistical software; Human-centered computing --> Visual analytics

Developing a positive relationship between a business and its customers is vital to success. The outcome of any customer interaction can determine future patronage of the business. Many industry's only point of interaction with their customers is through a contact centre where everything from sales to complaints are handled. This places tremendous importance on the operational efficiency of the contact centre and the level of care provided to the customers. These customer interactions are recorded and archived in large databases, but undertaking insightful analysis is challenging due to both the size and complexity of the data. We present a visual solution to the tracking of customer interactions at a large scale. RiverState visualises the collective flow of callers through the process of interacting with a contact centre using a river metaphor. We use finite state transition machines with customised edges to depict millions of events and the states callers go through to complete their journey. We implement a range of novel features to enhance the analytical qualities of the application, and collect feedback from domain experts to analyse and evaluate the use of the software., Computer Graphics and Visual Computing (CGVC), Visualization I, 81, 89, Richard C. Roberts, Dylan Rees, Robert S. Laramee, Paul Brookes, and Gary A. Smith, CCS Concepts: Human-centered computing --> Visual analytics; Visualization techniques; Information visualization; Mathematics of computing --> Exploratory data analysis