Implementing Visual Analytics Pipelines with Simulation Data

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

The 21st century will be heavily impacted by the capability to create value from recorded data of all kinds. In this regard, industrial manufacturing intuitions increasingly rely on new data-driven technologies, such as the Internet of Things or Machine Learning. In terms of data collection, manufacturing processes increasingly include sophisticated sensor equipment, which results in interconnected networks of manufacturing all assembling parts and producing data. However, manufacturing institutions currently face two challenges. First, large amounts of parts and hence data are produced during fully automated manufacturing processes. Second, due to the overwhelming amount of recorded data, it is particularly challenging to efficiently analyze manufacturing data. Hence, it is important to efficiently store and share gained knowledge from performed data analyses. Information visualization and Visual Analytics are two prolific branches of data analysis exploiting sophisticated visualization techniques to support the execution of analytical tasks and to store gained knowledge. This thesis looks at how data visualization approaches can help industrial manufacturing organizations to create value from large amounts of manufacturing data, as well as how to efficiently store and share knowledge from manufacturing data analyses. The goal is to understand how Visual Analytics can improve manufacturing processes in the context of knowledge management. Five Visual Analytics systems were designed, developed, and evaluated to tackle different domain problems that emerged from manufacturing setups. Findings from each system were used to carry out additional studies to enhance established theories of knowledge management. As a result, five success stories are provided of how Visual Analytics can significantly improve manufacturing processes and how knowledge can be efficiently created, formalized, and shared in an organization with Visual Analytics.

Deep-learning-based visual data analytics for smart construction management

Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

The Conference on Visualization and Data Analysis (VDA) 2022 covers all research, development, and application aspects of data visualization and visual analytics. Since the first VDA conference was held in 1994, the annual event has grown steadily into a major venue for visualization researchers and practitioners from around the world to present their work and share their experiences. We invite you to participate by submitting your original research as a full paper, for an oral or interactive (poster) presentation, and attending VDA 2022.

A theoretical model for pattern discovery in visual analytics

Data-driven approaches to problem solving and data analysis are becoming more and more important problems to consider and on which apply research ideas. In this respect, the capability to explore data, understands how algorithmic approaches work and steer them toward the desired goals make Visualization and Visual Analytics strong research fields in which to invest efforts. While this importance has been understood by several countries (e.g., USA, Germany, France) in Italy the research efforts in these fields are still disjointed. With the first edition of the ITA.WA. (Italian Visualization & Visual Analytics) workshop the goal is to make a step toward the creation of an Italian research community on these topics, allowing identification of research direction, joining forces in achieving them and developing common guidelines and programs for teaching activities.

Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data visualization as a distinct field. As such, the number of visualization resources and the demand for those resources is increasing at a rapid pace. After a decades-equivalent long search process, we present a survey of open visualization resources for all those with an interest in interactive data visualization and visual analytics. Because the number of resources is so large, we focus on collections of resources, of which there are already many ranging from literature collections to collections of practitioner resources. Based on this, we develop a classification of visualization resource collections with a focus on the resource type, e.g. literature-based, web-based, developer focused and special topics. The result is an overview and details-on-demand of many useful resources. The collection offers a valuable jump-start for those seeking out data visualization resources from all backgrounds spanning from beginners such as students to teachers, practitioners, developers, and researchers wishing to create their own advanced or novel visual designs. This paper is a response to students and others who frequently ask for visualization resources available to them.

We present an initial design framework for immersive analytics based on Brehmer and Munzner’s “What-Why-How” data visualisation framework. We extend their framework to take into account Who are the people or teams of people who are going to use the system, and Where is the system to be used and what are the available devices and technology. In addition, the How component is extended to cater for collaboration, multisensory presentation, interaction with an underlying computational model, degree of fidelity and organisation of the workspace around the user. By doing so we provide a framework for understanding immersive analytics research and applications as well as clarifying how immersive analytics differs from traditional data visualisation and visual analytics.

Business analytics involves using various technologies to analyze data. Data mining focuses on the automated search for knowledge, patterns, or regularities in data. As a business analyst, it is essential to recognize the type of analytical technique appropriate for solving a specific problem. Exploratory Data Analysis (EDA) describes data using statistical and visualization techniques to highlight important aspects of that data for further analysis. This involves examining a data set from many angles, describing it, and summarizing it without making assumptions about its content. Exploratory data analysis is an essential step before diving into statistical modeling or machine learning to ensure that the data is really what it claims to be and that there are no apparent errors. This type of analysis should be part of data science projects in every organization. Visual analytics is sometimes confused with data visualization. Visual analysis is not simply a matter of graphically representing data. Modern, interactive visual analytics makes combining data from multiple sources easy and performs in-depth data analysis directly in the visualization. Additionally, artificial intelligence and machine learning algorithms can offer recommendations for exploration. Ultimately, visual analytics helps transform massive data sets into business insights that can positively impact an organization. Considering the previous comment about visual data analysis, it should be added that the system has extensive capabilities to create graphical dashboards containing reports and charts. It is essential that in the case of desktops, in addition to the visualizations included in the system itself, it is possible to embed reports from third-party tools.

Making students aware of eye tracking technologies can have a great benefit on the entire application field since they may build the next generation of eye tracking researchers. On the one hand students learn the usefulness and benefits of this technique for different scientific purposes like user evaluation to find design flaws or visual attention strategies, gaze-assisted interaction to enhance and augment traditional interaction techniques, or as a means to improve virtual reality experiences. However, on the other hand, the large amount of recorded data means a challenge for data analytics in order to find rules, patterns, but also anomalies in the data, finally leading to insights and knowledge to understand or predict eye movement patterns which can have synergy effects for both disciplines - eye tracking and visual analytics. In this paper we will describe the challenges of teaching eye tracking combined with visual analytics in a computer and data science bachelor course with 42 students in an active learning scenario following four teaching stages. Some of the student project results are shown to demonstrate learning outcomes with respect to eye tracking data analysis and visual analytics techniques.

In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).

Visual Analytics is successfully employed for an integrated data analysis by means of combining visual and analytical methods. The starting point for current Visual Analytics tools and workflows is usually the readily available data set. Rarely though, Visual Analytics goes beyond the data set and also incorporates the data generating processes that have led to the data in the first place into the analysis. And indeed, in many use case scenarios, this is hardly possible, as these processes cannot be captured as data to be analyzed themselves. Yet for the applications, in which this is feasible, new opportunities and challenges open up.In this paper, we illustrate these opportunities and challenges by our efforts to bring together Visual Analytics and stochastic simulation for cell biological applications. The integration of both is possible, as the data generating process runs in silico and can thus be captured and analyzed alongside the mere simulation result. For this, we present solutions and tools, which permit Visual Analytics on all stages of this particular data generating process -- on the stages of the model, the experiment, the simulation runs, and a combination of all three.

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

A novel and alternate field-wide well integrity management system is presented that uses historical wireline acquired well integrity data along with relevant static and dynamic data to generate visually interactive and customizable well integrity maps, reports, and plots that could facilitate field development planning and reduce surveillance and well intervention efforts and costs. The initial system has been designed using two commonly acquired types of well integrity data, namely corrosion and cement evaluation data. However, the proposed solution can be extended to incorporate any type of cased hole or open-hole data. The key components of this novel system are a centralized database for all related data, customizable corrosion and cement quality maps, field characterization maps, candidate ranking lists, and a centralized repository for all historical processed logs and reports. To ensure the accuracy of the data visualization and data analytics, all the acquired wireline logs are first quality-checked, processed, and interpreted on a processing and interpretation platform such as Techlog (SLB software). The results are then organized and stored on an Oil Field Manager (OFM, SENSIA Software) compatible database such as Microsoft (MS) Access. Finally, the results are pushed to a visualization platform, such as OFM for subsequent data visualization and analytics. This management system aims to address the challenges associated with well integrity data. This data comes from many sources, uses different technologies, is acquired by different vendors, and as a result is stored in different formats. This makes it cumbersome for operators to extract actionable information from this data efficiently and accurately. The deployment of the proposed management system provides operators with a high-level view of the present well integrity status of the field, contains information on the well integrity history of the field, as well as has the potential to predict future well integrity related issues. Integration of relevant static and dynamic data also facilitates a deeper understanding of the field-wide well integrity by facilitating correlations between different well-level parameters. Based on all the data inputs and subsequent outputs, a candidate ranking system is generated which ranks the wells based on the prevalence and seriousness of well integrity issues, thereby alerting operators to take appropriate action. A future use-case of these maps is linkage with dynamic databases, which will enable continuous data flow through surface sensors and gauges. This will provide operators with a live, integrated snapshot of the production and well integrity performance of their assets. This novel well integrity management system is conceptualized to shift operators' focus from data gathering to data analytics by leveraging and integrating multiple sources of massive data and providing operators with a high-level snapshot of the well integrity status of the entire field. This system is envisioned to improve efficiency, optimize workovers, and greatly reduce the carbon footprint associated with well intervention operations. Eventually, through enhanced data analytics, operators can exercise greater control over the well integrity of their fields.