Iceberg Sensemaking: A Process Model for Critical Data Analysis.

This research aims to model the process of data visualization (DV) and design to facilitate computational thinking (CT) of secondary-level students. As an interdisciplinary method for visualizing complex data, creating data visualizations requires learners to analyze data from knowledge of pure and applied science. This study investigates creating DV to facilitate CT as an interdisciplinary method which combines mathematics and information technology. The study was carried out using synthesised design-based research (DBR) method by conducting two cycles comprised of five phases: problem analysis within real-life context, design solution, develop solution, evaluate in practice and reflection. CT dimensions (decomposition, pattern recognition, abstraction, and algorithm design) were tested by using the Computational Thinking through Data Visualization Rubric and reflection form acquired during implementation. The sample consisted of secondary school students in the data visualization creation process with 27 in the first cycle and 31 in the second cycle. This research proposes a model to facilitate development of CT by DV with the analysis of complex data, creating an effective method by enabling analytics and visualizing data. The proposed DV development process facilitating CT has the potential to inform research on interdisciplinary learning environments.

This work studies the problem of clustering one-dimensional data points such that they are evenly distributed over a given number of low variance clusters. One application is the visualization of data on choropleth maps or on business process models, but without over-emphasizing outliers. This enables the detection and differentiation of smaller clusters. The problem is tackled based on a heuristic algorithm called DDCAL (1d distribution cluster algorithm) that is based on iterative feature scaling which generates stable results of clusters. The effectiveness of the DDCAL algorithm is shown based on 5 artificial data sets with different distributions and 4 real-world data sets reflecting different use cases. Moreover, the results from DDCAL, by using these data sets, are compared to 11 existing clustering algorithms. The application of the DDCAL algorithm is illustrated through the visualization of pandemic and population data on choropleth maps as well as process mining results on process models.

Traditional public health surveillance systems would benefit from near real-time data integration and visualization that combines information from traditional and internet sources. In this paper, we describe a prototype system implemented to better automate the process of data collection, analysis and visualization of Influenza-Like Illness surveillance data. This approach enables timelier responses to abnormal events such as clusters, outbreaks and trends.

In the past year of 2020, life in the post-epidemic era continues to move towards cyberspace. The surge in magnitude and value of data has also brought more data abuse, attacks and thefts, and data security issues have become particularly prominent. Moreover, in recent years, data attacks have become more subtle and complex, making traditional security protection methods stretched. Therefore, in view of the data security threats under the new situation, it has become urgent to study new security protection methods. Due to the intuitive sensory advantages of visual analysis, security visual analysis is gradually becoming an effective means to guarantee data security and gradually arouses people's attention. As a cross-cutting research field, network security data visualization can effectively improve the perception and understanding of network security experts in the process of analyzing network security issues by providing information and interactive visualization methods. First, we review the methods of network security data visualization and analyze the basic methods and processes of visualization after investigating the current research status of network security data visualization. Then we introduce data visualization techniques suitable for network security and analyze the existing problems. Finally, we make a conclusion for future trends.

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.

Recent advances in unsteady flow visualization

With the rapid development of the Internet, many enterprises have launched their network platforms. When users browse, search, and click the products of these platforms, most platforms will keep records of these network behaviors, these records are often heterogeneous, and it is called log data. To effectively to analyze and manage these heterogeneous log data, so that enterprises can grasp the behavior characteristics of their platform users in time, to realize targeted recommendation of users, increase the sales volume of enterprises’ products, and accelerate the development of enterprises. Firstly, we follow the process of big data collection, storage, analysis, and visualization to design the system, then, we adopt HDFS storage technology, Yarn resource management technology, and gink load balancing technology to build a Hadoop cluster to process the log data, and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the results. Finally, the obtained results are displayed visually, and a log data analysis system is successfully constructed. It has been proved by practice that the system effectively realizes the collection, analysis and visualization of log data, and can accurately realize the recommendation of products by enterprises. The system is stable and effective.

Three-dimensional three-phase model for simulation of hydrodynamics, oxygen mass transfer, carbon oxidation, nitrification and denitrification in an oxidation ditch

Monitoring the spectrum utilization and capturing the available spectrum bands is the groundwork for the Cognitive Radio (CR) technologies. Because the spectrum management has been a politically as well as a technically sensitive issue, it is usually very hard to access the open data of real spectrum usage. In this sense, we believe grass-roots data collections are important. For these collections to be worthy of spectrum investigations, we need to reduce several noises inserted during monitoring because of the diversity of measurement environments. In addition to this, data visualization must be provided end users with intuitive understanding about the results of the analysis. This paper presents the basic requirements for the data format of grass-roots spectrum data collections and the visualization process using Virtual Earth platform to be accessed from the public. We then show a prototype system named SpectrumMap to aim at visualizing location-based or time-based information of spectrum resources and promoting research on the future CR solutions with user-friendly interfaces. Integrating spectrum analyzer, GPS (Global Positioning System) and the Virtual Earth platform, a mashup of spectrum utilization in two-dimensional (2D) and three-dimensional (3D) views is demonstrated for mobile as well as static environments.

With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of the reality of ocean data visualization results and the real-time interaction in the visualization process. Aiming at the challenges of 3D interactive key technology and GPU-based visualization algorithm technology, we developed a visualization system for large-scale 3D marine environmental data. The system realizes submarine terrain rendering, contour line visualization, isosurface visualization, section visualization, volume visualization and flow field visualization. In order to manage and express the data in the system, we developed a data management module, which can effectively integrate a large number of marine environmental data and manage them effectively. We developed a series of data analysis functions for the system, such as point query and line query, local analysis and multi-screen collaboration, etc. These functions can effectively improve the data analysis efficiency of users and meet the data analysis needs in multiple scenarios. The marine environmental data visualization system developed in this paper can efficiently and intuitively simulate and display the nature and changing process of marine water environmental factors.

Data visualization becomes an arising need to the end-users to assess, analyze, and reveal the data's insights, especially in the Big Data age. The amount of data generated by platforms and systems over the world increases rapidly. As an illustration, in Social Media and e-Science, domain data increases at a tremendous amount. Data provenance is a sort of metadata on the origin and life cycle of data is the issue of both domains for data analysis. Therefore, this requires further researches to ensure better understanding and efficient data interpretation. Our former comprehensive study has addressed several visualization approaches to meet these needs, such as data summarization and graph comparison in the e-Science domain. Proposed approaches implemented into a visualization tool to illustrate their applicability for the end-users. One of the objections was to involve end-users in the visualization process as much as possible. In this study, we present a detailed user study to assess our implemented visualization environment concerning Human Machine Interface (HMI) from the end-user perspective. Implemented visualization approaches are evaluated using task-level satisfaction and general-level transaction user surveys with well-defined study cases. In addition, System Usability Scale metrics are applied to measure the SUS score of the overall visualization system. The user study experiments proof that the proposed visualization approaches are applicable, and processing overhead is significantly low by facilitating the visualization system.

There are innumerable data generated on the internet every day, which is hardly effectively analysed by traditional means because of its capacities and complexities. This data is not only huge but also has complex relationships between different kinds of datasets. In addition, if people want to know more information of the changes of data during the certain periods of time that means time factor will be taken into consideration, which leads to the problem of analysing dynamic data. This kind of data is called big data. Under the circumstances of big data era, a new process of dealing with this data should be conceived. This process contains five steps. Those are collecting data, cleaning data, storing data, analysing data and further analysis of data. The aim of the paper is to illustrate every step. The fourth and fifth steps will be introduced in detail.

Business owners, managers, and policymakers are overwhelmed by the volume of data available in their company’s internal databases and external sources. The ability to represent meaningful and concise information that facilitates decision-making is challenging. Consider a technology firm such as Company A, where a large quantity of data is generated across numerous data sources covering all the North American locations. Traditionally, these data come in different formats such as email attachments (.pdf, .xlsx, .docx, etc.) and at different time intervals, e.g., daily, weekly, and some non-periodic. The data arrives at the team’s folder on the Microsoft SharePoint platform, where team members calculate key metrics. This existing process is tedious, manual, and prone to computation or latency errors due to database refresh. Data visualization design and automation serve as a solution to these issues by creating a method through which large volumes of data can be easily aggregated, represented, consumed, and understood. When data is visualized correctly, it is easy to draw simple, actionable conclusions. This study will explore how modern data visualization tools can be used to help policymakers and managers understand supply chain operations and performance in a relatively short time, as well as make strategic plans by interacting with the dashboard. This would be achieved by creating a visualization dashboard for the real-time monitoring of Company A North America Supply Chain data across all departments using the Microsoft Power BI tool. This would help to see the big picture all at once and enable business owners, key managers, and policymakers to make sound business choices and judgments. The overarching goal of this project is to automate the data collection and visualization process to help key business owners make informed decisions. This will allow the team managers to spend less time compiling large volumes of data and leverage the visualization tool to better identify and mitigate risk as well as proactively uncover valuable opportunities that lie in their databases.

Visualization of anatomical data for disease diagnosis, surgical planning, or orientation during interventional therapy is an integral part of modern health care. However, as anatomical information is typically shown on monitors provided by a radiological work station, the physician has to mentally transfer internal structures shown on the screen to the patient. To address this issue, we recently presented a new approach to on-patient visualization of 3D medical images, which combines the concept of augmented reality (AR) with an intuitive interaction scheme. Our method requires mounting a range imaging device, such as a Time-of-Flight (ToF) camera, to a portable display (e.g. a tablet PC). During the visualization process, the pose of the camera and thus the viewing direction of the user is continuously determined with a surface matching algorithm. By moving the device along the body of the patient, the physician is given the impression of looking directly into the human body. In this paper, we present and evaluate a new method for camera pose estimation based on an anisotropic trimmed variant of the well-known iterative closest point (ICP) algorithm. According to in-silico and in-vivo experiments performed with computed tomography (CT) and ToF data of human faces, knees and abdomens, our new method is better suited for surface registration with ToF data than the established trimmed variant of the ICP, reducing the target registration error (TRE) by more than 60%. The TRE obtained (approx. 4-5 mm) is promising for AR visualization, but clinical applications require maximization of robustness and run-time.

Thyroid carcinoma (THCA) incidence has already increased by 6.2% per year and become the sixth most common malignancy diagnosed in women. The IQGAP-miRNA interaction was known to affect many cancer types, including the carcinoma. Through in silico process known as miTS, a miRNA biomarker was researched along with its molecular pathway intervention. The input of the pipeline was THCA patients from GDC Data Portal ID and it underwent data mining, statistical, and visualization process using bioinformatics tools and databases such as TCGA Assembler in R Studio, Matlab, and STRING database. The aim of this study is to discover a potential microRNA biomarker regulating the expression of IQGAP in THCA along with its acknowledged pathways. Our study shows that (1) IQGAP1 become a promoter to the growth of THCA through supporting the growth of adherens junctions in cytoskeleton, (2) IQGAP2 was a cancer suppressor, (3) miR-146b have relation with MMP16, (4) it is still unknown whether miR-146b upregulate or downregulate the PDGFRA expression in THCA. Furthermore, the pipeline of the process and command line used was provided in an open-source package (https://github.com/stefanuswibowo/miTS_Pathway).