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Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be "sliced" to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers' workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur. [ABSTRACT FROM AUTHOR]

The exponential growth of urban data presents significant challenges in efficiently analyzing and gaining actionable insights for urban planning and design. This paper proposes a big data analytics framework using MapReduce-based parallel FP-growth (MP-PFP) algorithm leveraging tools like Hadoop, MapReduce, and distributed crawlers to uncover patterns and trends from large-scale, heterogeneous urban datasets. A key contribution is the integration of diverse data types, from socio-economic datasets to environmental parameters, into a consistent analysis framework. The methodology employs frequent pattern mining algorithms on a scalable analytics platform to process behavior data and derive planning directives. Additionally, data visualization and parametric analysis techniques transform raw statistics into interactive 3D landscape representations that expose environmental site attributes. Specifically, the MapReduce capabilities enable distributed parallel processing of vast urban data volumes, ensuring speed and efficiency. The data visualization module creates immersive VR representations of urban landscapes, allowing interactive modifications. Advanced simulation techniques are incorporated to model the impact of planning directives on multiple landscape attributes. The framework is designed as a scalable, customizable solution that can integrate diverse urban data sources with customizable analytics, modeling and visualization modules through APIs. Comparative evaluations demonstrate a classification accuracy improvement from 68 to 93% over prevailing approaches. The framework has proven superior in data integration, real-time responsiveness, and accurately modeling the dynamic complexities of urban landscapes. The quantifiable simulations empower designers to make more informed planning decisions aligned with community needs. Despite ongoing data accuracy and privacy concerns, the methodology shows promising capabilities in harnessing urban big data to drive intelligent, sustainable urban development through its integration of data-driven insights, computational analysis, and interactive visualization. It brings impactful innovations to the future of urban informatics and planning. [ABSTRACT FROM AUTHOR]

Big data and data-driven analysis could be utilized for traffic management to improve road safety and the performance of transportation systems. This paper introduces a web-based proactive traffic safety management (PATM) and real-time big data visualization tool, which is based on an award-winning system that won the US Department of Transportation (USDOT) Solving for Safety Visualization Challenge and was selected as one of the USDOT Safety Data Initiative (SDI) Beta Tools. State-of-the-art research, especially for real-time crash prediction and PATM, are deployed in this study. A significant amount of real-time data is accessed by the system in order to conduct data-driven analysis, such as traffic data, weather data, and video data from closed-circuit television (CCTV) live streams. Based on the data, multiple modules have been developed, including real-time crash/secondary crash prediction, CCTV-based expedited detection, PATM recommendation, data sharing, and report generation. Both real-time data and the system outputs are visualized at the front end using interactive maps and various types of figures to represent the data distribution and efficiently reveal hidden patterns. Evaluation of the real-time crash prediction outputs is conducted based on one-month real-world crash data and the prediction results from the system. The comparison results indicate excellent prediction performance. When considering spatial-temporal tolerance, the sensitivity and false alarm rate of the prediction results [i.e., high crash potential event (HCPE)] are 0.802 and 0.252, respectively. Current and potential implementation are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Twitter produces a massive amount of data due to its popularity that is one of the reasons underlying big data problems. One of those problems is the classification of tweets due to use of sophisticated and complex language, which makes the current tools insufficient. We present our framework HTwitt, built on top of the Hadoop ecosystem, which consists of a MapReduce algorithm and a set of machine learning techniques embedded within a big data analytics platform to efficiently address the following problems: (1) traditional data processing techniques are inadequate to handle big data; (2) data preprocessing needs substantial manual effort; (3) domain knowledge is required before the classification; (4) semantic explanation is ignored. In this work, these challenges are overcome by using different algorithms combined with a Naïve Bayes classifier to ensure reliability and highly precise recommendations in virtualization and cloud environments. These features make HTwitt different from others in terms of having an effective and practical design for text classification in big data analytics. The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information. We demonstrate the results of the experiments which quantify the levels of overfitting in the training stage of the model using different sizes of real-world datasets in machine learning phases. Our results demonstrate that the proposed system provides high-quality results with a score of nearly 95% and meets the requirement of a Hadoop-based classification system. [ABSTRACT FROM AUTHOR]

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students' teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students' teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students. [ABSTRACT FROM AUTHOR]

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets. [ABSTRACT FROM AUTHOR]

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

Unsupervised machine learning is one of the accepted platforms for applying a broad data analytics challenge that involves the way to identify secret trends, unexplained associations, and other significant data from a wide dispersed dataset. The precise yield estimate for the various crops involved in the planning is a critical problem for agricultural planning. To achieve realistic and effective solutions to this problem, data mining techniques are an essential approach. Applying distplot combined with kernel density estimate (KDE) in this paper to visualize the probability density of disseminated datasets of vast crop deals for crop planning. This paper focuses on analyzing and segmenting agricultural data and determining optimal parameters to maximize crop yield using data mining techniques such as K-means clustering and principal component analysis (PCA). [ABSTRACT FROM AUTHOR]

This paper presents an overview of emerging research at the Manifest Data Lab a collective of artists and climate scientists who employ climate data experimentally in public settings. We discuss one of our projects The Carbon Chronicles, a reverse-engineered climate data set, projected on prominent public buildings in London during the period of COP26. Our approach examines the potential of climate data visualisations to operate as a public form that surface issues of climate justice and historic responsibilities. We report on the project's methodological framing, production and associated artistic strategies. [ABSTRACT FROM AUTHOR]

Longitudinal cohort study is an effective way to probe into the risk factors of disease and evaluate intervention measures. It has gradually become the mainstream research method in precision medicine, chronic disease management and evidence-based education, and has been deployed in many National Science and Technology Major Projects, which established its specialized cohorts for natural populations, chronic diseases, specialized diseases. The quality of data is a make-or-break factor of longitudinal cohort study. The subjects and test tasks in longitudinal cohort study have dynamic changes over time, and the data generated involves multiple modalities and scales. Therefore, exploring how to model business-oriented longitudinal cohort data will contribute to a unified understanding and governance of longitudinal cohort data, and ultimately improve data quality. On the one hand, because different modal data in longitudinal cohort study have different dimensional indicators, it is difficult to carry out data modeling based on unified dimensional indicators through simple dimensional splicing; on the other hand, the needs of the longitudinal cohort management scenario determine the calculations should be focused on the granularity of individual subjects and data modal types. Considering the above, the traditional multi-dimensional data modeling method based on data dimension indicators and their measurements as basic elements couldn't be fully adapted to the counting and statistical requirements under the longitudinal cohort scenarios. This paper proposes a data cube model based on MOLAP named SubTaP, which take multimodal data objects as basic granularity. This model constructs a cube structure with three dimensions of Subject, Task and Phase. It can be applied to meet the visualization requirements of longitudinal cohort management scenario and guide the construction of a data information platform for cohort study. At the same time, it helps to build a unified understanding of longitudinal cohort study data among data generators, cohort maintainers, and data users. [ABSTRACT FROM AUTHOR]

Road-based freight movements are a critical component of the supply chain and transportation networks. With the drive to invest in big data collection capabilities, most of the collected freight movement data remain underutilized. To improve the efficiency and resiliency of the supply chain, it is essential to enhance the visibility of goods movements on road networks. To this end, we provide in this paper a comprehensive literature review on this topic and analyze the previous research from different perspectives, such as data levels of abstraction and existing visualization techniques. In addition, we provide a taxonomy of freight transportation visualization according to the underlying analytic objective. Furthermore, we propose a decision support tool to aid freight data analysts in selecting the right visualization tools. Finally, we identify research gaps in the field of freight transportation visualization. Highlights Systematic bibliometric analysis of state of art research related to location-based telematics data visualization Applications for location-based telematics data based on different levels of abstraction A taxonomy for visualization techniques for location-based telematics data and possible application in freight transportation A proposed decision support tool for selecting possible visualization techniques based on the abstraction and availability of data Discussion of challenges and future research direction for freight transportation data visualization [ABSTRACT FROM AUTHOR]

Data analysis and processing is playing an important role because of the large amount of data generated through various sources of big data. It is an important component in big data-based applications. Data qualities are the main concern in the data acquisition, transformation and data pre-processing under big data applications. Data pre-processing is required because of inconsistent, noisy and incomplete data generation in big data applications. Data analysis basically encompasses different methods and a function applicable to data's to detect characteristics such as data type, size, format, patterns and so on. Based on data format, it's easy to identify data qualities for further use in various applications. Moreover, data analysis and processing includes various steps such as data qualities identification, statistical analysis of data, defining modeling, and hypothetical testing of model and result from analysis. Raw data is unused data and required analysis, filtering, and processing in any system. This paper deals with the analysis and processing aspects of raw data and cleaned data in big data applications. This paper also deals with data cleaning and its implementation concepts. [ABSTRACT FROM AUTHOR]

The concept of Big Data is often used to describe any extremely large set of data and the analytical methods used to derive meaning from it. In semiconductor manufacturing, more sensors are added to manufacturing equipment to capture deeper levels of equipment and process data which gives engineers the opportunity to achieve tighter control of their manufacturing processes. The amount of data is increasing at an exponential rate, but the time available to analyze the data is not. This means engineers must become more efficient in finding the most critical data and responding to out-of-control events. This paper expands on a paper presented at the 2019 Compound Semiconductor Manufacturing Technology Conference in Minneapolis. We present automated data filtering techniques and additional Data Visualization tools that effectively communicate results when data is presented in tables and graphs. We show how we implemented these tools as part of a Process Engineering Dashboard. [ABSTRACT FROM AUTHOR]

In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. The previous research into the information association of L&I visualization network rarely tries to construct such a network or explore the information association of the network. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in detail. Experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, how to quickly obtain effective information and efficiently disseminate information technology has become the most popular topic. Studies have shown that the ability of the human brain to process data and information is unmatched by machines, and the processing of graphics is tens of thousands of times faster than that of words. Based on the deep belief network (DBN) algorithm, this paper studies the technology of information visualization graphic design teaching application. Firstly, the structure of the deep belief network is analysed to explore its technical application in graphic information reconstruction. It is concluded that the DBN algorithm can be used to deal with the problems of classification, regression, dimension calculation, feature point acquisition, accuracy calculation, and so on in machine learning training. Then, the deformation technology of graphic local design is studied based on the DBN algorithm to construct the visual teaching platform and analyse the technical research results of this algorithm in information graphic design. The results show that the DBN algorithm can quickly solve the problem of processing complex features in graphics, change the local deformation design of the original graphics to form new feature point data and add it to the teaching platform, and improve the ability of model fast learning and training, optimizing the operation efficiency of the teaching platform. [ABSTRACT FROM AUTHOR]

Electricity data could generate a large number of records from smart meter day by day. The traditional architecture might not properly handle the increasingly dynamic data that need flexibility. For effective storing and analytics, efficient architecture is needed to provide much greater data volumes and varieties. In this paper, we proposed the architecture of data storage and analytic in the big data lake of electricity usage using Spark. Apache Sqoop was used to migrate historical data to Apache Hive for processing from an existing system. Apache Kafka was used as the input source for Spark to stream data to Apache HBase to ensure the integrity of the streaming data. In order to integrate the data, we use the Hive and HBase principle of Data Lake as search engines for Hive and HBase. Apache Impala and Apache Phoenix are used separately. This work also analyzes electricity usage and power failure with Apache Spark. All of the visualizations of this project are presented in Apache Superset. Moreover, the usage prediction comparison is presented using HoltWinters algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we conduct an in-depth study and analysis of "Internet+ Business English" teaching through teaching big data model and visualization and elaborate on the change of educational objectives in the context of Internet education. From the perspective of individual value coordinates, the goal of education is to adhere to the people-oriented, personalized, and comprehensive free development of human beings; from the perspective of social value coordinates, the goal of education is to cultivate innovative talents for social innovation and development; through the multiple perspectives of the curriculum in the context of Internet education, we analyze the goal orientation and value reshaping of the curriculum in the context of educational change. The curriculum of the future will develop toward an intelligent curriculum and introduces the curriculum design and the form of curriculum organization in the context of Internet education. A comparison of the constructed regression and classification of a total of 27 data models reveals that the model constructed based on all data in the integrated education system is the most effective. The multiple linear regression model explained up to 66.5% of the variance in student academic performance; the explanatory power of the social and demographic characteristics dimension variables ranged from approximately 13% to 18%, the personal characteristics dimension variables ranged from 7% to 20%, and the student input dimension variables ranged from 10% to 17%. The highest correct prediction rate of the binary logistic regression model was 69%. [ABSTRACT FROM AUTHOR]

This paper tries to map the research work carried out in the field of Big Data through a detailed analysis of scholarly articles published on the theme during 2010-16, as indexed in Scopus. We have collected and analyzed all relevant publications on Big Data, as indexed in Scopus, through a quantitative as well as textual characterization. The analysis attempts to dwell into parameters like research productivity, growth of research and citations, thematic trends, top publication sources and emerging topics in this field. The analytical study also investigates country-wise publications output and impact in terms of average citations per paper, country-level collaboration patterns, authorship and leading contributors (countries, institutions) etc. The scholarly publication data is also subjected to a detailed textual analysis method to identify key themes in Big Data research, disciplinary variations and thematic trends and patterns. The results produce interesting inferences. Quantitative measures show that there has been a tremendous increase in number of publications related to Big Data during last few years. Research work in Big Data, though primarily considered a sub-discipline of Computer Science, is now carried out by researchers in many disciplines. Thematic analysis of publications in Big Data show that it's a discipline involving research interest from fields as diverse as Medicine to Social Sciences. The paper also identifies major keywords now associated with Big Data research such as Cloud Computing, Deep Learning, Social Media and Data Analytics. This helps in a thorough understanding and visualization of the Big Data research area. [ABSTRACT FROM AUTHOR]

In order to improve the quality of user experience of the wireless mobile multimedia communication and the mobile multimedia communication control efficiency, based on the little data and big data visualization, this paper proposed a mobile multimedia cooperative control algorithm. Firstly, the scale and content of the data generated by the wireless intelligent mobile terminal were considered in heterogeneous network environment. The paper established the consistency guarantee scheme of multimedia data. The control scheme of bandwidth allocation, small data distortion and channel state scheduling are presented. Then, based on the multimedia data frame sequence description of the entity mobile multimedia, according to user needs and three-dimensional space to reorganize the multimedia data. This kind of multimedia data can be used to control the quality of the user by multi dimension cross control of the multimedia stream and the entity captured by the intelligent mobile terminal. NS simulation results from the media playback quality, the quality of user experience and communication control efficiency compared with the proposed control algorithm with the adaptive cooperative FEC based on combination of network coding and channel Coding. The results show that the proposed control algorithm has high real-time performance, high efficiency, high reliability and high user satisfaction. [ABSTRACT FROM AUTHOR]

Abstract Severe weather events pose a significant threat to transportation networks. This research analyzes and discusses the impact of precipitation, temperature, visibility and wind speed on hourly weekday traffic flow volume in Atlanta, Georgia. The study involves the following: determine weather variables that affect traffic volume, develop a machine learning technique to derive decision rules based on weather and traffic volume, and create a web-based decision support visualization tool using the analyzed results. The relationship between extreme weather events and traffic volume was investigated by comparing traffic volume between a base case scenario and an extreme weather scenario. Data from 48 Automatic Traffic Recorder (ATR) sites around Atlanta, GA, USA and hourly precipitation data from 4 climate measurement stations were used to conduct this study. The spatiotemporal relationships between traffic volume and weather variables were analyzed individually and evaluated using a non-parametric statistical test. A machine learning technique is applied to derive decision rules that result in reduction in traffic volume. Results show significant impacts on traffic volume from visibility, precipitation and temperature and helps in isolating hours in a typical weekday when such impacts are felt. A decision support tool was also developed to visualize traffic volume and weather interactions. The data-driven insights from this analysis is applicable to transportation planners, centralized traffic control rooms and urban infrastructure decision makers. Highlights • Paper analyzes correlations between 4 weather hazards and traffic volume by combining Big Data repositories from climate science and transportation. • The paper using extensive statistical analysis to derive hours in a typical day where traffic at a particular location is impacted due to a weather hazard. • Machine learning models were also applied to derive decision rules for traffic planners and transportation personnel to optimize and plan for weather hazards. • A decision support tool was also derived to help display the data analysis, derive insights and optimize transportation decision making. [ABSTRACT FROM AUTHOR]

Social media platforms, search engine transaction logs, Web portal transaction logs, large text corpora, and other data sources offer users a variety of invaluable data sources. Data generated based on social media platforms and other data sources have become important sources of big data for researchers in information science. Big data provide not only challenges but also opportunities for information science. Emerging big data trends inevitably have an impact on research methods in information science. The authors of this paper discuss the impact of big data on research methods in information science. This paper addresses these challenges and opportunities through the lens of research methods, ranging from data processing, to sampling, to information visualization, to temporal analysis, to sentiment analysis, to correlation, to cause-effect relationship, to data accessibility, to data privacy, and data ethics issues. The discussions on related aspects of research methods provoke a healthy reflection and debate for the information science research community, which can help researchers in the field produce sound research designs for big data-oriented research studies and assist information practitioners in solving problems they face in the big data age. Research methods are fundamental and essential for any research studies. Big data analysis has a natural relationship with research methods. The paper discusses an emerging and important research topic big data from the unique angle of research methods. [ABSTRACT FROM AUTHOR]

In this paper, an in-depth study on the quantification of influencing factors and big data visualization of key monitoring indicators in the refined oil products market is carried out through fuzzy mathematical methods, and a system for quantifying influencing factors and big data visualization of key monitoring indicators in the refined oil products market with the fuzzy mathematical background is designed and implemented. The system realizes the functions of flow visualization, attack visualization, target tracking visualization, etc., and optimizes the system from the perspectives of performance and visualization effect. It achieves the display and interaction of multi-dimensional data in space and time with multiple views, angles, and dimensions. Data tagging and data correlation for key aspects of the product production process are realized through fuzzy mathematics and other means, and a quality traceability system for the manufacturing industry is realized on this basis, through which the data of some key stages of the product production process can be displayed retrospectively. The study proves that the business model of refined oil logistics platform based on value network can significantly improve the user's perceived value and benefit all parties within the value network, realizing the complementary advantages of refined oil production enterprises and logistics platform companies, improving the efficiency of enterprise's logistics and maximizing the profit of each subject within the value network to achieve profitability for all parties. [ABSTRACT FROM AUTHOR]

Data breach incidents are reported in the media to be on the rise with continuously increasing numbers. Additionally, data breaches serve a major negative impact to organizations. This study focuses on combining experience in data analytics, visualization, and quantitative analysis for business intelligence in the context of cybersecurity big-data over a period of 15-years. A large data set containing 9,015 data breaches was provided via the Privacy Rights Clearinghouse data breach database from the start of 2005 to the end of 2019. The aim of this work was to slice the data as well as represent it into a business-related visualization using time-series analysis that can help executives understand complex cybersecurity breaches, their impact, and their trend over time. We have created visualization figures along with explanations of what each visualization means in the context of cyber-attacks over time. This project was set to serve as a breakdown of the important findings from the Privacy Rights Clearinghouse data breach database of over 15- years. These findings are communicated through both key numbers and quantitative analyses for business intelligence. While our project does not cover every aspect of the dataset (due to its significant size), it serves more as a focus on one particular part of the data: incident types and their volume over the 15-year timeframe to help business executives visualize cybersecurity trends. This paper ends with a conclusion and discussion on how such cybersecurity visualizations can help industries along with future research needed. [ABSTRACT FROM AUTHOR]

This paper explores data analytics applied to urban education, focusing in particular on issues of representationalism, the view that representations (here, digital data) stand in mimetic relation to some external reality from which they are ontologically distinct. Based on interviews conducted over the 2016–2017 school year with a team of data professionals employed by a Charter Management Organization (CMO) that operates one dozen schools in South and East Los Angeles, this paper shows that respondents endorse a range of views about how data can stand in for things, events, and people. Data professionals charged with the creation and management of student-level data expressed views consistent with a representationalist understanding of digital data; by contrast, those charged chiefly with aggregating and analyzing heterogeneous streams of data expressed greater skepticism toward representationalist commitments. Despite this heterogeneous set of views, all of the data professionals interviewed relied on the same medium to communicate their interpretations of data to other members of the organization, including school personnel: a bespoke platform that consists of 125 dashboards. Critically, graphical means of visualization expressed objectivity, certainty, and actuarial foresight, even in cases where data professionals expressed ambivalence about the representational power of a given source of data. [ABSTRACT FROM AUTHOR]

Aim/Purpose: This journal paper seeks to understand historical aspects of data management, leading to the current data issues faced by organizational executives in relation to big data and how best to present the information to circumvent big data challenges for executive strategic decision making. Background: This journal paper seeks to understand what executives value in data visualization, based on the literature published from prior data studies. Methodology: The qualitative methodology was used to understand the sentiments of executives and data analysts using semi-structured interview techniques. Contribution: The preliminary findings can provide practical knowledge for data visualization designers, but can also provide academics with knowledge to reflect on and use, specifically in relation to information systems (IS) that integrate human experience with technology in more valuable and productive ways. Findings: Preliminary results from interviews with executives and data analysts point to the relevance of understanding and effectively presenting the data source and the data journey, using the right data visualization technology to fit the nature of the data, creating an intuitive platform which enables collaboration and newness, the data presenter's ability to convey the data message and the alignment of the visualization to core the objectives as key criteria to be applied for successful data visualizations. Recommendations for Practitioners: Practitioners, specifically data analysts, should consider the results highlighted in the findings and adopt such recommendations when presenting data visualizations. These include data and premise understanding, ensuring alignment to the executive's objective, possessing the ability to convey messages succinctly and clearly to the audience, having knowledge of the domain to answer questions effectively, and using the right technology to convey the message. Recommendation for Researchers: The importance of human cognitive and sensory processes and its impact in IS development is paramount. More focus can be placed on the psychological factors of technology acceptance. The current TAM model, used to describe use, identifies perceived usefulness and perceived ease-of-use as the primary considerations in technology adoption. However, factors that have been identified that impact on use do not express the importance of cognitive processes in technology adoption. Future Research: Future research requires further focus on intangible and psychological factors that could affect technology adoption and use, as well as understanding data visualization effectiveness in corporate environments, not only predominantly within the Health sector. Lessons from Health sector studies in data visualization should be used as a platform. [ABSTRACT FROM AUTHOR]

With the continuous deepening of study of data mining, the application area of data mining gradually expanded, its influence also spread to the media industry. Data visualization technology has changed the traditional narrative mode, make the news becomes a product that be produced. This paper analyzes the history of computer aided reporting to data news, the main models of data news visualization, and the process of data news production through data mining. The study found that data news focuses on the way of data processing in the entire news workflow, it involves not only classical computer graphics technology, image-processing technology and computer audio technology, but also more data analysis and visual processing technologies based on new media and cloud computing involved in. Research data mining and visualization of data-driven journalism can help journalists use big data to do news work better, deepen people's cognition of news events, find the logic which cannot be reflected in traditional news, and maximize the connotation of news report. [ABSTRACT FROM AUTHOR]

We propose a new framework for research synthesis of both evidence and influence, named research weaving. It summarizes and visualizes information content, history, and networks among a collection of documents on any given topic. Research weaving achieves this feat by combining the power of two methods: systematic mapping and bibliometrics. Systematic mapping provides a snapshot of the current state of knowledge, identifying areas needing more research attention and those ready for full synthesis. Bibliometrics enables researchers to see how pieces of evidence are connected, revealing the structure and development of a field. We explain how researchers can use some or all of these tools to gain a deeper, more nuanced understanding of the scientific literature. Highlights An exponential increase in scientific publications requires informative and integrative reviews to provide a detailed synthesis of a particular research field, and this has resulted in the emergence of novel methods for synthesizing heterogeneous research. Research weaving provides a novel framework that combines bibliometrics and systematic mapping to inform the development of a field, the influence of research papers and their interconnections, and to visualize content across and within publications. Research weaving has the potential to provide a more efficient, in-depth, and broad synthesis of a research field, to identify research biases, gaps, and limitations. Such insights have the potential to inform ecological and environmental policy and communicate research findings to the general public in more effective ways then are typically done in current research syntheses. [ABSTRACT FROM AUTHOR]

As the data science rising massive changes in many fields, effective visualization is urged for the huge amounts of data, also in the education industry is no exception. Education contains a great quantity of linked data whose key value lies in the connection. However, we do not know how different courses can carry out diversified cooperation in different major curricula. This paper proposes a network modelling approach to curriculum mapping depend on the 11 years' data of a university curriculum in the United States, using the network structure to study the curriculum connection from a network dynamic perspective, the network models present a visualize pattern with identify relationships and attributes around different courses. We also provide various descriptive statistics analysis data, such as density, average clustering, and number of nodes etc. Using complex network mapping the curriculum pattern of university education provide a gap analysis of a visualization pattern of educational curriculum research. [ABSTRACT FROM AUTHOR]

In this paper, a formal method is presented to: (1) establish a system to visualize huge quantities of related information (2) graphically show the degree of similarity between these related documents (3) navigate between related documents so that the views are reconfigured in a dynamic and gradual way. This method uses, as input, a set of documents that have been related using any existing algorithm (relationships based on contents, on votes to contents, on social relations, on demographic information, etc.). The proposed method includes two different visualization and navigational versions related to: (a) visualization by explicit graph and (b) visualization by implicit graph. As a summary, the following characteristics are presented: (1) this method maximizes the information that is displayed at each moment, so that a balance is found between: (a) the number of relations shown, (b) the number of documents viewed, (c) the amount of information presented by each document and (d) how to show the degree of similarity between documents, (2) the method includes specific rules of interaction with the user to allow navigation in a simple, gradual and intuitive way. [ABSTRACT FROM AUTHOR]

Several time-critical problems relying on large amount of data, e.g., business trends, disaster response and disease outbreak, require cost-effective, timely and accurate data summary and visualization, in order to come up with an efficient and effective decision. Self-organizing map (SOM) is a very effective data clustering and visualization tool as it provides intuitive display of data in lower-dimensional space. However, with O (N 2) complexity, SOM becomes inappropriate for large datasets. In this paper, we propose a force-directed visualization method that emulates SOMs capability to display the data clusters with O (N) complexity. The main idea is to perform a force-directed fine-tuning of the 2D representation of data. To demonstrate the efficiency and the vast potential of the proposed method as a fast visualization tool, the methodology is used to do a 2D-projection of the MNIST handwritten digits dataset. [ABSTRACT FROM AUTHOR]

With the advent of the Internet and the flourishing of connected technology, electronic commerce has become a new business model that disrupts the traditional transactional model and is transforming the consumer’s lifestyle. Electronic commerce leads to constantly changing customer needs, therefore quick action and collaboration between production and the market is essential. Meanwhile, the abundant transactional data generated by electronic commerce allows us to explore browsing behaviors, habits, preferences and even characteristics of customers, which can help companies to understand their customer’s needs more clearly. Traditional supply chain management (SCM) simply cannot keep up with electronic commerce because demand forecasts are constantly changing. Customer demands create and affect the whole supply chain. The purpose of SCM is to satisfy the customers who support the company by paying for the products; so meeting changing customer needs should be incorporated into SCM by developing demand chain management (DCM). In this paper, we explore how DCM can perform better in the electronic commerce environment based on studying website behavior data and using data analytics tools. The results show that DCM performs much better when paired with the benefits of electronic commerce and Big Data than traditional SCM methods. [ABSTRACT FROM AUTHOR]

As web technology and (big) data continue to transform how we organize ourselves, scholarly research increasingly zooms in on the socio-material conditions of citizen participation and public engagement, the objects and devices that organize publics. Where social issues may often be the driver of such public engagement, increasingly the city and, more specifically, the neighborhood itself have become a central objects connecting their inhabitants through online networks and neighborhood events. Tools and apps for citizen participation then weave together neighborhood stakeholders (e.g. inhabitants, municipal parties and entrepreneurs). This paper zooms in on a sample of 40 such tools that enable and organize bottom-up citizen participation in the city of Amsterdam. Combining a theoretical framework with content analysis, digital methods and data visualization, this paper marks the starting point of a longitudinal analysis of online tools for the urban bottom-up movement. [ABSTRACT FROM AUTHOR]

Institutions of higher education are operating in an increasingly complex and competitive environment. This paper identifies contemporary challenges facing institutions of higher education worldwide and explores the potential of Big Data in addressing these challenges. The paper then outlines a number of opportunities and challenges associated with the implementation of Big Data in the context of higher education. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on Big Data. [ABSTRACT FROM AUTHOR]

This paper attempts to address two issues relevant to the sense-making of Big Data. First, it presents a case study for how a large dataset can be transformed into both a visual language and, in effect, a 'text' that can be read and interpreted by human beings. The case study comes from direct observation of graduate students at the IIT Institute of Design who investigated task-switching behaviours, as documented by productivity software on a single user's laptop and a smart phone. Through a series of experiments with the resulting dataset, the team effects a transformation of that data into a catalogue of visual primitives — a kind of iconic alphabet — that allow others to 'read' the data as a corpus and, more provocatively, suggest the formation of a personal pattern language. Second, this paper offers a model for human-technical collaboration in the sense-making of data, as demonstrated by this and other teams in the class. Current sense-making models tend to be data- and technology-centric, and increasingly presume data visualization as a primary point of entry of humans into Big Data systems. This alternative model proposes that meaningful interpretation of data emerges from a more elaborate interplay between algorithms, data and human beings. [ABSTRACT FROM AUTHOR]

As more and more data become available for machine prognostic analysis in the big data environment, effective data suitability assessment methods become highly desired to help locate data with sufficient quality for analysis. Driven by this purpose, this paper proposes a novel and systematic methodology for data suitability assessment based on the needs of prognostics and health management (PHM). In this study, the data suitability for PHM is assessed from the aspects of detectability, diagnosability, and trendability, which correspond to the three major tasks of PHM: fault detection, fault diagnosis, and degradation assessment. The proposed methodology is mainly built upon the recent research studies on maximum mean discrepancy in the field of machine learning, which include a family of test statistics that are used to test the difference between two data distributions. The effectiveness of the proposed methodology is demonstrated in diverse industrial applications, which include semiconductors, boring tool degradation, and sensorless drive diagnosis. The results in the case studies indicate that the proposed methodology can be a promising tool to evaluate whether the data under study or the extracted feature set is suitable for PHM tasks. [ABSTRACT FROM PUBLISHER]

Classically, Data Warehouses (DWs) supports business analyses on data coming from the inside of an organization. Nevertheless, Lined Open Data (LOD) might sensibly complete these business analyses by providing complementary perspectives during a decision-making process. In this paper, we propose a conceptual modeling solution, named Unified Cube, which blends together multidimensional data from DWs and LOD datasets without materializing them in a stationary repository. We complete the conceptual modeling with an implementation framework which manages the relations between a Unified Cube and multiple data sources at both schema and instance levels. We also propose an analysis processing process which queries different sources in a transparent way to decision-makers. The practical value of our proposal is illustrated through real-world data and benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of visual instance mining, which is to automatically discover frequently appearing visual instances from a large collection of images. We propose a scalable mining method by leveraging the graph structure with images as vertices. Different from most existing approaches that focus on either instance-level similarities or image-level context properties, our method captures both information. In the proposed framework, the instance-level information is integrated during the construction of a sparse instance graph based on the similarity between augmented local features, while the image-level context is explored with a greedy breadth-first search algorithm to discover clusters of visual instances from the graph. This framework can tackle the challenges brought by small visual instances, diverse intra-class variations, as well as noise in large-scale image databases. To further improve the robustness, we integrate two techniques into the basic framework. First, to better cope with the increasing noise of large databases, weak geometric consistency is adopted to efficiently combine the geometric information of local matches into the construction of the instance graph. Second, we propose the layout embedding algorithm, which leverages the algorithm originally designed for graph visualization to fully explore the image database structure. The proposed method was evaluated on four annotated data sets with different characteristics, and experimental results showed the superiority over state-of-the-art algorithms on all data sets. We also applied our framework on a one-million Flickr data set and proved its scalability. [ABSTRACT FROM AUTHOR]

This paper presents several geometrically motivated techniques for the visualization of high-dimensional biological data sets. The Grassmann manifold provides a robust framework for measuring data similarity in a subspace context. Sparse radial basis function classification as a visualization technique leverages recent advances in radial basis function learning via convex optimization. In the spirit of deep belief networks, supervised centroid-encoding is proposed as a way to exploit class label information. These methods are compared to linear and nonlinear principal component analysis (autoencoders) in the context of data visualization; these approaches may perform poorly for visualization when the variance of the data is spread across more than three dimensions. In contrast, the proposed methods are shown to capture significant data structure in two or three dimensions, even when the information in the data lives in higher dimensional subspaces. To illustrate these ideas, the visualization techniques are applied to gene expression data sets that capture the host immune system’s response to infection by the Ebola virus in non-human primate and collaborative cross mouse. [ABSTRACT FROM AUTHOR]

The use of the Big Data in social sciences has raised a number of very delicate questions both in terms of access and in the management strategies of these sources of information. In particular, Big Data has posed a challenge mainly to data analysis from a research, decision and evaluation perspective. The aim of the paper is to illustrate the role of data visualization in assisting the process of data analysis and communication. After a brief introduction to the reasons behind the growing interest in this area and the importance of seeking new forms of data representation, attention will focus on two of the key components: the analytical component, oriented in the analysis of the data, and the communicative component, focuses on the forms of dissemination of the information. Analyzing and communicating data are the elements of the understanding process on which data visualization can make an important contribution. [ABSTRACT FROM AUTHOR]

Currently dashboards are the preferred tool across organizations to monitor business performance. Dashboards are often composed of different data visualization techniques, amongst which are Key Performance Indicators (KPIs) which play a crucial role in quickly providing accurate information by comparing current performance against a target required to fulfil business objectives. However, KPIs are not always well known and sometimes it is difficult to find an appropriate KPI to associate with each business objective. In addition, Data Mining techniques are often used when forecasting trends and visualizing data correlations. In this paper we present a new approach to combining these two aspects in order to drive Data Mining techniques to obtain specific KPIs for business objectives in a semi-automated way. The main benefit of our approach is that organizations do not need to rely on existing KPI lists or test KPIs over a cycle as they can analyze their behavior using existing data. In order to show the applicability of our approach, we apply our proposal to the fields of Massive Open Online Courses (MOOCs) and Open Data extracted from the University of Alicante in order to identify the KPIs. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to reveal the structure and patterns of cross-national collaborations in Big Data research through application of various social network analysis and geographical visualization methods.Design/methodology/approach The sample includes articles containing Big Data research, covering all years, in the Web of Science Core Collection as of December 2015. First, co-occurrence data representing collaborations among nations were extracted from author affiliations. Second, the descriptive statistics, network indicators of collaborations, and research communities were calculated. Third, topological network maps, geographical maps integrated with topological network projections, and proportional maps were produced for visualization.Findings The results show that the scope of international collaborations in Big Data research is broad, but the distribution among nations is unbalanced and fragmented. The USA, China, and the UK were identified as the major contributors to this research area. Five research communities are identified, led by the USA, China, Italy, South Korea, and Brazil. Collaborations within each community vary, reflecting different levels of research development. The visualizations show that nations advance in Big Data research are centralized in North America, Europe, and Asia-Pacific.Originality/value This study applied various informetric methods and tools to reveal the collaboration structure and patterns among nations in Big Data research. Visualized maps help shed new light on global research efforts. [ABSTRACT FROM AUTHOR]

Massively parallel simulations generate increasing volumes of big data, whose exploitation requires increasingly large storage resources, efficient networking technologies and post-processing facilities. In the coming era of exascale supercomputing, there is an emerging need for new data analysis and visualization strategies. A promising solution consists of coupling analysis with simulation, so that both are performed simultaneously. This paper describes a client-server in situ analysis for massively parallel time-evolving computations. Its application to very large turbulent transition simulations using a spectral approximation is presented. It is shown to have a low impact on the computational time with a reasonable increase of resource usage, while enriching data exploration. Computational steering is performed with real-time adjustment of the simulation parameters, thereby getting closer to a numerical experiment process. This would not have been achieved with a classical work flow using off-line visualization. [ABSTRACT FROM AUTHOR]

Currently dashboards are the preferred tool across organizations to monitor business performance. Dashboards are often composed of different data visualization techniques, amongst which are Key Performance Indicators (KPIs) which play a crucial role in quickly providing accurate information by comparing current performance against a target required to fulfill business objectives. However, KPIs are not always well known and sometimes it is difficult to find an appropriate KPI to associate with each business objective. In addition, Data Mining techniques are often used when forecasting trends and visualizing data correlations. In this paper we present a new approach to combining these two aspects in order to drive Data Mining techniques to obtain specific KPIs for business objectives in a semi-automated way. The main benefit of our approach is that organizations do not need to rely on existing KPI lists or test KPIs over a cycle as they can analyze their behavior using existing data. In order to show the applicability of our approach, we apply our proposal to the fields of Massive Open Online Courses (MOOCs) and Open Data extracted from the University of Alicante in order to identify the KPIs. [ABSTRACT FROM AUTHOR]