Big Data Visualisation and Visual Analytics for Music Data Mining

Barkwell, Katrina E.; Cuzzocrea, Alfredo; Leung, Carson K.; Ocran, Ashley A.; Sanderson, Jennifer M.; Stewart, J.; Wodi, Bryan H.

doi:10.1109/iv.2018.00048

Cited by 43 publications

(6 citation statements)

References 29 publications

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“…The most common being multidimensional data, which can be presented using graphs and charts, taking multiple variables, for instance, bar or pie charts. Another data type is geospatial; this involves data collected from the earth through location data, visualised through distribution maps, cluster maps, and more commonly, contour maps [71].…”

Section: ) Data Visualisationmentioning

confidence: 99%

Digital Twin: Enabling Technologies, Challenges and Open Research

et al. 2020

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Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

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Section: ) Data Visualisationmentioning

confidence: 99%

Digital Twin: Enabling Technologies, Challenges and Open Research

et al. 2020

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“…These studies show that traditional data visualization is limited and often ignored by the user. In support, (Barkwell et al, 2018) [6], (Okada, Yoshida, Itoh, Czauderna, Stephens, 2018) [7] stipulated that user experience can be improved by integrating a real-time interactive visualization such as hue-saturation-value (HSV) based data visualization and Spatio-temporal, respectively.…”

Section: Data Visualizationmentioning

confidence: 99%

Visualization and Geo-Mapping of Philippine Fire Incidents

Oñate¹

2022

J. Eng. and Emerging Tech.

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A fire incident is one of the most catastrophic calamity bringing injuries, loss of properties, and casualties. In the Philippines, a rapid increase in fire incidents was recorded from 2013 to 2016. In this paper, we explore the importance of data visualization and analysis in extracting useful information that might help in planning and decision-making. This study used the 2012–2016 Philippine Nationwide Fire Incident Statistics data provided by the Bureau of Fire Protection through Open Data Philippines. Data cleaning and reprocessing were conducted to develop a dynamic system known as FireStatPH using Flask. Different data visualization techniques such as choropleth map were also used in the study to represent each dataset. FireStatPH can easily identify the cities and municipalities with high fire incidents, injuries, deaths, and damages. It also provides fast statistics based on fire incident data. The system contributes to the planning and decision-making process in eschewing fire incidents in the Philippines.

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“…Several platforms and visualizers [27][28][29] have been built to visualize knowledge discovered from different data mining tasks. Among them, some [30,31] were designed and developed to visualize the discovered frequent patterns. For example, FIsViz [32] displays each frequent pattern containing k items (i.e., k-itemset) in the form a polyline that connects k nodes in a 2-dimensional space.…”

Section: B Frequent Pattern Visualizersmentioning

confidence: 99%

Big Data Visualization and Visual Analytics of COVID-19 Data

Leung

Chen

Hoi

et al. 2020

2020 24th International Conference Information Visualisation (IV)

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In the current era of big data, a huge amount of data has been generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. As "a picture is worth a thousand words", having methods to visualize and visually analyze these big data makes it easily to comprehend the data and the discovered knowledge. In this paper, we present a big data visualization and visual analytics tool for visualizing and analyzing COVID-19 epidemiological data. The tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for visualization and visual analytics of epidemiological data, it is applicable to visualization and visual analytics of big data from many other real-life applications and services.

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Big Data Visualisation and Visual Analytics for Music Data Mining

Cited by 43 publications

References 29 publications

Digital Twin: Enabling Technologies, Challenges and Open Research

Digital Twin: Enabling Technologies, Challenges and Open Research

Visualization and Geo-Mapping of Philippine Fire Incidents

Big Data Visualization and Visual Analytics of COVID-19 Data

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