Temporal Data Visualization Technique Based on Treemap

Carvalho, Marissa Brasil de; Meiguins, Bianchi Serique; Morais, Jefferson

doi:10.1109/iv.2016.65

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The higher values (biggest squares) are located on the upper left corner, and lower values (smallest squares) are located on the lower right corner of the structure. Each hierarchy level contains information about one variable, and the principal visual data representations are squares size, color, and label [39,40]. Figure 10 shows the treemap with the results of the experiments performed for the Meuse and Wolfcamp databases.…”

Section: Discussionmentioning

confidence: 99%

A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic Algorithms

et al. 2019

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Kriging is a geostatistical interpolation technique that performs the prediction of observations in unknown locations through previously collected data. The modelling of the variogram is an essential step of the kriging process because it drives the accuracy of the interpolation model. The conventional method of variogram modelling consists of using specialized knowledge and in-depth study to determine which parameters are suitable for the theoretical variogram. However, this situation is not always possible, and, in this case, it becomes interesting to use an automatic process. Thus, this work aims to propose a new methodology to automate the estimation of theoretical variogram parameters of the kriging process. The proposed methodology is based on preprocessing techniques, data clustering, genetic algorithms, and the K-Nearest Neighbor classifier (KNN). The performance of the methodology was evaluated using two databases, and it was compared to other optimization techniques widely used in the literature. The impacts of the clustering step on the stationary hypothesis were also investigated with and without trend removal techniques. The results showed that, in this automated proposal, the clustering process increases the accuracy of the kriging prediction. However, it generates groups that might not be stationary. Genetic algorithms are easily configurable with the proposed heuristic when setting the variable ranges in comparison to other optimization techniques, and the KNN method is satisfactory in solving some problems caused by the clustering task and allocating unknown points into previously determined clusters.

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Section: Discussionmentioning

confidence: 99%

A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic Algorithms

et al. 2019

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“…Another important aspect of our hierarchies is that they represent temporal development, and researchers have visualized temporal data with trees before. For example, Carvalho et al [dCMdM16] combined aspects of treemaps with a calendar, so that users could target data within a certain time slot. In our case, the division time is different for each cell and the former and latter stages should always be presented for reference, so that we cannot use filters to target at a single level.…”

Section: Visualization Of and With Hierarchiesmentioning

confidence: 99%

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Hong

Trubuil

Isenberg

2022

Computer Graphics Forum

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We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.

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“…The problem of using Visual Analytics approach to explore time data has been addressed by several authors; examples are Ellis [28] and de Carvalho et al [29]. Electronic nose technology and data format and management has been introduced by Pearce [30], Scott [31] while Buono and Costabile [6] aimed at understanding how much domain experts are able to use a visual workflow tool like KNIME in order to process pollution data to perform analysis.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed framework integrates heterogeneous tools to build the working pipeline. The process is summarized in the high-level Algorithm 1 which is divided into four branches where the first one (lines 1-8) loads the temporal data by the server, followed by the execution of the KNIME workflow nodes (lines 9-12) and by the clustering functions (lines 13-21); finally the fourth phase (lines [22][23][24][25][26][27][28][29] shows results to users through the multiple Views.…”

Section: Knime Workflow Implementationmentioning

confidence: 99%

MonitorApp: a web tool to analyze and visualize pollution data detected by an electronic nose

Buono

Balducci

2019

Multimed Tools Appl

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The quality of the air is assessed by sensors in monitoring stations that measure the concentration of specific chemical compounds that may affect people's health. Assuming that some chemical compounds in the air produce a bad smell, people may detect that something is going wrong acting as sensors that alert potential risks. This paper proposes a visual analytics approach to support air quality experts in the analysis of data produced by electronic nose devices. Experts create or modify data workflows to manage and transform raw data, then apply clustering and visualization techniques to get insights. The proposed approach is supported by calendar, map and line graph visualizations also maneuvering clustering attributes and methods. An interactive map is used to show the position of monitoring stations in order to support making hypotesis related to data source locations.

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Temporal Data Visualization Technique Based on Treemap

Cited by 7 publications

References 10 publications

A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic Algorithms

A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic Algorithms

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

MonitorApp: a web tool to analyze and visualize pollution data detected by an electronic nose

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