Visual data analysis with computational intelligence methods

2010 ◽  
Vol 58 (3) ◽  
pp. 393-401 ◽  
Author(s):  
R. Kruse ◽  
M. Steinbrecher
Keyword(s):  
Data Analysis ◽  
Association Rules ◽  
Computational Intelligence ◽  
Graphical Model ◽  
Visual Analysis ◽  
Real Life ◽  
Model Parameters ◽  
Visual Data ◽  
Visual Approach ◽  
Computational Intelligence Methods

Visual data analysis with computational intelligence methodsVisual data analysis is an appealing and increasing field of application. We present two related visual analysis approaches that allow for the visualization of graphical model parameters and time-dependent association rules. When the graphical model is defined over purely nominal attributes, its local structure can be interpreted as an association rule. Such association rules comprise one of the most prominent and wide-spread analysis techniques for pattern detection, however, there are only few visualization methods. We introduce an alternative visual representation that also incorporates time since patterns are likely to change over time when the underlying data was collected from real-world processes. We apply the technique to both an artificial and a complex real-life dataset and show that the combined automatic and visual approach gives more and faster insight into the data than a fully-automatic approach only. Thus, our proposed method is capable of reducing considerably the analysis time.

Visual Analysis of a Large and Noisy Dataset

2015 ◽  
Vol 3 (2) ◽  
pp. 12-24
Author(s):  
Honour Chika Nwagwu ◽  
Constantinos Orphanides
Keyword(s):  
Data Analysis ◽  
Scientific Inquiry ◽  
Visual Analysis ◽  
Research Community ◽  
Formal Context ◽  
Formal Concept ◽  
Visual Data ◽  
Inconsistent Data ◽  
Data Analyst ◽  
Tools And Techniques

Visual analysis has witnessed a growing acceptance as a method of scientific inquiry in the research community. It is used in qualitative and mixed research methods. Even so, visual data analysis is likely to produce biased results when used in analysing a large and noisy dataset. This can be evident when a data analyst is not able to holistically explore, all the values associated with the objects of interest in a dataset. Consequently, the data analyst may assess inconsistent data as consistent when contradiction associated with the data is not visualised. This work identifies incomplete analysis as a challenge in the visual data analysis of a large and noisy dataset. It considers Formal Concept Analysis (FCA) tools and techniques and prescribes the mining and visualisation of Incomplete or Inconsistent Data (IID) when dealing with a large and noisy dataset. It presents an automated approach for transforming IID from a noisy context whose objects are associated with mutually exclusive many-valued attributes, to a formal context.

Interactive 3-D visual analysis of ERA 5 data: improving diagnostic indices for Marine Cold Air Outbreaks

2021 ◽  
Author(s):  
Marcel Meyer ◽  
Iuliia Polkova ◽  
Marc Rautenhaus
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Visual Analysis ◽  
Extreme Weather ◽  
Visual Exploration ◽  
Extreme Weather Events ◽  
Visual Data ◽  
Cold Air ◽  
Diagnostic Indices ◽  
Weather Events

<p>We present the application of interactive 3-D visual analysis techniques using the open-source meteorological visualization framework Met.3D <strong>[1]</strong> for investigating ERA5 reanalysis data. Our focus lies on inspecting atmospheric conditions favoring the development of extreme weather events in the Arctic. Marine Cold Air Outbreaks (MCAOs) and Polar Lows (PLs) are analyzed with the aim of improving diagnostic indices for capturing extreme weather events in seasonal and climatological assessments. We adopt an integrated workflow starting with the interactive visual exploration of single MCAO and PL events, using an extended version of Met.3D, followed by the design and testing of new diagnostic indices in a climatological assessment. Our interactive visual exploration provides insights into the complex 3-D shape and dynamics of MCAOs and PLs. For instance, we reveal a slow wind eye of a PL that extends from the surface up into the stratosphere. Motivated by the interactive visual analysis of single cases of MCAOs, we design new diagnostic indices, which address shortcomings of previously used indices, by capturing the vertical extent of the lower-level static instability induced by MCAOs. The new indices are tested by comparison with observed PLs in the Barents and the Nordic Seas (as reported in the STARS data set). Results show that the new MCAO index introduced here has an important advantage compared with previously used MCAO indices: it is more successful in indicating the times and locations of PLs. We thus propose the new index for further analyses in seasonal climate predictions and climatological studies. The methods for interactive 3-D visual data analysis presented here are made freely available for public use as part of the open-source tool Met.3D. We thereby provide a generic tool that can be used for investigating atmospheric processes in ERA5 data by means of interactive 3-D visual data analysis. Met.3D can be used, for example, during an initial explorative phase of scientific workflows, as a complement to standard 2-D plots, and for detailed meteorological case-analyses in 3-D.</p><div><br><div> <p>[1] http://met3d.wavestoweather.de, https://collaboration.cen.uni-hamburg.de/display/Met3D/</p> </div> </div>

scholarly journals A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis

2020 ◽  
Vol 17 (11) ◽  
pp. 3973 ◽  
Author(s):  
Jie Hua ◽  
Guohua Wang ◽  
Maolin Huang ◽  
Shuyang Hua ◽  
Shuanghe Yang
Keyword(s):  
Hong Kong ◽  
Data Analysis ◽  
Severe Acute Respiratory Syndrome ◽  
Visual Analysis ◽  
Prevention Measures ◽  
Peak Period ◽  
Common Control ◽  
The World ◽  
Visual Approach ◽  
Better Than

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective.

scholarly journals Visual Data Analysis Technology Based on Data Center

2022 ◽  
Vol 2146 (1) ◽  
pp. 012016
Author(s):  
Tianjun Wang ◽  
Cengceng Wang ◽  
Jiangtao Guo ◽  
dildar alim
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Recognition Rate ◽  
Development Trend ◽  
Data Sets ◽  
Visual Data ◽  
Visualization Technology ◽  
Processing And Storage ◽  
And Storage ◽  
Crucial Part

Abstract Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.

scholarly journals Interactive 3-D visual analysis of ERA5 data: improving diagnostic indices for marine cold air outbreaks and polar lows

2021 ◽  
Vol 2 (3) ◽  
pp. 867-891
Author(s):  
Marcel Meyer ◽  
Iuliia Polkova ◽  
Kameswar Rao Modali ◽  
Laura Schaffer ◽  
Johanna Baehr ◽  
...  
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Visual Analysis ◽  
Meteorological Data ◽  
Three Dimensional ◽  
Visual Data ◽  
Cold Air ◽  
Polar Lows ◽  
Diagnostic Indices ◽  
Cold Air Outbreaks

Abstract. Recent advances in visual data analysis are well suited to gain insights into dynamical processes in the atmosphere. We apply novel methods for three-dimensional (3-D) interactive visual data analysis to investigate marine cold air outbreaks (MCAOs) and polar lows (PLs) in the recently released ERA5 reanalysis data. Our study aims at revealing 3-D perspectives on MCAOs and PLs in ERA5 and at improving the diagnostic indices to capture these weather events in long-term assessments on seasonal and climatological timescales. Using an extended version of the open-source visualization framework Met.3D, we explore 3-D perspectives on the structure and dynamics of MCAOs and PLs and relate these to previously used diagnostic indices. Motivated by the 3-D visual analysis of selected MCAO and PL cases, we conceptualize alternative index variants that capture the vertical extent of MCAOs and their distance to the dynamical tropopause. The new index variants are evaluated, along with previously used indices, with a focus on their skill as a proxy for the occurrence of PLs. Testing the association of diagnostic indices with observed PLs in the Barents and the Nordic seas for the years 2002–2011 shows that the new index variants based on the vertical structure of cold air masses are more skilful in distinguishing the times and locations of PLs, compared with conventional indices based on sea–air temperature difference only. We thus propose using the new diagnostics for further analyses in climate predictions and climatological studies. The methods for visual data analysis applied here are available as an open-source tool and can be used generically for interactive 3-D visual analysis of atmospheric processes in ERA5 and other gridded meteorological data.

Justifying the data analytical choice in single case research in relation to the expected data pattern

2019 ◽  
Author(s):  
Rumen Manolov
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Multilevel Models ◽  
Single Case ◽  
Analytical Techniques ◽  
Real Data ◽  
Small Scale ◽  
Data User ◽  
Single Case Research ◽  
User Friendly

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.

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