Andy Kirk Discusses Reflective Learning as Part of the Data Visualization Process

2021 ◽  
Keyword(s):  
Data Visualization ◽  
Reflective Learning ◽  
Visualization Process

Data Visualization

2014 ◽  
pp. 322-351 ◽  
Author(s):  
Ravishankar Palaniappan
Keyword(s):  
Data Structure ◽  
Data Visualization ◽  
Real World ◽  
Research Field ◽  
Use Case ◽  
Visualization Method ◽  
Visualization Process ◽  
Active Research ◽  
Real World Datasets ◽  
Qualitative Exploration

Data visualization has the potential to aid humanity not only in exploring and analyzing large volume datasets but also in identifying and predicting trends and anomalies/outliers in a “simple and consumable” approach. These are vital to good and timely decisions for business advantage. Data Visualization is an active research field, focusing on the different techniques and tools for qualitative exploration in conjunction with quantitative analysis of data. However, an increase in volume, multivariate, frequency, and interrelationships of data will make the data visualization process notoriously difficult. This necessitates “innovative and iterative” display techniques. Either overlooking any dimensions/relationships of data structure or choosing an unfitting visualization method will quickly lead to a humanitarian uninterpretable “junk chart,” which leads to incorrect inferences or conclusions. The purpose of this chapter is to introduce the different phases of data visualization and various techniques which help to connect and empower data to mine insights. It exemplifies on how “data visualization” helps to unravel the important, meaningful, and useful insights including trends and outliers from real world datasets, which might otherwise be unnoticed. The use case in this chapter uses both simulated and real-world datasets to illustrate the effectiveness of data visualization.

A geometric method for batch data visualization, process monitoring and fault detection

2018 ◽  
Vol 67 ◽  
pp. 197-205 ◽  
Author(s):  
Ray Wang ◽  
Thomas F. Edgar ◽  
Michael Baldea ◽  
Mark Nixon ◽  
Willy Wojsznis ◽  
...  
Keyword(s):  
Fault Detection ◽  
Data Visualization ◽  
Process Monitoring ◽  
Geometric Method ◽  
Visualization Process ◽  
Batch Data

Data Visualization

2017 ◽  
pp. 576-605
Author(s):  
Ravishankar Palaniappan
Keyword(s):  
Data Structure ◽  
Data Visualization ◽  
Real World ◽  
Research Field ◽  
Use Case ◽  
Visualization Method ◽  
Visualization Process ◽  
Active Research ◽  
Real World Datasets ◽  
Qualitative Exploration

Data visualization has the potential to aid humanity not only in exploring and analyzing large volume datasets but also in identifying and predicting trends and anomalies/outliers in a “simple and consumable” approach. These are vital to good and timely decisions for business advantage. Data Visualization is an active research field, focusing on the different techniques and tools for qualitative exploration in conjunction with quantitative analysis of data. However, an increase in volume, multivariate, frequency, and interrelationships of data will make the data visualization process notoriously difficult. This necessitates “innovative and iterative” display techniques. Either overlooking any dimensions/relationships of data structure or choosing an unfitting visualization method will quickly lead to a humanitarian uninterpretable “junk chart,” which leads to incorrect inferences or conclusions. The purpose of this chapter is to introduce the different phases of data visualization and various techniques which help to connect and empower data to mine insights. It exemplifies on how “data visualization” helps to unravel the important, meaningful, and useful insights including trends and outliers from real world datasets, which might otherwise be unnoticed. The use case in this chapter uses both simulated and real-world datasets to illustrate the effectiveness of data visualization.

scholarly journals STRATEGIES OF SELECTING THE BASIS VECTOR SET IN THE RELATIVE MDS

2006 ◽  
Vol 12 (4) ◽  
pp. 283-288
Author(s):  
Jolita Bernatavičienė ◽  
Gintautas Dzemyda ◽  
Olga Kurasova ◽  
Virginijus Marcinkevičius
Keyword(s):  
Experimental Investigation ◽  
Data Visualization ◽  
Basis Vector ◽  
Multidimensional Data ◽  
Clustering Method ◽  
Data Set ◽  
Original Algorithm ◽  
Multidimensional Data Visualization ◽  
Visualization Process ◽  
Basis Vectors

In this paper, a method of large multidimensional data visualization that associates the multidimensional scaling (MDS) with clustering is modified and investigated. In the original algorithm, the visualization process is divided into three steps: the basis vector set is constructed using the k‐means clustering method; this set is projected onto the plane using the MDS algorithm; the remaining data set is visualized using the relative MDS algorithm. We propose a modification which differs from the original algorithm in the strategy of selecting the basis vectors. In our modification, the set of basis vectors consists of vectors that are selected from k clusters in a new way. The experimental investigation showed that the modification exceeds the original algorithm in visualization quality and computational expenses.

Aesthetics in Data Visualization

Big Data ◽  
2016 ◽  
pp. 1053-1076
Author(s):  
Heekyoung Jung ◽  
Tanyoung Kim ◽  
Yang Yang ◽  
Luis Carli ◽  
Marco Carnesecchi ◽  
...  
Keyword(s):  
Open Source ◽  
Programming Languages ◽  
Case Studies ◽  
Data Visualization ◽  
Authoring Tools ◽  
Interdisciplinary Field ◽  
Visualization Process ◽  
Visualization Tools ◽  
Component Models ◽  
Aesthetic Sensibility

Data visualization has been one of the major interests among interaction designers thanks to the recent advances of visualization authoring tools. Using such tools including programming languages with Graphics APIs, websites with chart topologies, and open source libraries and component models, interaction designers can more effectively create data visualization harnessing their prototyping skills and aesthetic sensibility. However, there still exist technical and methodological challenges for interaction designers in jumping into the scene. In this article, the authors introduce five case studies of data visualization that highlight different design aspects and issues of the visualization process. The authors also discuss the new roles of designers in this interdisciplinary field and the ways of utilizing, as well as enhancing, visualization tools for the better support of designers.

Aesthetics in Data Visualization

2014 ◽  
pp. 1-24 ◽  
Author(s):  
Heekyoung Jung ◽  
Tanyoung Kim ◽  
Yang Yang ◽  
Luis Carli ◽  
Marco Carnesecchi ◽  
...  
Keyword(s):  
Open Source ◽  
Programming Languages ◽  
Case Studies ◽  
Data Visualization ◽  
Authoring Tools ◽  
Interdisciplinary Field ◽  
Visualization Process ◽  
Visualization Tools ◽  
Component Models ◽  
Aesthetic Sensibility

Data visualization has been one of the major interests among interaction designers thanks to the recent advances of visualization authoring tools. Using such tools including programming languages with Graphics APIs, websites with chart topologies, and open source libraries and component models, interaction designers can more effectively create data visualization harnessing their prototyping skills and aesthetic sensibility. However, there still exist technical and methodological challenges for interaction designers in jumping into the scene. In this article, the authors introduce five case studies of data visualization that highlight different design aspects and issues of the visualization process. The authors also discuss the new roles of designers in this interdisciplinary field and the ways of utilizing, as well as enhancing, visualization tools for the better support of designers.

scholarly journals Data visualization process through storytelling technique in Business Intelligence

2016 ◽  
pp. 49
Author(s):  
Andrés Gutiérrez ◽  
Cynthia B. Pérez
Keyword(s):  
Data Visualization ◽  
Business Intelligence ◽  
Data Analytics ◽  
New Technologies ◽  
Business Organizations ◽  
Visualization Process ◽  
The Impact ◽  
Area Of Study

New technologies have allowed corporations to collect, store and manage huge quantities of data, being necessary in their daily management. Hence, business intelligence and data analytics have become an important and rapidly growing area of study that reflects the impact of data-related problems to be solved in business organizations. However, it is difficult to recognized patterns and draw conclusions from large amount of data and also, understand the stories of such information. We propose a methodology based on storytelling technique in order to improve the perception and interpretation of the data with the aim to make the information more memorable for people.

scholarly journals Overlapping issues and solutions in data visualization techniques

2019 ◽  
Vol 16 (3) ◽  
pp. 1600
Author(s):  
Nur Diana Izzati Husin ◽  
Nur Atiqah Sia Abdullah
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Network Data ◽  
Data Sets ◽  
Complex Data ◽  
Advantages And Disadvantages ◽  
Data Analyst ◽  
Visualization Process ◽  
New Knowledge ◽  
Visualization Techniques

<span>The tremendous growth of big data has caused the data visualization process becomes more complex and challenging, and yet, data is expected to be increased from time to time. With these massive and complex data, it is getting harder for the data analyst to interpret or read the data in order to gain new knowledge or information. Therefore, it is important to visualize these data using different techniques. However, there are many remaining issues in data visualization techniques. These issues make the data visualization a big challenge to the data analyst. The most common issue in data visualization techniques is the overlapping issue. This paper reviews the overlapping issues in multidimensional and network data visualization techniques. The existing solutions are also reviewed and discussed in term of advantages and disadvantages. This paper concludes the advantages of the overlapping issues and solutions, before discussing their drawbacks. This paper suggests the color-based approach, relocation, and reduction of data sets to solve the overlapping issues.</span>

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