Hyperbolic Space for Interactive Visualization

2011 ◽  
pp. 575-581
Author(s):  
Jörg Andreas Walter
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Hyperbolic Space ◽  
Exploratory Data Analysis ◽  
Strong Support ◽  
Interactive Visualization ◽  
User Interaction ◽  
Efficient Integration ◽  
Exploratory Data ◽  
The Given

For many tasks of exploratory data analysis, visualization plays an important role. It is a key for efficient integration of human expertise — not only to include his background knowledge, intuition and creativity, but also his powerful pattern recognition and processing capabilities. The design goals for an optimal user interaction strongly depend on the given visualization task, but they certainly include an easy and intuitive navigation with strong support for the user’s orientation.

scholarly journals System Design to Utilize Domain Expertise for Visual Exploratory Data Analysis

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 140
Author(s):  
Tristan Langer ◽  
Tobias Meisen
Keyword(s):  
Data Analysis ◽  
System Design ◽  
Domain Knowledge ◽  
Exploratory Data Analysis ◽  
User Interaction ◽  
Use Cases ◽  
Conceptual System ◽  
Domain Experts ◽  
Domain Expertise ◽  
Exploratory Data

Exploratory data analysis (EDA) is an iterative process where data scientists interact with data to extract information about their quality and shape as well as derive knowledge and new insights into the related domain of the dataset. However, data scientists are rarely experienced domain experts who have tangible knowledge about a domain. Integrating domain knowledge into the analytic process is a complex challenge that usually requires constant communication between data scientists and domain experts. For this reason, it is desirable to reuse the domain insights from exploratory analyses in similar use cases. With this objective in mind, we present a conceptual system design on how to extract domain expertise while performing EDA and utilize it to guide other data scientists in similar use cases. Our system design introduces two concepts, interaction storage and analysis context storage, to record user interaction and interesting data points during an exploratory analysis. For new use cases, it identifies historical interactions from similar use cases and facilitates the recorded data to construct candidate interaction sequences and predict their potential insight—i.e., the insight generated from performing the sequence. Based on these predictions, the system recommends the sequences with the highest predicted insight to data scientist. We implement a prototype to test the general feasibility of our system design and enable further research in this area. Within the prototype, we present an exemplary use case that demonstrates the usefulness of recommended interactions. Finally, we give a critical reflection of our first prototype and discuss research opportunities resulting from our system design.

scholarly journals VISUALISASI DATA INTERAKTIF DATA TERBUKA PEMERINTAH PROVINSI DKI JAKARTA: TOPIK EKONOMI DAN KEUANGAN DAERAH

2016 ◽  
Vol 12 (2) ◽  
pp. 82
Author(s):  
Nadiar Ahmad Syaripul ◽  
Adam Mukharil Bachtiar
Keyword(s):  
Data Analysis ◽  
Data Visualization ◽  
Human Visual System ◽  
Exploratory Data Analysis ◽  
Mobile Platforms ◽  
Personal Lives ◽  
Abstract Data ◽  
Exploratory Data ◽  
Open Datasets ◽  
The Given

Based on statistics from data.id, in the first quarter of 2016, there are 1,137 datasets distributed at 32 institutions and 18 groups in Indonesia. DKI Jakarta Province contributes to these data at the most, i.e. 714 datasets. A lot of accessible open datasets have an impact on the availability of valuable information that can be extracted to good use, for businesses, governments, and personal lives. To get the desired information, an exploratory data analysis is needed to make data more alive. The goal of this research is to provide a proper visualization of the given data. Data visualization is a way (perhaps a solution) to communicate abstract data, to aid in data understanding by leveraging human visual system. The result of this visualization is effective and engaging charts appropriates to the given data and can be run on mobile platforms.

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