Prajna: Adding Automated Reasoning to the Visual Analysis Process

2011 ◽  
Author(s):  
Edward Swing
Keyword(s):  
Automated Reasoning ◽  
Visual Analysis ◽  
Analysis Process

scholarly journals Visual analysis to compare academic performances of quota and non-quota students from computer-related programs

2020 ◽  
Author(s):  
Pedro B. Pio ◽  
Igor C. Sodré ◽  
Vinicius R. P. Borges
Keyword(s):  
Academic Performance ◽  
Public Universities ◽  
Visual Analysis ◽  
Dropout Rates ◽  
Quota System ◽  
Brazilian Society ◽  
Analysis Process

The implementation of affirmative actions in public universities is a topic of debate within the Brazilian society, specially regarding the academic performance of students that have been admitted through the quota system. This paper describes a visual analysis process to explore and compare the academic performances of quota and non-quota students from computer-related programs in a public Brazilian university. The results revealed that both failure and dropout rates for quota students are slightly higher than non-quota students in the first terms, but tends to present similar rates at the final terms.

A systematic view on data descriptors for the visual analysis of tabular data

2016 ◽  
Vol 16 (3) ◽  
pp. 232-256 ◽  
Author(s):  
Hans-Jörg Schulz ◽  
Thomas Nocke ◽  
Magnus Heitzler ◽  
Heidrun Schumann
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Data Gathering ◽  
Climate Impact ◽  
General Concept ◽  
Basic Knowledge ◽  
Data Types ◽  
Process Data ◽  
Tabular Data ◽  
Analysis Process

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.

VafusQ: A methodology to build visual analysis applications with data quality features

2018 ◽  
Vol 18 (4) ◽  
pp. 384-404
Author(s):  
Jhon Alejandro Triana ◽  
Dirk Zeckzer ◽  
Hans Hagen ◽  
Jose Tiberio Hernandez
Keyword(s):  
Data Quality ◽  
Visual Analysis ◽  
New Technologies ◽  
The Public ◽  
Interactive Applications ◽  
Public Transport System ◽  
Analysis Process ◽  
Interactive Visualizations ◽  
Quality Features

The use of interactive applications to support the decision-making process is more common every day. However, a huge amount of data is required in order to make more informed decisions. Fortunately, with the arrival of new technologies there are many data sources available. This requirement of data causes heterogeneity and data quality problems. A set of data quality problems are reduced in the preprocessing stage. However, many data quality issues persist after the preprocessing stage. For this reason, we proposed a methodology to take the data quality problems, to represent them and simultaneously support the analysis process. In addition, an application is developed as a use case of the methodology by analyzing the public transport system in Bogotá. Furthermore, a case study is performed to test the usefulness of the developed application. As a result, the methodology made possible the development of interactive visualizations that constitute an application that is useful to achieve the analysis tasks by including data quality features.

scholarly journals Participatory Social Sensor: A Framework to Social Media Data Acquisition and Analysis

2019 ◽  
Author(s):  
Igor Araujo ◽  
Paulo Henrique Lopes Rettore ◽  
João Guilherme Maia de Menezes
Keyword(s):  
Social Media ◽  
Data Acquisition ◽  
Visual Analysis ◽  
Social Behaviors ◽  
Data Access ◽  
Heterogeneous Data ◽  
Social Media Data ◽  
Analysis Process ◽  
Media Data ◽  
Social Sensor

Nowadays, understanding urban mobility, transit, people viewpoint, and social behaviors has been the focus of many research and investments. However, data access is restricted to private companies and governments. In addition, the costs to create a sensor infrastructure on a given area is prohibitive. Then, using Location-Based Social Media (LBSM) may provide a new way to better comprehend the social behaviors, by the use of a users viewpoint. In this work, we propose the use of LBSM as participatory sensing, designing the Participatory Social Sensor (PSS), a friendly framework to social media data acquisition and analysis. We develop the Twitter data acquisition and analysis process, aiming to achieve the user application goals through a file setup,where the user specifies the spatial area, temporal interval, tags, and other parameters. As a result, the PSS shows a set of visual analysis which provides a context overview, allowing an easy way to researchers make-decision. A case study, Detection and Enrichment Service for Road Events Based on Heterogeneous Data Merger for VANETs, based on PSS framework was published in the current conference.

Tweet Sentiment Analysis Regarding the Brazilian Stock Market

2019 ◽  
Author(s):  
Murilo C. Medeiros ◽  
Vinicius R. P. Borges
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Visual Analysis ◽  
Vector Space Model ◽  
Principal Component ◽  
Sentiment Classification ◽  
Space Model ◽  
Analysis Process

This paper describes a methodology for analyzing sentiments and for knowledge discovery in tweets regarding the Brazilian stock market. The proposed methodology starts by preprocessing and characterizing tweets to obtain an associated vector-space model. After that, a dimensionality reduction is em- ployed by using Principal Component Analysis and t-Stochastic Neighbor Embedding. Sentiment analysis of stock market tweets is performed by considering the tasks of sentiment classification, topic modeling and clustering, along with a visual analysis process. Experiments results showed satisfactory performances in single and multi-label sentiment classification scenarios. The visual analysis process also revealed interesting relationships among topics and clusters.

Topological visual analysis of clusterings in high-dimensional information spaces

2015 ◽  
Vol 57 (1) ◽  
Author(s):  
Patrick Oesterling ◽  
Patrick Jähnichen ◽  
Gerhard Heyer ◽  
Gerik Scheuermann
Keyword(s):  
Visual Analysis ◽  
Noisy Data ◽  
Information Loss ◽  
Local Analysis ◽  
High Dimensional ◽  
Information Spaces ◽  
Domain Specific ◽  
Analysis Process ◽  
Visual Clutter ◽  
Case Structure

AbstractIn many applications, domain-specific entities are easily compared and categorized if they are represented as high-dimensional feature vectors. To detect object similarities and to quantify coherent groups, analysts often visualize the vectors directly, aiming to identify clusters visually. However, common visualizations for high-dimensional data often suffer from information loss, occlusions and visual clutter for large and noisy data. In this case, structure is misleading and false insights are derived. We use topological concepts to provide a structural view of the points. We analyze them in their original space and depict their clustering structure using intuitive landscapes. We describe the visual analysis process to define and simplify the structural view and to perform local analysis by linking individual features to other visualizations.

scholarly journals Visual Analysis of Cylindrically Polarized Light Beams’ Focal Characteristics by Path Integral

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 358-363
Author(s):  
Cheng-feng Yue ◽  
Yong-bo Wu ◽  
Zhi-lie Tang
Keyword(s):  
Path Integral ◽  
Visual Analysis ◽  
Polarization State ◽  
Polarized Light ◽  
Total Contribution ◽  
Polarized Beams ◽  
Analysis Process ◽  
Integral Curves ◽  
Path Integral Method ◽  
Light Beams

Abstract To analyze the focal characteristics of cylindrically polarized beams, a visual analysis method is proposed. As known, the focal field can be described by three mutually perpendicular components, each one is the total contribution of all parts of the incident beams. For each component of all contributing parts weapply path integral method, then from the path integral curves extract focal field properties immediately, such as polarization state or intensity distribution. The analysis process of PI is visual and more understandable, and has more powerful information extraction function, which is also helpful for the design of special filtering pupil.

scholarly journals Analysis of Public Opinion on Religion and Politics in Indonesia using K-Means Clustering and Vader Sentiment Polarity Detection

2020 ◽  
Vol 3 ◽  
pp. 749-754
Author(s):  
Tanzilal Mustaqim
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Data Clustering ◽  
Visual Analysis ◽  
Religion And Politics ◽  
Analysis Data ◽  
Analysis Process ◽  
Processing Data ◽  
Public Media

Religion and politics are two things that are closely related to each other and cannot be separated. Various public responses expressed by various public media such as print media and social media that can be classified as positive, neutral and negative, one of which is using Twitter. Twitter is a microblogging social media that contains many writings with many types from various types of users including posts that contain opinions about religion and politics. This research conducted an analysis process in the form of extraction of hidden insight data, visual analysis and sentiment analysis of public opinion related to religion and politics. The analysis was conducted on 5433 datasets written on Twitter on November 12, 2019. The analysis process began with data pre-processing, data clustering and sentiment analysis. Pre-processing data generates clean data from characters and non-essential data for use in the process of data clustering and sentiment analysis. Data clustering produces extraction of hidden insight data using k-means clustering. Sentiment data analysis uses vader sentiment polarity detection to determine dataset sentiments. The results of tests carried out using jupyter notebook show insight data hidden in the form of 50 unique words that are divided into 5 clusters of 10 words each then the sentiment analysis process is carried out in each cluster. Another result is visual analysis in the form of word cloud and hashtag clustering which shows the dominant words of each piece of data according to sentiment and word count. Also pointed out words that have a frequency of dominant emergence accompanied by word sentiments. The process of analyzing public opinion datasets related to religion and politics using k-means clustering and vader polarity detection sentiments can be done well.

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