Finding Patterns in Visualized Data by Adding Redundant Visual Information (original) (raw)

Enhancing data visualization techniques

International Workshop on Visual Data Mining in conjunction with ICDM 2003, 2003

The challenge in the Information Visualization (Infovis) field is two-fold: the exploration of raw data with intuitive visualization techniques and the discover of new techniques to enhance the visualization power of well-known infovis approaches, improving the synergy between the user and the mining tools. This work pursues the second goal, presenting the use of interactive automatic analysis combined with visual presentation. To demonstrate such ideas, we present three approaches aiming to improve multivariate visualizations. The first approach, named Frequency Plot, combines frequencies of data occurrences with interactive filtering to identify clusters and trends in subsets of the database. The second approach, called Relevance Plot, corresponds to assign different shades of color to visual elements according to their relevance to a user’s specified set of data properties. The third approach makes use of basic statistical analysis presented in a visual format, to assist the analyst in discovering useful information. The three approaches were implemented in a tool enabled with refined versions of four well-known existent visualization techniques, and the results show an improvement in the usability of visualization techniques employed.

Visual exploration of frequent patterns in multivariate time series

Information Visualization, 2012

The detection of frequently occurring patterns, also called motifs, in data streams has been recognized as an important task. To find these motifs, we use an advanced event encoding and pattern discovery algorithm. As a large time series can contain hundreds of motifs, there is a need to support interactive analysis and exploration. In addition, for certain applications, such as

Graphical perception of multiple time series

2010

Abstract Line graphs have been the visualization of choice for temporal data ever since the days of William Playfair (1759-1823), but realistic temporal analysis tasks often include multiple simultaneous time series. In this work, we explore user performance for comparison, slope, and discrimination tasks for different line graph techniques involving multiple time series.

KronoMiner: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data

CHI'11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2011

The need for pattern discovery in long time-series data led researchers to develop interactive visualization tools and analytical algorithms for gaining insight into the data. Most of the literature on time-series data visualization either focus on a small number of tasks or a specific domain. We propose KronoMiner, a tool that embeds new interaction and visualization techniques as well as analytical capabilities for the visual exploration of time-series data. The interface design has been iteratively refined based on feedback from expert users. Qualitative evaluation with an expert user not involved in the design process indicates that our prototype is promising for further research.