Ploceus: Modeling, visualizing, and analyzing tabular data as networks

2013 ◽  
Vol 13 (1) ◽  
pp. 59-89 ◽  
Author(s):  
Zhicheng Liu ◽  
Shamkant B Navathe ◽  
John T Stasko
Keyword(s):  
Visual Analysis ◽  
Dynamic Network ◽  
Visual Exploration ◽  
Tabular Data ◽  
Immediate Feedback ◽  
Levels Of Abstraction ◽  
Multiple Networks ◽  
Algebraic Framework ◽  
Network Manipulation ◽  
User Actions

Tabular data are pervasive. Although tables often describe multivariate data without explicit definitions of a network, it may be advantageous to explore the data by modeling it as a graph or network for analysis. Even when a given table design specifies a network structure, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. We present a system called Ploceus that offers a general approach for performing multidimensional and multilevel network–based visual analysis on multivariate tabular data. Powered by an underlying relational algebraic framework, Ploceus supports flexible construction and transformation of networks through a direct manipulation interface and integrates dynamic network manipulation with visual exploration through immediate feedback mechanisms. We report our findings on the learnability and usability of Ploceus and propose a model of user actions in visualization construction using Ploceus.

scholarly journals Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ratanond Koonchanok ◽  
Swapna Vidhur Daulatabad ◽  
Quoseena Mir ◽  
Khairi Reda ◽  
Sarath Chandra Janga
Keyword(s):  
Single Molecule ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Direct Sequencing ◽  
Visual Exploration ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Rna Sequences ◽  
Sequencing Technologies ◽  
Signal Features

Abstract Background Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia.

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

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.

Visual Data Mining of Multimedia Data for Social and Behavioral Studies

2009 ◽  
Vol 8 (1) ◽  
pp. 56-70 ◽  
Author(s):  
Chen Yu ◽  
Yiwen Zhong ◽  
Thomas Smith ◽  
Ikhyun Park ◽  
Weixia Huang
Keyword(s):  
Data Mining ◽  
Visual Analysis ◽  
Hybrid Approach ◽  
Heterogeneous Data ◽  
Multimedia Data ◽  
Visual Exploration ◽  
Visual Data Mining ◽  
Flexible Tool ◽  
Visualization System ◽  
Behavioral Studies

With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.

Modeling and evaluating user behavior in exploratory visual analysis

2016 ◽  
Vol 15 (4) ◽  
pp. 325-339 ◽  
Author(s):  
Khairi Reda ◽  
Andrew E. Johnson ◽  
Michael E. Papka ◽  
Jason Leigh
Keyword(s):  
Visual Analysis ◽  
User Behavior ◽  
Empirical Evaluation ◽  
Visual Exploration ◽  
Chain Process ◽  
Visualization System ◽  
Log Files ◽  
Analytic Process ◽  
Visualization Systems ◽  
Analytical Tools

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This article presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.

Visual Analysis for Subgroups in a Dynamic Network

2018 ◽  
Author(s):  
Qi Ma ◽  
Xueshi Wei ◽  
Liwenhan Xie ◽  
Zhiyi Yin ◽  
Yiping Liu ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Dynamic Network

Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets

2018 ◽  
Vol 34 (6-8) ◽  
pp. 1087-1098
Author(s):  
Rainer Splechtna ◽  
Michael Beham ◽  
Denis Gračanin ◽  
María Luján Ganuza ◽  
Katja Bühler ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Data Sets ◽  
Tabular Data ◽  
Interactive Visual Analysis

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>

Studio conversations

2018 ◽  
Vol 14 (3) ◽  
pp. 379-384
Author(s):  
Alison Shields
Keyword(s):  
Visual Analysis ◽  
Visual Exploration ◽  
Art Making ◽  
Creative Processes ◽  
Way Of Thinking

In 2014, I embarked on a cross-Canada journey, visiting artists in their studios. Through interviews with artists and photograph documentation of the studios, I sought to understand the creative processes that occur within these spaces through art making. This visual essay draws from metaphors used by artists to describe a studio alongside photographs that I took to reveal my visual exploration of the space and my visual analysis and interpretation of the metaphors. Through the use of these metaphors alongside the photographs, I propose that a studio is more than a room, but rather a way of thinking. Furthermore, I reflect on how we might embrace these metaphors to imagine ways of fostering a creative educational space.

Sign in / Sign up

Export Citation Format

Share Document