scholarly journals DataSite: Proactive visual data exploration with computation of insight-based recommendations

2018 ◽  
Vol 18 (2) ◽  
pp. 251-267 ◽  
Author(s):  
Zhe Cui ◽  
Sriram Karthik Badam ◽  
M Adil Yalçin ◽  
Niklas Elmqvist
Keyword(s):  
Data Analysis ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Recommendation System ◽  
User Study ◽  
Data Exploration ◽  
Server Side ◽  
Knowledge Requirements ◽  
High Knowledge ◽  
Visual Data Exploration

Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.

scholarly journals MCLEAN: Multilevel Clustering Exploration As Network

2018 ◽  
Vol 4 ◽  
pp. e145 ◽  
Author(s):  
Daniel Alcaide ◽  
Jan Aerts
Keyword(s):  
Data Analysis ◽  
Visual Analytics ◽  
User Study ◽  
R Package ◽  
Agglomerative Hierarchical Clustering ◽  
Levels Of Detail ◽  
Heterogeneous Datasets ◽  
Community Finding ◽  
Multilevel Representations ◽  
Different Levels

Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common tasks is the identification and representation of clusters. However, this is non-trivial in heterogeneous datasets since the data needs to be analyzed from different perspectives. Indeed, highly variable patterns may mask underlying trends in the dataset. Dendrograms are graphical representations resulting from agglomerative hierarchical clustering and provide a framework for viewing the clustering at different levels of detail. However, dendrograms become cluttered when the dataset gets large, and the single cut of the dendrogram to demarcate different clusters can be insufficient in heterogeneous datasets. In this work, we propose a visual analytics methodology called MCLEAN that offers a general approach for guiding the user through the exploration and detection of clusters. Powered by a graph-based transformation of the relational data, it supports a scalable environment for representation of heterogeneous datasets by changing the spatialization. We thereby combine multilevel representations of the clustered dataset with community finding algorithms. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis. To evaluate our proposed approach, we conduct a qualitative user study, where participants are asked to explore a heterogeneous dataset, comparing the results obtained by MCLEAN with the dendrogram. These qualitative results reveal that MCLEAN is an effective way of aiding users in the detection of clusters in heterogeneous datasets. The proposed methodology is implemented in an R package available athttps://bitbucket.org/vda-lab/mclean.

scholarly journals MCLEAN: Multilevel Clustering Exploration As Network

2017 ◽  
Author(s):  
Daniel Alcaide ◽  
Jan Aerts
Keyword(s):  
Data Analysis ◽  
Visual Analytics ◽  
User Study ◽  
R Package ◽  
Agglomerative Hierarchical Clustering ◽  
Levels Of Detail ◽  
Heterogeneous Datasets ◽  
Community Finding ◽  
Multilevel Representations ◽  
Different Levels

Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common tasks is the identification and representation of clusters. However, this is non-trivial in heterogeneous datasets since the data needs to be analyzed from different perspectives. Indeed, highly variable patterns may mask underlying trends in the dataset. Dendrograms are graphical representations resulting from agglomerative hierarchical clustering and provide a framework for viewing the clustering at different levels of detail. However, dendrograms become cluttered when the dataset gets large, and the single cut of the dendrogram to demarcate different clusters can be insufficient in heterogeneous datasets. In this work, we propose a visual analytics methodology called MCLEAN that offers a general approach for guiding the user through the exploration and detection of clusters. Powered by a graph-based transformation of the relational data, it supports a scalable environment for representation of heterogeneous datasets by changing the spatialization. We thereby combine multilevel representations of the clustered dataset with community finding algorithms. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis. To evaluate our proposed approach, we conduct a qualitative user study, where participants are asked to explore a heterogeneous dataset, comparing the results obtained by MCLEAN with the dendrogram. These qualitative results reveal that MCLEAN is an effective way of aiding users in the detection of clusters in heterogeneous datasets. The proposed methodology is implemented in an R package available at https://bitbucket.org/vda-lab/mclean

scholarly journals MCLEAN: Multilevel Clustering Exploration As Network

2017 ◽  
Author(s):  
Daniel Alcaide ◽  
Jan Aerts
Keyword(s):  
Data Analysis ◽  
Visual Analytics ◽  
User Study ◽  
R Package ◽  
Agglomerative Hierarchical Clustering ◽  
Levels Of Detail ◽  
Heterogeneous Datasets ◽  
Community Finding ◽  
Multilevel Representations ◽  
Different Levels

Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common tasks is the identification and representation of clusters. However, this is non-trivial in heterogeneous datasets since the data needs to be analyzed from different perspectives. Indeed, highly variable patterns may mask underlying trends in the dataset. Dendrograms are graphical representations resulting from agglomerative hierarchical clustering and provide a framework for viewing the clustering at different levels of detail. However, dendrograms become cluttered when the dataset gets large, and the single cut of the dendrogram to demarcate different clusters can be insufficient in heterogeneous datasets. In this work, we propose a visual analytics methodology called MCLEAN that offers a general approach for guiding the user through the exploration and detection of clusters. Powered by a graph-based transformation of the relational data, it supports a scalable environment for representation of heterogeneous datasets by changing the spatialization. We thereby combine multilevel representations of the clustered dataset with community finding algorithms. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis. To evaluate our proposed approach, we conduct a qualitative user study, where participants are asked to explore a heterogeneous dataset, comparing the results obtained by MCLEAN with the dendrogram. These qualitative results reveal that MCLEAN is an effective way of aiding users in the detection of clusters in heterogeneous datasets. The proposed methodology is implemented in an R package available at https://bitbucket.org/vda-lab/mclean

Narrative Scientific Data Visualization in an Immersive Environment

2021 ◽  
Author(s):  
Richen Liu ◽  
Hailong Wang ◽  
Chuyu Zhang ◽  
Xiaojian Chen ◽  
Lijun Wang ◽  
...  
Keyword(s):  
Data Visualization ◽  
Domain Knowledge ◽  
User Study ◽  
Scientific Visualization ◽  
Scientific Data ◽  
Immersive Environments ◽  
Related Information ◽  
New Strategy ◽  
Scientific Data Visualization

Abstract Motivation Narrative visualization for scientific data explorations can help users better understand the domain knowledge, because narrative visualizations often present a sequence of facts and observations linked together by a unifying theme or argument. Narrative visualization in immersive environments can provide users with an intuitive experience to interactively explore the scientific data, because immersive environments provide a brand new strategy for interactive scientific data visualization and exploration. However, it is challenging to develop narrative scientific visualization in immersive environments. In this paper, we propose an immersive narrative visualization tool to create and customize scientific data explorations for ordinary users with little knowledge about programming on scientific visualization, They are allowed to define POIs (point of interests) conveniently by the handler of an immersive device. Results Automatic exploration animations with narrative annotations can be generated by the gradual transitions between consecutive POI pairs. Besides, interactive slicing can be also controlled by device handler. Evaluations including user study and case study are designed and conducted to show the usability and effectiveness of the proposed tool. Availability Related information can be accessed at: https://dabigtou.github.io/richenliu/

scholarly journals Web-based visual data exploration for improved radiological source detection

2017 ◽  
Vol 29 (18) ◽  
pp. e4203
Author(s):  
Gunther H. Weber ◽  
Mark S. Bandstra ◽  
Daniel H. Chivers ◽  
Hamdy H. Elgammal ◽  
Valerie Hendrix ◽  
...  
Keyword(s):  
Data Exploration ◽  
Source Detection ◽  
Visual Data ◽  
Web Based ◽  
Visual Data Exploration

Interactive Maps for Visual Data Exploration*

2006 ◽  
pp. 453-479
Author(s):  
Natalia Andrienko ◽  
Gennady Andrienko
Keyword(s):  
Data Exploration ◽  
Visual Data ◽  
Interactive Maps ◽  
Visual Data Exploration

scholarly journals Preprocessing Profiling Model for Visual Analytics

2020 ◽  
Author(s):  
Alessandra Maciel Paz Milani ◽  
Fernando V. Paulovich ◽  
Isabel Harb Manssour
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Visual Analytics ◽  
Data Preprocessing ◽  
Interview Study ◽  
Raw Data ◽  
Important Stage ◽  
Analysis Process

Analyzing and managing raw data are still a challenging part of the data analysis process, mainly regarding data preprocessing. Although we can find studies proposing design implications or recommendations for visualization solutions in the data analysis scope, they do not focus on challenges during the preprocessing phase. Likewise, the current Visual Analytics processes do not consider preprocessing an equally important stage in their process. Thus, with this study, we aim to contribute to the discussion of how we can use and combine methods of visualization and data mining to assist data analysts during the preprocessing activities. To achieve that, we introduce the Preprocessing Profiling Model for Visual Analytics, which contemplates a set of features to inspire the implementation of new solutions. In turn, these features were designed considering a list of insights we obtained during an interview study with thirteen data analysts. Our contributions can be summarized as offering resources to promote a shift to a visual preprocessing.

scholarly journals E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

2016 ◽  
Author(s):  
Maia A. Smith ◽  
Cydney Nielsen ◽  
Fong Chun Chan ◽  
Andrew McPherson ◽  
Andrew Roth ◽  
...  
Keyword(s):  
Single Cell ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Treatment Resistance ◽  
Cancer Evolution ◽  
Imaging Data ◽  
Web Based ◽  
Clinical Endpoints ◽  
Cell Experiment ◽  
Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

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