Visual Analytics for the Big Data Era - Semantic Scholar

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VISUAL ANALYTICS FOR THE BIG DATA ERA – A COMPARATIVE REVIEW OF STATE-OF-THE-ART COMMERCIAL SYSTEMS

Leishi Zhang, Andreas Stoffel, Michael Behrisch, Sebastian Mittelstädt, Tobias Schreck, Daniel Keim (University of Konstanz, Germany)

Outline 

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Motivation Survey Design Results Summary of Key Findings Conclusions

Outline 

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Motivation Survey Design Survey Result Summary of Key Findings Conclusions

Motivation 

Big Data Era



A survey of VA systems   



What is available  



provide overview of the state-of-the-art stimulate innovative ideas avoid redundant effort survey of open source tools survey of BI tools

What is missing 

comparison of commercial VA tools

Our Goal 



Complementing existing surveys An encompassing survey of commercial VA systems  

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functional comparison benchmark system performance

Provide recommendations to potential users Identify future directions for VA system development

Outline 

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Motivation Survey Design Results Summary of Key Findings Conclusions

Workflow 

Identify relevant commercial systems 



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study current market share, select systems in different categories, assign priority level for each system

Design structured questionnaire for functional comparison Analyze functional comparison result Further investigation on top-priority systems 

system stress test, test against benchmark data

In this paper… 

We report our findings on the systems 

15 systems in the initial list

Tableau, Spotfire, QlikView, JMP (SAS), JasperSoft, ADVIZOR Solutions, Board, Centrifuge, Visual Analytics, Visual Mining Cognos(IBM), SQL Server BI (Microsoft), Business Objects (SAP), Teradata, PowerPivot (Microsoft)  

10 answered our questionnaire (in green) Some additional text analysis systems investigated

nSpace (Oculus), Palentir, and In-Spire (PNNL)

Outline 

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Motivation Survey Design Results Summary of Key Findings Conclusions

Results 



Part 1: functional comparison Part 2: test with data  

Use cases Scalability (loading stress) test

Functional Comparison 

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Data Management Data Modelling Visualization System and Architecture

Data Management

Data Modelling

Visualization

System and Architecture

Benchmarking System Performance 

Use case study 1.

2.



Practice Fusion Medical Research Data (Health 2.0 Data Challenge) Geospatial and Microblogging Data (VAST Challenge 2011)

Scalability test

Use Case 1

Use case 2

Use case 2

Use case 2

Use case 2

Scalability –Loading Stress Test

Outline 

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Motivation Survey Design Survey Result Summary of Key Findings Conclusions

Key Findings 

Tasks categorization Exploration, Dashboarding, Reporting, Alerting



System characteristics  

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Interactivity Tableau Automatic Analysis Spotfire Data Compression & memory optimization QlikView Analytical add-ons JMP, Cognos Presentation oriented features Centrifuge, Board, Visual Mining, JasperSoft Network visualization Centrifuge, Visual Analytics Linguistic analysis on text documents Business Objects, Cognos, Teradata, nSpace, Palentir, and In-Spire

Outline 

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Motivation Survey Design Survey Result Summary of Key Findings Conclusions

Concluding Remarks 

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Semi- and Unstructured Data Advanced Visualization Customizable Visualization Real Time Analysis Predictive Analysis

Thanks for your attention! 

Questions?