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Zenvisage: A Expressive and Interactive Visual Analytics System

09 Jan 16:16
@adityagp adityagp
v0.1
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Zenvisage: An Interactive and Expressive Visual Analytics System

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Zenvisage is a visual data exploration system that can automatically identify and recommend visualizations that match desired user patterns. The user can specify at a high level what they are looking for either via interactions or via a query language (ZQL), and the system will perform the necessary computation to identify these visualizations.

Version

The current version is 0.1.

Features

Zenvisage enables users to effortlessly receive visualization recommendations for interesting trends, patterns, and insights from large datasets. Here are the key features of Zenvisage:

  • Zenvisage users can directly draw the trend-line they are looking for, and then rely on the system to find appropriate matches: for instance, a person browsing a dataset of material properties may be looking for those materials displaying a specific correlation between two given properties. Users can also drag-and-drop trends onto the canvas and then subsequently modify the trend. Using this interface, users can specify the insights they are looking for, and expect Zenvisage to find matches, much like a "visualization search engine".
  • Zenvisage supports a query language, called ZQL (pronounced "zee-quel"), short for Zenvisage Query Language, a flexible, powerful and intuitive mechanism to specify desired insights from visualizations. Using a small number of lines of ZQL, users can explore trends, patterns, and insights in any which way they desire.
  • In addition to returning results for user-submitted queries, zenvisage runs a host of parallel queries to find the most typical and outlier trends for the subset of data the user is currently viewing and presents them as additional context for the user.

Additional Readings

  • Our project webpage is here; regular updates will be posted at this webpage.
  • Our VLDB'17 paper describing ZQL, our SmartFuse1 ZQL optimizer, as well as a ZQL-centric user study is here.
  • Our CIDR'17 paper describing the overall Zenvisage system, along with some target user scenarios is here.

Required Software

  • Java Platform (JDK) >= 8; once installed, update JAVA_HOME to your installed java folder.
  • PostgreSQL >= 9.5; many ways to install this, including an app on Mac OSX.
  • Apache Maven 3.0.5; many ways to install this, including brew install maven on Mac OSX.

Installation Instructions

The installation of Zenvisage is straightforward, assuming Postgres, Java, and Maven are installed.

  • Clone the zenvisage repository. (Alternatively, you can download the source as a zip.)

    git clone https://github.com/zenvisage/zenvisage.git

  • Configure Postgres. First, you need to install PostgresSQL successfully. (A tutorial on installing PostgresSQL on Mac OSX can be found here.) Zenvisage uses the default username -- postgres. Users need to create a password zenvisage for the user postgres. Thus, zenvisage uses the following username and password:

      username: postgres
  password: zenvisage

For making the above change, you could run the following commands:

        ALTER USER postgres WITH PASSWORD 'zenvisage';
        ALTER USER postgres WITH SUPERUSER;

  • Build and deploy code. Inside the zenvisage folder,

      sh build.sh.

  • Run

      sh run.sh

  • Launch http://localhost:8080/ (preferably in Chrome).

License

MIT

1The smart-fuse optimization algorithms are not part of this release. Instead, we employ a simpler optimization scheme that works well for all but the most complex queries.

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