zenvisage's foundational framework
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Latest commit a0c2549 Sep 8, 2017 @siddta siddta committed on GitHub Merge pull request #149 from ucsky/master
Notes in README.md about configuration


Zenvisage: An Interactive and Expressive Visual Analytics System

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.


The current version is 0.1.


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] zenvisage-website; 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] zenvisage-vldb.
  • Our CIDR'17 paper describing the overall Zenvisage system, along with some target user scenarios is [here] zenvisage-cidr.

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. If you want to install using a Docker container, the instructions are here.

  • 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';
  • Update Database Schema

          DROP schema public cascade; CREATE schema public; CREATE TABLE zenvisage_metafilelocation (database TEXT, metafilelocation TEXT, csvfilelocation TEXT); CREATE TABLE zenvisage_metatable (tablename TEXT, attribute TEXT, type TEXT, axis TEXT, min FLOAT, max FLOAT); CREATE TABLE zenvisage_dynamic_classes (tablename TEXT, attribute TEXT, ranges TEXT);
  • Clean Postgres

          psql -d postgres -U postgres
          \connect postgres;
          DROP schema public cascade;
  • Data files location: Have you data folder under zenvisage folder, same level of src folder, name it data https://drive.google.com/drive/u/1/folders/0B3otFgGFeJnpVk96dEZqUnVaV2c

  • In Terminal:

          git pull --rebase origin v2.0
          sudo rm -f -r target/
          sudo rm nohup.out
          sudo kill $(sudo lsof -t -i:8080)  
  • Build and deploy code. Inside the zenvisage folder,

         sudo sh build.sh
         sudo sh run.sh 
  • Launch http://localhost:8080/ (preferably in Chrome, if has error mostly because of uncleared cache, use incognito mode probably fix).


In file src/main/resources/config.properties.


Dataset Upload Requirements

Dataset file

Currently, Zenvisage only accepts Comma-separated values (.csv) dataset file. The top row is attributes name and the following rows for data.

  • Sample Dataset File


Schema file

Schema file is a .txt file which specifies the way data are processed on backend and presented on graphs.

  • Schema file format

        attribute's name:fundemental attribute's data type,indexed,x-axis,y-axis,z-axis,F,F,0,general attribute's data type

attribute's name - needs to be exactly the same as the attribute name in dataset file

fundemental attribute's data type - is the type of attribute in dataset (e.g string/int/float)

indexed - currently detault value per design

x-axis - whether it would be shown on x-axis or not (e.g T/F)

y-axis - whether it would be shown on y-axis or not (e.g T/F)

z-axis - whether it would be shown on z-axis or not (e.g T/F)

F - currently detault value per design

F - currently detault value per design

0 - currently detault value per design

general attribute's data type - is the type of attribute in dataset (e.g O/C/Q) (O for Ordinal, C for Categorical, Q for Qualitative)

  • Sample Schema File


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.