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 0.2, live at link.
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.
Since Version 0.2, Zenvisage also supports pattern specification, equation specification, and zooming into specific areas of the search canvas. Here's a Medium blog post about our new features.
You can try out Zenvisage Version 0.2 at this link. You don't need to install anything for this!
- 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.
- Our technical report describing the use of Zenvisage to address genomics, astrophysics, and battery science use-cases is here.
You can try out Zenvisage Version 0.2 at this link without installing any software. You only need to install software if you'd like to run a local deployment.
- Java Platform (JDK) >= 8; once installed, update
JAVA_HOMEto 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 mavenon Mac OSX.
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
zenvisagefor 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;
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, selectedX BOOLEAN, selectedY BOOLEAN, selectedZ BOOLEAN); CREATE TABLE zenvisage_dynamic_classes (tablename TEXT, attribute TEXT, ranges TEXT);CREATE TABLE dynamic_class_aggregations (Table_Name TEXT NOT NULL, Tag TEXT NOT NULL, Attributes TEXT NOT NULL, Ranges TEXT NOT NULL, Count INT NOT NULL);CREATE TABLE users (id TEXT, password TEXT);CREATE TABLE users_tables (users TEXT, tables TEXT);INSERT INTO users_tables (users, tables) VALUES ('public', 'cmu'), ('public', 'flights'),('public', 'real_estate'),('public', 'weather'),('public', 'real_estate_tutorial');
Postgres: psql -d postgres -U postgres \connect postgres; DROP schema public cascade;
git pull --rebase origin v2.0 sudo rm -f -r target/ sudo rm nohup.out sudo kill $(sudo lsof -t -i:8080)
username=postgres password=zenvisage port=8080 metatable=zenvisage_metatable
Build and deploy code. Inside the zenvisage folder,
sudo sh build.sh sudo sh run.sh
http://localhost:8080/(preferably in Chrome, if has error mostly because of uncleared cache, use incognito mode probably fix).
For uploading your datasets, you can follow the instructions explained here.
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.