A RESTful Service for the visualization of database query results.
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README.md

MobSOS Query Visualization

Build Status codecov Join the chat at https://gitter.im/rwth-acis/mobsos

MobSOS Query Visualization is a Web service for the interactive exploration and visualization of relational data sets. The exploration basically consists in authoring query visualizations that can be persisted, exported to written reports or published on Web sites. Authoring a query visualization consists in three simple steps:

  1. selecting a data set,
  2. formulating a query statement (usually in SQL) and
  3. selecting a visualization type (e.g. pie or bar chart).

Optionally, query statements and visualizations can be parameterized and further described by meta data such as title or display dimensions.

Use

Build

Before you build the project make sure you have set up a database. Enter the credentials in the property file. The following values are important:

Property Description
stDbKey Default database key
stDbHost Host for the connection
stDbPort Port of the SQL Server
stDbDatabase Name of the database
stDbUser Login name
stDbPassword Password

Execute the following command on your shell:

ant all 

Deploy

The start_network.bat script uses the L2PNodeLauncher class to start the service. It does also register the content of the "startup" directory and it starts the Web-Connector at port 8080.

Steps to take before launching:

  1. Check the LASHOST variable at the queryviz.js (should be ok if you use the default startup script)
  2. Change the address of the "qv_code_template" script (located at the demo.html file) according to your setup. This is needed for exported queries to work.

Background

The original use case behind MobSOS QV was the exploration of MobSOS datasets for metrics explaining the success (or failure) of artifacts (i.e. services or tools) provided by a community information system (CIS). Any MobSOS data set comprises automatically collected, cleaned and metadata-enriched usage data as well as end-user-contributed survey data. The exploration of possible CIS success metrics is much more convenient and intuitive with the help of interactive query visualizations. Additional persistence of such query visualizations enables analysts to build up their own CIS success metric catalogues, to create dashboards showing query visualizations on real-time data and ultimately to compile MobSOS-style hierarchical CIS success models. CIS success metrics thereby serve as proxy indicators of certain CIS success factors. These factors in turn are assigned to one of six predefined and scientifically validated CIS success dimensions. The result is a CIS success model to be validated and refined over time to reflect a community's changing understanding of CIS success for given CIS artifacts. However, MobSOS QV quickly turned out to be a rather generic tool for query visualizations on arbitrary relational data sets. It has been used for creating dashboards on the evolution of different scientific or open-source developer communities.