Helps you track, manage, and learn from your web searches
To run the extension, just clone the repo, and add it to chrome using the 'Load unpacked extension...' button.
This chrome extension is developed using Bower and Grunt for asset management. All
external packages are downloaded and placed in the vendor directory using bower. Any assets in the assets directory are
compiled (if they are less/coffee script) and synced to the dist directory along with the vendor files using Grunt. Please try
to continue to develop this extension using the files in the assets directory rather than the dist directory.
The application is divided into two working parts. A background service that manages the searches, and a front-end companion that provides some real-time information from the service.
First, running in the background, is the search-tracker service. This can be found in the searchTrack.coffee file. Additionally,
an API / Database service runs in series with this, in the trackAPI.coffee file. If you wish to access the information
recorded by the service, you just need to include TaffyDB and the trackAPI.coffee files in your app. This can be
seen in the companion app provided.
The service exposes two global variables. The SearchInfo variable and the PageInfo variable. The SearchInfo variable
tracks the queries performed, while the PageInfo tracks the pages in relation to each other and the query. These two variables
can be queries by using TaffyDB query commands against their db parameter. EX: PageInfo.db({query: some_query})
The front end provides some simple interaction with the information in the back-end. Its written in angular and linked to the
back-end service through a couple of update functions that allow it to react in real time to the searches being performed.
It has 3 views
- a simple list that correlates web pages to the related searches
- a tree view that allows you to view the web page path for a search.
- and a settings page that allows you to clear the service's information
This application relies on a couple of dependencies. The core functionality only depends on TaffyDB to manage and save the searches monitored.
The front-end application uses AngularJS to create a real-time application and Underscore.js to perform some data processing on the results returned from the back-end service. There are some additional dependencies that provide the tree graphic (D3), integration with Twitter Bootstrap (Angular-Bootstrap), URI processing (URI.js) and, of course, jQuery.