Egont Part II

(part I here)


Egont is a shared space where users mashup personal information.
Its top goals are:
  • Discovering and curating new information in a personalized and dynamic way.
  • Promoting emergent behavior in a shared programming environment
  • Facilitating Serendipity.

Egont is a personalization environment where users can connect to, import, expose, and index data from their web services. They can also apply functions to build mashups around their personal interest like in a spreadsheet. On Egont, users can combine and exchange information. For example, users can connect their Egont accounts to a variety of services like movie rankings, and merge rankings from their social networks. If they want to find independent films they can filter out blockbusters. When users from their social networks update their rankings, these updates are processed and the result is automatically recalculated. The same idea can be applied to streams from Twitter or blog posts. One user can apply a filter to those streams to curate information apart from mainstream trends and recommendation systems, while other users can build new filters using this user’s data. Third parties can take advantage of the data flowing in this shared environment by developing new information functions.

Egont has a simple programming language where experienced users can access other user’s variable namespaces and handle security granularities to enable or restrict the flow of information. Less experienced users personalize their Egont experience using a simpler web interface.


Egont is composed of the following elements:
  1. A data flow engine
  2. A data store where cell values are persisted.
  3. A web application
  4. A simple programming language

Data Flow Engine

The data flow engine works like a spreadsheet. Some cells may be dependant on others. Values are recalculated only when necessary. For example, one cell may contain a function to retrieve new tweets, while another cell takes those tweets and uses a second function to extract named entities like places or proper names. Users can personalize the vast flow of information from many sources to process, aggregate, and filter information. The data flow engine limits recalculation to affected cells only.

The key feature of the engine is its ability to apply functions to a set of shared cells from other users. Another important feature is the handling of security settings. Users can configure which cells are shared with which users at a very granular level.

Web Application

The web application has two important parts. One is the editor where advanced users can use the browser to edit their Egont scripts. The other is a simpler user interface where users are able to define their sources of information and apply functions to them more easily.

Programming Language

The goal of Egont is to simplify the building of personalization and mashups, so its programming language is oriented to quickly orchestrating user information.

This is a rough example of how an advanced user could use Egont programming language to merge friends movie rankings.

friends <- [egont.users.alice, egont.users.bob, me] # list of friends.
movies_ranking <- imdb.ranking("swain-4") # persist my ranking on movies_ranking from my user on IMDB.
movies_average <- average(apply(friends, ’movies_ranking’)) # calculate the average of movies rankings from my specified friends. It only changes when rankings are updated
egont.feeds <- movies_average # expose the results as a feed in the web application.

Whenever any of the above users modify a movie’s ranking Egont recalculates that movie’s score.

With Egont,  we will have a place where we can discover new resources, research our interests, and create a community capable of sifting through the ever more vast sea of data available on today’s web.

See Also

  1. Parsing S-Expressions in C# using OMeta


  1. A Brief History of Spreadsheets
  2. Kahn process networks
  3. Directed acyclic graph
  4. Advances in IC-Scheduling Theory: Scheduling Expansive and Reductive Dags and Scheduling Dags via Duality
  5. Pregel: A System for Large-Scale Graph Processing
  6. Grzegorz Malewicz’s Google Research page
  7. CIEL: a universal execution engine for distributed data-flow computing
  8. Bloom Programming Language (via ComingThoughts)