The eLog library was initially developed as a research prototype and later published for lifelogging researchers in 2010 to help them easily analyze heterogenous data and complex visualization. It has been kept growing with the progress of mobile computing environments and recently its UI part is released with GPL v3 license for wider usage. The eLog UI library is optimized for mobile environment and can get easily integrated with existing Web services.

Who We Are

The original work was proposed by Pil Ho and later extended the work with collaboration with 28 researchers around the world who contributed their lifelogs, collaborated for lifelog analysis and share research results to build up an open lifelogging platform for the public. Pil Ho has been keeping the development updating the library following up the progress in mobile computing.


  • Nov. 2014: Change the web page skin using bootstrap.
  • Nov. 2014: Published elog UI library as GPL v3.
  • Oct. 2014: Version up eLog library and documentation.


K-mean GPS points classification

To meet the Google Geocoding API server limit, we use the OpenCV K-mean method to classify the GPS points into 2500 area. I slightly modified the OpenCV K-means algorithm to perform the hierarchical classification by space and time.

Hierarchical GPS spatial clustering

Prepare GPS data table

See K-mean GPS spatial database.

Program codes

See K-mean GPS spatial classification code.

Running results

Hierarchical clustering example of around Trento GPS points


Extend the search using the Google Places API