Loren Siebert

As I mentioned in a recent blog post about image search, we’re avid users of Elasticsearch for search. We also recently ported another vital part of our system to Elasticsearch: analytics. This post is a technical deep dive into how our analytics system works, and specifically how and why we used Elasticsearch to build it. Background DigitalGov Search is

In the first part of A Picture Is Worth a Thousand Tokens, I explained why we built a social media-driven image search engine, and specifically how we used Elasticsearch to build its first iteration. In this week’s post, I’ll take a deep dive into how we worked to improve relevancy, recall, and the searcher’s experience

Increasingly, we’ve noticed that our agency customers are publishing their highest quality images on social media and within database-driven multimedia galleries on their websites. These sources are curated, contain metadata, and have both thumbnails and full-size images. That’s a big improvement in quality over the images embedded within HTML pages on agencies’ websites. After some