Kibana

This is part 2 of 2 about using Elasticsearch and Kibana to track patterns in baby activity. Part 1 here covers the hardware and setup for tracking baby kicks. Machine learning Having collected about 6 weeks of baby kicking data, it’s time to test the new toy in the Elasticsearch stack: Machine learning. Installing this was a straightforward case of following the instructions, and from the ‘Machine Learning’ new menu item in Kibana, I chose ‘Create new job’, and ‘Create a single metric job’: [Read More]

This is part 1 of 2 about using Elasticsearch and Kibana to track patterns in baby activity. Part 2 is here. Kicking things off According to countthekicks.org “Counting baby kicks is important because changes in your baby’s movement pattern may indicate potential problems with your pregnancy”. Counting and patterns sounds like a technological problem for Elasticsearch and Kibana! There’s a few apps dedicated to tracking kicks (over 20 apps last count on Android), but they’re mostly crap (lacking detailed history or useful insights) and being a data geek I’m not entirely happy handing over the data without a definite means to export it. [Read More]

Sentiment analysis, Elasticsearch and Kibana The idea From the Twitter streaming API grab tweets for a live TV show whilst it’s showing, classify by contestant, analyze sentiment and provide a real-time dashboard into the outlook. Try to predict good bets on the winners. Optional: Gamble. :-) The tools Grabbing the data: Scala and the twitter4j Java library Sentiment analysis: SentiStrength Search engine: Elasticsearch Dashboard: Kibana (also from the Elasticsearch guys) The show I picked the final of the BBC’s “Strictly Come Dancing” in the UK. [Read More]