At this year’s Programming Hackathon, we are opening up the PI System to allow you to Bring Your Own Data! (BYOD)
You can bring any sort of dataset that is free and public (and within legal requirements of course).
At the Hackathon, you are also welcome to develop tools that send and store external data into the PI System.
We will allow you to be fully creative!
Our goal is to create a real-time Data Market at the Hackathon and encourage sharing of datasets among teams. You will earn additional points if your dataset is used by other teams, so we encourage you to think ahead and bring your own data to the event!
Don't worry. We will also have pre-collected data in a PI System for you to access, but it is much more fun to work with data that you find interesting.
We will also provide you with exclusive hands-on access to the latest PI technologies so you can get the most value out of your data!
This year's theme is wearable devices and personal fitness data.
Some suggestions for data sources to bring into PI include:
1) Geolocation route data during a run, walk, commute, or bike ride. This information can be collected via free mobile apps such as RunKeeper, RunTastic, and Strava. Stay tuned to the Hackathon space as we will be providing sample projects showing how this data can be brought into PI and visualized with Esri ArcGIS maps!
2) Real-time geolocation data via HTML5 as described in this PI Square blog post.
3) Real-time geolocation data via a native phone app. For example, SensorLog for iOS is a native app that collects accelerometer data from a phone and exports to a file. Can you find a way to send this data in real-time to PI?
1) Wearable device data. We will be providing pre-collected activity data from wearable devices from volunteers that will already be stored in a PI System. Although the data will be available in PI, we will also be sharing the tools we used to collect data from the wearable device. Stay tuned to this space!
2) Heart rate data. Free apps such as Cardiio and Instant Heart Rate use your phone’s camera to measure heart rate. This data can be exported to a file and stored in PI. Answer questions such as: How does my heart rate change throughout the day? Is it affected by weather, sleep, and/or exercise?
Real-time RESTful APIs
1) Twitter Streaming API. Track all the tweets from Twitter in real-time and use PI as the infrastructure. For example, use PI to collect and analyze fitness trends and patterns in real-time. We will provide an example on PI Square in the weeks before the Hackathon so please stay tuned!
2) Bing Traffic API. Get information about traffic incidents and congestion. How can traffic data be used to recommend ideal running routes?
3) Programmable Web. This website is a repository of the most popular RESTful API’s. For example, you will find here the Instagram API, Foursquare API, and Facebook Real-Time Updates API.
External data repositories
1) UCI Machine Learning Repository. Large datasets used in machine learning research. The possibilities are endless. For example, looking at this data set (Human Activity Recognition Using Smartphones), can you create a recommender system using PI as an infrastructure to identify human activities in real-time?
2) data.gov. Contains a wealth of open data from the US government.
3) Your favorite dataset!