The goal of our project was to implement a score system for Convention Nation, a company that recommends conventions to its users. Scores would be assigned based on level of engagement with Convention Nation’s social media presence. This project combined the principles of gamification with a range of technologies offered at OPIM Innovate. We incorporated IOT in the form of a device that displays Facebook data, AI in the form of sentiment analysis and AR in the form of Splunk’s AR workspaces.
We were able to take data from Facebook using the Graph Application Programming Interface. We then used Natural Language Toolkit, a platform for working with natural language data in Python, to perform sentiment analysis on Facebook comments. For our presentation, we created a Facebook page that visitors could interact with and assigned scores to the administrators of the page. As each page admin made posts and visitors made comments on them, scores changed based on the level of constructive engagement. Splunk, a data analytics platform, provided a series of tools that could be used to view trends in the data and display it in augmented reality. I designed laser cut QR code lapel pins using the laser cutter at the Maker Studio in the library. The Splunk AR workspaces allowed us to scan the pins with an iPad and see our engagement scores appear next to us.
This past semester, I participated in OPIM 4895: An Introduction to Industrial IoT, a course that brings data analytics and Splunk technology to the University’s Spring Valley student farm. In this course, I learned how to deploy sensors and data analytics to monitor real-time conditions in the greenhouse in order to practice sustainable farming and aquaponics. The sensors tracked data for pH, oxygen levels, water temperature, and air temperature which was then analyzed through Splunk. At the greenhouse, we were able to visualize the results of this data in real time at the greenhouse using an augmented reality system with QR codes through Splunk technology. We were also able to monitor this data remotely through Apple TV Dashboards in the OPIM Innovate Lab on campus.
As a senior who is graduating this upcoming December, I appreciated the opportunity to have hands on experience working with emerging technology. Learning tangible skills is critical to students who plan on entering the workforce, especially in the world of technology. The Industrial IoT course has been one of my favorite courses of my undergraduate career as a student at UConn. I believe this is largely because it has significantly strengthened my technical skills through interactive learning, working closely with other students and faculty, and traveling on-site to the greenhouse. Using Splunk to analyze our own data that was produced by sensors that we deployed at the farm is a perfect example of experiential learning.
After taking OPIM 3222 I learned about this really cool big data platform called Splunk. I learned that the big selling point of Splunk is that it can take any machine data and restructure it so that it is actually useful. So after I got a Fitbit Charge HR my first thought was, “I wonder what we can find if we Splunk this.” I worked with Professor Ryan O’Connor on this project, and after several hiccups, we finally got a working add-on. Back when we first started this project we found a “Fitbit Add-On” in Splunkbase that we just had to install and then we would be ready to go. After spending a lot of time trying to get this add-on set up we learned that it was a bit outdated and had some bugs that were making it quite difficult to use. After a while this project got pushed off to the side as we worked on other IoT related projects in the OPIM Gladstein Lab.
By the time another spark of inspiration came along, the Splunk add-on was gone because of its age and bugs. Since the add-on was gone we had to take matters into our own hands. Professor O’Connor and I split the work so that I would work on using the Fitbit API to pull data and he would then work on putting it into Splunk. I wrote Python scripts to collect data on steps, sleep, and heart rate levels. We then found that the Fitbit API required OAuth2 authentication every few hours to be able to continuously pull data. Professor O’Connor already tackled a similar issue when making his Nest add-on for Splunk. He used a Lambda function from Amazon Web Services to process this OAuth2 request. We decided to use the same function for the Fitbit, but the major difference is that the function is called every few hours. Professor O’Connor then made a simple interface for users to get their API tokens and setup the add-on in minutes. We then took a look at all of the data we had and decided the best thing that we could make was a step challenge. We invited our friends and family to track their steps with us and create a dashboard to summarize activity levels, create an all-time leaderboard, and visualize steps taken over the course of the day. However, this app only scratches the surface of what can be done with Fitbit data. The possibilities are endless from both a business and research perspective. We have already gotten a lot of support from the Splunk developer community and we are excited to see what people can do with this add-on.
Before the introduction of Apple’s “Siri” in 2010, Artificial Intelligence voice assistants were no more than science fiction. Fast forward to today, and you will find them everywhere from in your phone helping you navigate your contacts and calendar, to in your home helping you around the house. Each smart assistant has its pros and cons, and everyone has their favorite assistant. Over the last few years I have really enjoyed working with Amazon’s Alexa smart assistant. I began working with Alexa during my summer internship at Travelers in 2016. I attended a “build night” after work where we learned how to start developing with Amazon Web Services and the Alexa platform. Since then, I’ve developed six different skills and received Amazon hoodies, t-shirts, and Echo Dots for my work.
So I mentioned “skills”, but I didn’t really explain them. An Alexa “skill” is like an app on your phone. These skills use the natural language processing and other capabilities of the Amazon “Lex” platform to do whatever you can think of. Some of the skills I have made in the past include a guide to West Hartford, a UConn fact generator, and a quiz to determine if you should order pizza or wings. However, while working in the OPIM Innovate lab I have found some other uses for Alexa that go beyond novelties. The first was using Alexa to query our Splunk instance. Lucky for us, the Splunk developer community has already done a lot of the leg work for us. The “Talk to Splunk with Amazon Alexa” Splunk add-on handles most of the networking between the Alexa and your Splunk instance. In order for Alexa Splunk to securely communicate we had to set up a private key, a self-signed certificate, and a Java keystore. After doing a few basic configuration steps on the Splunk side, you can start creating your Splunk Alexa Skill. This skill will be configured to talk to your splunk instance, but it is up to you to determine what queries to run. You can create “Intents” that will take an english phrase and convert that to a splunk query that you write. However, you can also use this framework to make Alexa do things like play sounds or say anything you want. For example, I used the Splunk skill we created to do an “interview” with Alexa about Bank of America’s business strategy for my management class. Below you can find links to the Alexa Add-On for Splunk as well as a video of that “interview”.