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Data and community are two major assets that cannot be invented or disrupted as quickly as technology. At the OSIsoft Users Conference 2017 in San Francisco I had the opportunity to witness firsthand how the PI community is pushing the envelope for a smarter manufacturing world. In the process they have at their disposal decades of real data sitting across several thousands of PI Systems around the world. An ever-increasing number of organizations are making good on the promise of turning time-series process data into decision-ready and predictive knowledge. Below is a short summary of my observations.


Activities and opportunities


  • Hackathon: we offered real operational data from Barrick Gold, the biggest gold producer in the world. The dataset spanned 6 months of sensor measurements from their massive haul trucks. 60 data streams from 30+ trucks made this a rich dataset. The trucks are big parts of Barrick’s operational cost. Each of these giants cost $4MM while one tire costs $50K. Their miles per gallon (mpg) is 0.3. That gives you an idea how costly this operation is and how efficiency can be vital to any mine operator like Barrick. When a truck is full the value of gold waiting to be extracted is $60K. It means if a truck goes down without notice a significant amount of capital can sit in the middle of nowhere for days before fix arrives.

Our hackers took advantage of the opportunity and built very innovative ideas. As a judge I was struck by the level of quality and maturity of the submissions in 23 hours. Several teams focused on advanced analytics and data as the cornerstone of their submission while others focused on software development. Most notably, the winning team merged machine learning with social engineering to design a system where drivers would earn points by driving “well”. And “well” was learned through sifting through sensor data, the strains and temperatures across the truck body, as well as geospatial qualities of the road.


  • Partners and customers: several OSIsoft partners and customers flexed their muscles around data science with PI data. We enjoyed several hands-on labs and presentations on the topic ranging from real customer stories to educational pieces on how to pull off a successful machine learning project with PI data. Most notably “United Utilities” (UK) presented how they built a demand forecasting engine which is critical to serving water to their customers efficiently. “Total” showcased how they use data form PI System to build and deploy a model and predict the percentage of gasoil in the residues of their distillation tower. Many conversations I had with attendees all point to the acceleration of more advanced analytics and data science in the PI world. This is all exciting because it shows how much business opportunity is out there waiting to be tapped.




Like everything else in the world the nice benefits don’t come for free. There are still significant challenges along the way:


  • Quality of the data: throughout the event a constant theme was challenges around data quality. While it’s typical to immediately focus on the machine learning algorithm or architecture it is evident that industrial data can be messy, vague, or flat out nonsense. The nature of these data sources and their paths to server make them susceptible to sensor error, noise contamination, process errors, mistakes in units of measure, unlogged changes over time, and lack of context to name a few. A significant amount of effort has to be spent on vetting, reshaping, cleaning, and reformatting data before a machine learning algorithm can be applied. Building a diverse and broad team of data scientists and subject matter experts seem to be the right strategy to alleviate such pain points.
  • Cultural and governance issues: preserving and sharing data may not be as easy as the technology that enables it. The industrial community has a long history of protecting itself against all sorts of malicious attacks and innocent mistakes, hence isolating itself from the rest of the world. The new needs and opportunities call for easing up some of the traditional requirements while guaranteeing security. It takes a significant cultural shift on top of technological and security advancements. Besides, addressing the data quality issues mentioned above takes a change in the mindset across the organization from top to bottom. To top this off with yet another layer, in many cases data is comprised of elements sitting in different jurisdictions which makes data sharing and aggregation even more challenging.


The opportunities for data science and machine learning in the PI world abound as do the challenges. However, challenges are nothing that we can’t overcome with the power of our smart and energetic community. All the signs are pointing to a wave of industrial organizations investing serious capital and resources in this area. After all it may be the differentiator between the survivors and failures of the coming decade. I ask of anyone in this community to share their thoughts, experiences, challenges, and ideas in this field. We at OSIsoft are committed to push this forward with your help.

This is the material for the "Developing Cross-Platform Mobile Apps using Xamarin and PI Web API " hands-on-lab held during the UC SF 2017 Developer Track.


It includes:

1. Visual Studio Solution with two projects

2. Workbook

3. XML to be imported in PI AF

4. PowerPoint Presentation



Click "Download ZIP" on the right-side of the toolbar or fork and clone to your local repository.


This lab has 5 exercises.

  • Exercises 1 and 5 will focus on PI Web API calls.
  • Exercise 3 will explain how integrate your app with PI Coresight.
  • Exercises 2 and 4 are about Xamarin.Forms.


If you want to have another hands-on-lab next year about developing mobile apps using Xamarin or if you have any other comments or questions, please post a comment on PI Square.

We are excited to present the Users Conference Programming Hackathon 2017 winners!


The theme of this year's Hackathon was IIoT: Asset Health Monitoring, Predictive Analytics, and Maintenance Optimization of industrial mobile assets. Barrick Gold Corporation, the largest mining company in the world, kindly provided a sample of one of their sites with haul trucks data. Participants were encouraged to create killer applications for Barrick by leveraging the PI System infrastructure.


The participants had 23 hours to create an app using any of the following technologies:

  • PI Server 2017 Beta
  • PI Web API 2017 Beta
  • PI Vision 2017  Beta
  • PI OLEDB Enterprise 2016 R2


Our judges evaluated each app based on their creativity, technical content, potential business impact, data analysis and insight and UI/UX. Although it is a tough challenge to create an app in 23 hours, seven groups were able to finish their app and present to the judges!



1st place: $400 Amazon gift card, one year free subscription to PI Developers Club, one time free registration at OSIsoft Users Conference over the next 1 year

2nd place: $300 Amazon gift card, one year free subscription to PI Developers Club, 50% discount for registration at OSIsoft Users Conference over the next 1 year

3rd place: $200 Amazon gift card, one year free subscription to PI Developers Club



Without further do, here are the winners!


1st place - Random Sample


The team members were: Jacqueline Davis, James Hughes, Matthew Wallace and Jon Horton.




Team Random Sample developed an app for the drivers of haul trucks. They receive points for optimal driving and reporting of road hazards. The displays of the app support driver interaction. On top of that, they have added a bar code security to access the web site.


The team used the following technologies:

  • PI Vision
  • Google Maps
  • Node.JS


Here are some screenshots presented by the Random Sample team!






2nd place - Machine Learners


The team members were: Ionut Buse, Gael Cottet and Jean-Francois Beaulieu





They developed an app named Truck learning. It is a predictive analytics application, which uses Azure machine learning to predict fuel consumption statistics based on different attributes of a haul truck's trip.


The team used the following technologies:

  • Azure Machine Learning
  • AngularJS
  • AF-SDK
  • Azure Web Services


Here are some screenshots presented by Machine Learners!






3rd place - Atomic 79


The team members were: Mina Andrawos, Stew Bernhardt, Seth Gregg and Dave Johnson.





Team Atomic 79 developed an app named Barrick Tomcat, which is actually a suite of the following 3 apps:

  • Color LED data visualization of truck health
  • Intelligent bot for truck status
  • Convert human voice to PI tag values for truck driver to annotate PI data


The team used the following technologies:

  • Node.js (axios, firmata, node-pixel)
  • Microsoft Bot Framework
  • Azure SDK
  • Microsoft Cognitive Services


Here are some screenshots presented by Atomic 79!




3rd place - PI in the sky


The team members were: Rhys Kirk, Rob Raesemann, Yevgeni Nogin and Lonnie Bowling.




PI in the sky team created the Haul Truck Monitoring and Performance app. The app provides analytics and visualization of Haul Truck Performance including pattern recognition, custom dashboard, and PI Vision displays.


The team used the following technologies:

  • Angular 2
  • D3
  • Python
  • Asyncio


Here are some screenshots presented by PI in the sky!



It's that time of year again!  In recognition of an ever growing community, we have added a new category this year called Rising Star in order to shine the spotlight on a few more, well-deserving contributors. The goal is to acknowledge the great impact of newer faces in our community. If you have thoughts on this new category or ideas regarding possible future categories, please share them with us.


As a former customer and All-Star myself, it is my distinct pleasure to announce the 2017 All-Stars and Rising Stars.  From unselfishly dedicating their time to answer posts by others, as well as participating in interesting discussions to expand our overall knowledge base, these individuals are a tremendous source of valued experience within the PI community.  We would not be where we are without the many great contributions from these individuals.  So I ask my fellow PI Geeks for a hearty round of applause as we recognize these respected members of our community.


PI Developers Club Community All-Stars 2017


They win:

  1. Recognition in the community
  2. One year free subscription or renewal to PI Developers Club
  3. One-time free registration at UC/TechCon (UC EMEA 2017 or UC San Francisco 2018 - not including labs)
  4. Amazon gift card worth $400 USD


PI Developers Club Community Rising Stars 2017


They win:

  1. Recognition in the community
  2. One year free subscription or renewal to PI Developers Club
  3. One-time free registration at UC/TechCon (UC EMEA 2017 or UC San Francisco 2018 - not including labs)
  4. Amazon gift card worth $400 USD


PI Developers Club OSIsoft All-Stars 2017


They win:

  1. Recognition in the community
  2. Amazon gift card worth $200 USD


Please join me in congratulating our 2017 All-Stars!  Will you be among the nominees next year?

Hey PI geeks,


All of us on the PI DevClub Team are very excited about the upcoming UC 2017 in San Francisco! We are looking forward to chatting with all of you and hearing what you've been up to with PI Developer Technologies.We've been working on some things too and would love to share them with you!


Until then, here's a sneak peek at two of the things we're experimenting with:


Search-based Asset Context Picker for PI Coresight Displays


This widget gives an alternative way of picking assets of interest in PI Coresight, starting with some explicit search fields and allowing the results of multiple searches to be combined. It's still a work in progress - Paul Martin, Robert Schmitz, and I are hoping this can drive some good discussions.

Custom Search-based Asset Context Picker for Coresight Displays - YouTube

GitHub repository: PI-Coresight-Custom-Symbols/Community Samples/CoolTeam at master · osipmartin/PI-Coresight-Custom-Symbols · GitHub



Custom Symbol for Manual Data Entry

Manual Data Entry symbol for PI Coresight - YouTube

GitHub repository: PI-Coresight-Custom-Symbols/Community Samples/OSIsoft/manual-data-entry at master · AnnaPerry/PI-Coresight-Custom-Symbol…

Also available in single-button form:


If these are interesting to you, or you have some ideas and suggestions, we hope you'll come see us at the PI Developers Club pod at the Users Conference!

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