Blogging in a time of AI

I am renewing my frequency in blog posting. This will come after an almost 5 year break from blogging. I am returning because I am back to working on open-source software, educational projects, and the digitization of oceans. And I know I have a learning and success journey to share. 

Some Background

I started blogging twenty years back. Yes, 20 years. I was an early adopter and I was involved with technology startups and how the internet was influencing education. At this time I was a big believer that blogging was about content creation. Adding to the collective of the internet by adding meaningful and descriptive content rather than only being a consumer of content. To date, I have published over 500 posts to my assorted blogs. Most of this work was in the first 10 years of my blogging. I essentially posted once a month for the first 10 years. I did have a year where I posted over 100 times. As a summary this is how I posted over the last 20 years.

2004 to 2014 - I published 420 blog posts with good readership. I had a year where I posted more than 100 times. I set this as a goal / experiment to see if I could post twice a week. I did this while holding full-time work, which meant many early mornings and late evenings writing. I learned a whole bunch and my writing skills improved. 

The themes for these first ten years was mostly; 

• technology leadership for startups, 
• hard-core technology and approaches, 
• innovative and emerging education, 
• and the intersection of these three.

2015 to 2024 - I published only 41 blog posts during this 10 years, and nothing in the past three years. Honestly, I was distracted by raising my family of three and doing a whole bunch of life living. Not so focused on work and career advancement. 

The themes for the last ten years have mostly been; 

• integrating with technology community in St. John's NL (I moved), 
• continue my work on digital badges and micro-credentials,
• development of an ocean data startup (still a work in progress),
• and working the idea of a reference architecture for the digitization of oceans.

Most exciting of all this is more than 1/2 million views during the 20 years and at some points having over 2500 weekly views. What have I learned from all this blogging? Mostly, that having to write and publish openly to the internet helps the overall community knowledge and it helps me learn more deeply in these chosen subjects.

Next Steps

Again, I will use blogging as my cognitive gymnasium. My subjects themes haven't changed and I will focus upon two main subject areas and continue with updates to this critical technology blog;

1. Education technology, Heutagogy, and the self-directed learner.
• The Happy Heutagogue - https://heutagogue.blogspot.com/
2. Many things related to, and in support of, the authoring a reference architecture for the digitization of oceans.
• The Ocean Set CTO - https://oceanset.blogspot.com/
3. And my continued musings about technology through my gen X view of the world.
• Critical Technology - https://criticaltechnology.blogspot.com/

With all my work and R&D efforts I will learn a bunch of stuff and apply this to the real world through the successful projects I am a part of. I will reflect upon these successes and all that I have learned I will create content that can provide further learning for those around me. And hopefully they will also be entertaining reads.

Collaborating with your AI partner

Blogging has changed for me. There has been a lot of technical and social change since I did most of my blog posting over a decade ago. I had a few focused subjects I was very passionate about, and I wrote about them often. I wrote unincumbered for I would consider myself an early adopter and there was less people publishing to the internet in my chosen subjects. Today there is much more content covering these subjects. And all this content comes in photos, videos, audio, and written articles. Artificial Intelligence is doing a great job of creating and summarizing the content which addresses the complex audience needs and their questions and prompts. Content creation has changed. For a human content creator I believe our work needs to be more intelligent, critical, and creative. Content creators in a time of AI need to do what the AI cannot; daydream, reflect on unrelated subjects, see unlikely connections, be critical, add meaning, create new content that falls between the generated content, fact check and confirm, and add more human intelligence.

How will my blog writing process change? Um, it already has...

I must reflect and draw upon my mastery and do my best to add the new content that AI cannot... AI needs our creativity because it has already parsed the published body of human knowledge. For more insight on my approach, use your favorite large language model chatbot (ChatGPT, Gemini) with the prompt 'limitations of generative AI' followed by the prompt 'How would you suggest a human writer overcome these AI limitations'.

Step by Step my blogging will partner with AI and follow this basic approach;

1. Capture ideas for new posts, be verbose, be imaginative, think about context
2. Put these ideas to incomplete blog posts, work ideas for days, for weeks...
3. Read extensively, add to the understanding of any specific idea
4. Keep references, cut and paste to the bottom of the related incomplete posts
5. Prompt AI with phrases from the idea generation
6. Take blocks of text from written ideas and push them into generative AI, be critical, harvest what you can.
7. Take the written blog post and ask AI for a rewrite. Change your audience. be critical, harvest what you can.
8. Try and see, try and write, what AI cannot... add to the body of human knowledge.
9. Add story telling to improve the overall post
10. Find pictures to support the writing, format for engagement. Use AI to generate images from passages of text taken from the blog post.
11. Format, edit, improve, repeat. Be bold... Publish.
12. Use AI to improve the quality of the writing... Publish again.
13. Rest, reflect, improve... Publish again.
14. Yes, I am an advocate to publish before writing is perfect. Publish and then make improvements over the days and weeks that follow. Once the post is considered finished finished... promote it on social media.
15. Identify what is most important about the post and rewrite for the LinkedIn business audience. Publish to LinkedIn.
16. Repeat...

Suggested reading: https://www.weforum.org/agenda/2023/02/ai-can-catalyze-and-inhibit-your-creativity-here-is-how/

It's Alive! The Elastic Stack as our Data Lab

So much technical work, so little time! I finished my first three sprints toward standing up the data lab. Standing up infrastructure from scratch so you have clean new compute power is fun, and also a lot of work. Particularly when you include; doing it right, taking no short cuts, and making sure it is secure.

Sprint 0: Setup Ubuntu 20.04 Server with ELK stack.

This was mostly rehydrating virtual server infrastructure I hadn't used in 8.5 years. It needed an upgrade from all perspectives and had a completely new OS. I implemented the ELK stack and made a couple of security changes to lock it all down. I ran a few tests by setting up a couple of websites, getting the JSON confirmation from ElasticSearch, and called up the Kibana dashboard. Oooo... sweet success!

Sprint 1: Vulnerability Assessment. Security changes if required.

This evening I spent some time poking at the overall vulnerability of the server and with the ElasticSearch and Kibana services. I made a few additions and changes for further locking down the services and believe they are as secure as they can be for this first release. Very happy to feel reasonably confident about it's being locked down. Maybe, I'll get lucky and get some free PEN testing. ha.

Sprint 2: Identify and register some well aligned domain names.

I registered the following domain names, even considered buying one... it would have been too expensive. I'll implement the data lab on the oceansofdatalab.com site when it becomes closer to being a minimal viable product (MVP).

• oceansofdatalab.com
• oceansofdatalab.org
• oceansofdatalab.net
• oceansofdata.net
• sevenseasofdata.com
• sevenseasofdata.org

ODE Data Lab has its technology footing

This month has become more about standing up technology than it has been talking to people about their ocean data needs. That's ok... if you are building a technology company, you need to build technology. Ocean of Data Endeavours (ODE) is about building and utilizing software towards making it super easy to work with data, large amounts of data.

The last 10 days have been about refreshing a server infrastructure I stood up 12 years ago for a number of other projects. What was left was a couple of simple websites, some domain hosting, and all the related mail server infrastructure. All of this needed a complete refresh to be brought up to date;

1. Rebuild the server infrastructure to have more horsepower. - DONE
2. Upgrade the Ubuntu OS from 10.04 (Lucid Lynx) to 20.04 (Focal Fossa). - DONE
3. Rework all the domain aliases to remove dependency on a domain I no longer owned. - DONE
4. Do some basic security work to the server. Mostly SSH focused. - DONE
5. Create a new mail server, and do some mailbox maintenance. - DONE
6. Install Apache2 httpd host. - DONE
7. Configure Apache2 for a couple of web sites. - DONE
8. Code some basic HTML to confirm the sites are working. - DONE
9. Celebrate! http://endeavours.com/

So good to have all this done. The server will provide a strong foundation and is well prepared for the ELK stack and the first load of ocean data. So excited! 

Peter, we need to integrate data more easily.

I liked this article on Fundamental truths when it comes to innovators. I do think Elon Musk and Jeff Bezos have figured out how to be crazy successful in business. I do wish they were more philanthropic with the monies they have accumulated from their success. I digress...

I like the idea of a fundamental truth as a foundation for your business that doesn't change through time. And when I think about my commitment to build a data services business, I have now started to think about what would be the fundamental truths?

1. We need to access and integrate data more easily.
2. We need better ways to visualize, communicate, and understand, the data.
3. We would like to reduce the compute and storage costs of data.

These are what I came up with through my review of my initial thinking of fundamental truths. I know there will be a few more and these three will be edited as my idea grows and gets greater footing.

Building the Data Lab Technology Stack

The idea of building a data lab is emerging from my ocean data conversations and how to best utilize my knowledge and skillset within this opportunity. In my mind, the service offering would be twofold;

1.  Data Engineering / Software Development consulting and services with focus on ocean data. We will do the heavy lifting of extracting, cleansing, transforming, and loading your data. And then we will help with analysis and visualizing the data. We are comfortable working in both the open source and Microsoft technology stacks.
2. Standing up (and data loading) the technology stack for the data lab. You are going to need to host all this compute power and storage somewhere. It could be on-premise. Most likely, it will be in the cloud. We can help with this also. We could build it in Azure, using the Microsoft technology stack. Or we could build it using an Open Source stack on top of Linux in any of the hosted environments of Azure, AWS, or Rackspace.

https://stock.adobe.com/

How do you build a low priced, large compute, technology stack to support data engineering efforts, implement a data lab, and showcase these new services capabilities. The low price is the key factor given the current startup state of this ocean data endeavour. Particularly, when you think of the cost of compute for processing and storing large amounts of data. I believe the the best way forward is as follows;

1. Use open source where you can. Fortunately, many of the infrastructures, tools, frameworks, and programming languages for the data lab are open source.
2. Automate the build so it can be built and torn down with ease. This would eliminate the need for the stack to always be running.
3. Store the data at it's source, if possible. Fetch, and load, the data when you automatically rebuild the stack. Keep in mind this limits the amount of big data you can store locally, and loading large amounts of data can cumulatively take days. Be mindful of this. 

Note: this stack is to showcase the services capabilities. A full data lab would also need the ability to both persist and fetch data. It's going to take some time to build the data lab!

The Data Lab Technology Stack

The deployment of this technology stack will use open source wherever possible running on a Linux (Ubuntu) Server hosted at Rackspace. The rational for these decisions are;

• Little to No licensing costs 
• Strong familiarity with Rackspace as hosting company
• Existing domain name (endeavours.com) hosted with Rackspace
• Extensive experience with Ubuntu Linux in a hosted environment
• Familiarity with deploying data intensive solutions using the ELK stack
• Experience programming in Python

Note: The deployment of this technology stack will happen in phases, where each phase will complete with some basic tests to ensure the stack behaves as desired.

Phase 0

Phase 0 will be a basic ELK stack running on an Ubuntu Linux server hosted at Rackspace with access via the endeavours.com web domain. The use case for where the data comes from, how we transform it, the analysis, and visualization is still to be determined. This use case will be used for testing this first iteration of the newly stood up data lab. Exciting times!

Phase 1

During phase 1 we will add the Python programming language to the technology stack and use it for two purposes;

• Apply a model to the data using Python.
• Present the processed data to a web page for display.

Phase 2

During phase 2 we will add Kafka as an infrastructure resource, identify some additional data sources, and pre-process the data before it gets loaded into ElasticSearch.

Phase 3 and beyond

Investigate the Apache Data lab stack, add Spark to our lab, add a data workbench...

The Beginning of Ocean Data Endeavours (ODE)

Thirty-nine months ago I started on a deep dive into developing a reference architecture for the digitization of oceans. The idea of developing this reference architecture was initiated by the Canadian Government awarding Atlantic Canada with the Ocean Super Cluster initiative and all my recent work with leading the software engineering group at Provincial Aerospace. My writing and research into ocean data took me to the point where I needed to deepen my understanding of a number of subjects, and I needed this deeper understanding before I could continue the writing and research (even though you could consider deepening understanding as research). I needed to have an intermediate understanding of what had come before and the current state of things with a reference architecture for the digitization of oceans. In particular, I needed to work directly with ocean data and the standards that influence its structure.

Over the last three years I have been lucky to work with Triware Technologies Inc., and together we have found projects that align with this need to deepen my understanding of all things digital and all things ocean. My recent project successes include;

OCIO Digital by Design - I was fortunate to be awarded the opportunity to be the data architect for the initial phase in digitizing the NL governments citizen facing portal. I remained on the project for the first 12 months through to the portals launch. Being on the design team to create the data tier and integrate with legacy data was a great achievement. And I deeply enjoyed using a scrum / jira approach with a multi-vendor, multi-disciplined team. We achieved a lot in a short period of time.

Lessons Learned - Agile, Scrum and Jira can scale well to a government organization with multiple scrum teams working toward an integrated solution.

Ocean Sector Search - We needed a way to index the Canadian Ocean Sector. So we built a search engine seeded by as many oceans related URLs as our analysts could gather. The technical architecture of this ocean specific search engine can be found in this previous post.

Lessons Learned - With reasonable technical effort Nutch can be configured and seeded to crawl a specific industry sector (in this case Canada Oceans Sector). The Nutch crawl harvested a significant number of pages (> 32000) that were then loaded into the ElasticSearch (ELK) stack while relevancy scoring each page along the way.

NLCHI - My work with the Newfoundland and Labrador Centre for Health Information (NLCHI) was a quick engagement to focus their requirements backlog into a few manageable sprints. I was super fortunate to help get an important project underway and gain insights into the concept of a customer focused data workbench for a specific subject domain.

Lessons Learned - The idea of a personal data workbench is very compelling when you consider the number of data sets already available in the oceans sector. And if we could fold in open and proprietary data sets, while honoring security and privacy we may be onto something...

Nalcor Energy Database Consolidation - So many databases, so little time. One of my favorite enterprise type projects is when the project pays for itself, over time, by the savings created by the projects downstream accomplishments. Not revenue generation, but operational expense savings. I believe one of the best KPI's for IT is not new systems implemented, but old systems retired.

Lessons Learned - an amazing amount of data can be moved with the correct use of tools, well built and managed ETL (pipelines), and a mindset of automation.

Argo Floats - 2018

NEXT STEPS

Over the last month I have revisited how to best develop my intermediate understanding of oceans data. After a number of conversations, with experts of oceans data, I believe my next steps are twofold; I need to focus on the existing standards for oceans data and I need to write some code to integrate some open oceans data sets.

I need to find opportunities to work directly with oceans data. If you are in the oceans sector, in any way, and you have the need of a very experienced data engineer, then I would love to help with your project. If you know of an oceans data project in needs of a data engineer, please forward on my credentials. Thanks to everyone for reading this far. And thank-you Triware for your ongoing career support!

Seeking New Opportunities

TLDR;
Veteran technology professional seeking new opportunities. If you are requiring 35 years of career success in the software technology realm that spans startups through to large enterprise environments, then I'm your guy!

I have tonnes of experience and I can hit the ground running. I can work as a senior project manager, an enterprise solution architect, as a scrum master and build a team focused with agile approaches, I can design databases and related structures for your business analytic projects, and I can own your Business Intelligence initiative. I can provide strong, and proven, leadership. And if need be I can occupy the technical director level. You will find I provide great business benefit and good value, my experience provides the ability to save you more than I would cost.

A history of project success
I'm back to consulting in the technology realm. Being a technology consultant has provided my clients, and myself, many project successes. These successes have been both with technology startups and with medium to large business environments. For more details describing some of my project successes please read these two posts describing projects I have managed and provided technical leadership over the last decade;

Increasing Access to Education - this collection of eight projects helped CLEBC become one of the globes premier legal educators.

Career success in three year cycles - These four major corporate initiatives leveraged all my abilities to help PAL into their next level of technology capability and realized business value.

Where I can help the most

I can have an immediate positive impact to any technology initiative within large and small organizations. This success can come with startups and enterprise environments, or something new that can leverage my skills, knowledge, and experience.

There are five roles to where I can bring the greatest immediate business value;

1. Enterprise and Solution Architecture - The solution side of designing technical architecture has most often been my preferred role within a software development initiative. I've done this for small startups and large enterprises. Good solution architecture creates a better technical solution that saves development costs, improves quality, and ensures alignment with business needs and the other technologies within the business ecosystem.
2. Senior Project Management and/or Managing a Software Development Teams - Through time I have managed many projects with software development teams varying in size from 3 to 30. I have proven track record of bringing projects to completion on-time and on-budget. I seriously enjoy assisting a team of technical people to ship software.
3. Data Analytics Project (Architecture and Team Management) - The B.Tech undergrad degree I completed in the 80's was focused on database management. And since that time my career has had data as its foundation. I do very well with data management [including the design of data structures and related communications (read API's)]. I can also work well within big data projects as my experience with big data goes back to the 1980's. Read one of my big data posts from a few years back to get a sense of this capability; Big Data; Similarities and Differences.
4. Technical Mentorship - I'm an educator and have experienced many startups during my time in Vancouver. If you want to discuss the technical side, the team management side, and/or the development methodology to an innovation project (startup or otherwise) I can help here.
5. Innovation - Many times in my career I have taken business strategy and made it happen. If you have a business strategy that requires software innovation I can make it tactical and implement. 

I'm willing to travel

A single flight from St. John's Newfoundland is my preference. I would also be very interested in consulting work in Vancouver (my home town). I have family there and could easily have all the amenities I require for extended working stays. In a previous blog post I describe how I consider the size of the St. John's marketplace to be 40 million people. I also need to consider the St. John's market to be a single flight away as I seek out new challenges and opportunities. My list of cities I will immediately consider consulting opportunities, include;

1. Halifax: it's a morning flight away, and can be there and back including a full days work. I'd also stay for a few days if required.
2. Toronto, Ottawa, Montreal: single 3 hour flight, couple of days then return home for remote working
3. Vancouver: longer stays and longer aways... I have family in Vancouver, so it would be a very nice approach to working.
4. London, England: An outlier, but... It would be nice to work in the EU and I have a British passport so it would ease my travel and work permitting.

One personal constraint
To be succinct, I require work-life balance... mostly it is about having time to care for middle-school age children and aging parents. This means I am available part-time or full-time with loads of schedule flexibility.

The oceans data collection platforms

My last post from the end of 2017 spoke to the number of sensors and devices that are currently, and becoming easily, available for data collection. This previous post focused more on the generic types of end-points and sensor technology. In this post we focus on the oceans and look at the variety of end-points used in the maritime environment.

Underwater Sensors
As previously identified there are a growing number of sensors and devices available for collecting data in all types of environments. This is also true for the growing number of "platforms" in which these sensors can be deployed. When we extend our view of sensors to include those used in oceans the list of data capture technologies grows. What is important, beyond the details of the data being emitted by these sensors, is how many are becoming a part of the digitization of oceans data "ecosystem". And with so many sensors, and therefore, so much data... a standardized approach and reference architecture defining the approaches to coalesce all this data will become increasingly important.

The oceans data collection "platforms"

Weather buoys
Weather buoys are instruments which collect weather and ocean data within the world's oceans, as well as aid during emergency response to chemical spills, legal proceedings, and engineering design. Moored buoys have been in use since 1951, while drifting buoys have been used since 1979.


An Integrated Ocean Data System 
Spotter is a web-integrated solution for collecting ocean wave and surface current data, designed from the ground up to be easy to use, intuitive, and extremely low cost. The Spotter Device is a compact, solar-powered, surface-follower, which measures surface waves and currents. Through our online Dashboard you can remotely configure your Spotters, access your data in real time, visualize wave data and position tracks. Your Spotter is already connected, so all you do is turn it on and focus on collecting the data you need.

Autonomous Underwater Vehicle (AUV, or unmanned robot submarine)
AUV are increasingly non-proprietary and made up of “plug-and-play” AUV modules which can be brought together and configured in the field. When assembled with a set of survey-grade sonar modules, a Gavia AUV becomes a self-contained survey solution with a low logistics footprint that is capable of carrying out a wide range of missions for commercial, defense, and scientific applications.

Autonomous Underwater Observatories

A submersible platform enabling tailor-made solutions with sensors from a variety of sources. They can be used off-the-shelf or customized to better cover application requirements. They have the ability for long-term deployments and the ability to collect and transmit data through a variety of methods. As an example, review the deep argo a submersible device for collecting data at extreme depths.

Others...
The list doesn't stop at what is described here. Reading and research on ocean sensors and the digitization of oceans will direct the reader toward the large number of devices collecting data in and around the ocean. Honestly, I'm amazed with the number of devices and the amount of data currently being collected about the oceans. I am increasingly of the belief the we need a reference architecture for the digitization of oceans... Actually, I'm surprised there isn't one already... for it would certainly help to bring together all the oceans data collection on a global scale for the good of us all. If you have any knowledge of an open reference architecture for the digitization of oceans please forward this information along. Thank-you!

Over the next few months I will be publishing a series of blog posts describing, in more detail, all the aspects for building a successful digitization of oceans reference architecture. Next up is; "communications" with focus on the data communications available to oceans technology. Please follow along and make comment. For a table of contents of these coming posts please review a companion post; Digitization of Oceans Reference Architecture TOC

three posts for digitization of oceans reference architecture

The next three posts within my series describing the need for a digitization of oceans reference architecture will be focused on the three technology domains of; end-points, communications, and data stores. This separation is to allow a deeper look into each domain as they have different considerations in relation to technology architecture and attributes important to the digitization of oceans.

End Points: the sensors and devices which collect and emit data. Consider this the Internet of Things (IoT) that can be located anywhere within and around oceans, airborne, surface, and submersible.

https://en.wikipedia.org/wiki/User:Peterrawsthorne/Digitization_of_Oceans#End_Points

Communications: the communications technologies available to transfer data from one place to another. A lot to explore within this domain; as underwater data transmission is an emerging technology, and the structure of the data messages will become the foundation of the reference architecture.

https://en.wikipedia.org/wiki/User:Peterrawsthorne/Digitization_of_Oceans#Communications

Data Stores: there are many existing data storage approaches, locations, and structures that can be used to store the oceans data. Databases and database designs are already available for many of the subjects within the digitization of oceans. Better to use exiting methods to store the structured and unstructured data and use a federated approach to bring them together.

https://en.wikipedia.org/wiki/User:Peterrawsthorne/Digitization_of_Oceans#Data_Stores

Creating an inventory
The number of technologies, vendors, standards, and approaches within these three domains will be large and forever growing. To start documenting this inventory I have created a Wikipedia page under my Wikipedia account. Once this page describing the Digitization of Oceans and its Reference Architecture is more complete I will submit it as a published article otherwise consider it a work in progress. Feel free to join in and edit the work in progress wiki page;

https://en.wikipedia.org/wiki/User:Peterrawsthorne/Digitization_of_Oceans#Lists

What is a reference architecture?

There are many descriptions of reference architectures available on the web. Here is a list of some I consider do a good job of describing the subject while supporting the description I am working  toward in the digitization of oceans;

1. Reference Architecture: The best of best practices - given its age (published 2002) it is still relevant and pragmatic. Though I do consider the description too dependent upon RUP, which introduces many weighty practices and misses some of the more agile and emergent approaches. Still the description gives good detail to the importance, breadth, and depth of the reference architecture. The later sections of this description provide information on creating, using, updating, and working with a reference architecture. These sections are particularly useful in developing the digitization of oceans reference architecture. I strongly believe the oceans reference architecture will be emergent as many new technologies and stakeholders contribute and become a part of developing the architecture.
   

   Emergent architecture is when organizational structures such as business processes and technologies are designed incrementally by many designers.

2. Wikipedia: Reference Architecture - very short for a complex topic, but it is too the point in defining the reference architecture as templates within a subject, industry, or domain. It stresses the importance of a common vocabulary and in drawing upon successful projects within the domain. It aligns with the use of APIs which I believe will become an important part of a strong digitization of oceans reference architecture.  It also calls out a number of the benefits derived from the reference architecture.
   
   
3. CIO Online Magazine - describes where the reference architecture fits within the EA toolkit, and looks to all the relationships among business, systems, and technology. It describes how the reference architecture can greatly assist in defining specific technical deliverables within these complex systems. Having a proven, standards based, and shared toolkit for developing the oceans reference architecture will assist in keeping the architecture team distributed throughout Atlantic Canada well aligned when creating and maintaining the reference architecture..

Some example reference architectures:

1. Microsoft Industry Reference Architecture for Banking (MIRA-B)
2. A Reference Architecture for The Open Banking Standard
3. IBM Insurance Reference Architecture
4. Healthcare Reference Architecture

These four serve as examples of reference architectures from established industries where the patterns, technologies, and architectures have developed through time. As you read through these you can get a sense of the value, industry collaboration, growth, and innovation that can be facilitated by having a comprehensive industry reference architecture.

What is unique about a digitization of oceans reference architecture?

The infrastructure of the Digital Ocean. (Courtesy of Liquid Robotics, a Boeing Company)

It's too early in this discussion to be specific about the oceans reference architecture, it is important to note that it is both broad and deep. It is broad in that it includes many ground based systems, processes, and infrastructure similar in complexity to the previously mentioned examples. In addition to this broadness we need to add the theater in which the digitization of oceans operates; we have vessels of many types (airborne, surface, and submersible), we have a growing collection of sensors and protocols, we have cross industry collaborations (fisheries, environment, oil and gas, shipping, researchers, academia, defense, etc.). I believe it is safe to say the reference architecture for the digitization of oceans will be very broad due to the number of data collection points and the number of intersections (technical and otherwise). The oceans reference architecture will also be very deep in that the data will be coming from many sources above and below the ocean surface. And the data that is being collected will both be very specific and detailed, while also being general at a more meta level. I believe it is the breadth and depth of the digitization of oceans that make its reference architecture unique. And its creation is a large, important, and emerging challenge... more on this to come.

Over the next few months I will be publishing a series of blog posts describing, in more detail, all the aspects for building a successful digitization of oceans reference architecture. Next up is; "a plethora of end points" with focus on oceans technology and the Internet of Things (IoT). Please follow along and make comment. For a table of contents of these coming posts please review a companion post; Digitization of Oceans Reference Architecture TOC

Digitization of Oceans Reference Architecture TOC

For a sense of where I am going with this series of posts describing a reference architecture for the digitization of oceans, please consider this "table of contents". As I complete items in the list, I will update this TOC;

1. Introduction - summary of why a series of posts describing a digitization of oceans reference architecture.
2. What is a reference architecture? - summary of the existing online descriptions of reference architecture and why it is important to building a strong technology ecosystem.
3. A plethora of end points - with new Internet of Things (IoT) end points coming available with increasing frequency we look to how many sensor types are available to an oceans reference architecture and some examples of how they are being used. I've included a couple more posts describing end points, as I deepened my research I felt the descriptions needs to be expanded to include what is happening as end-points in the oceans and in the air.
• Oceans end-points
• Airborne end-points
4. Communications - description of the current state of data communications above and below the oceans surface. And why it matters to the reference architecture.
5. Messaging Standards - the structure of the data packages (or messages) between the endpoints is a very important attribute of a successful reference architecture.
6. What is the digitization of oceans? - a high level description of the digitization of oceans. This will detail the breadth and depth of what is considered the digitization of oceans. This description should also consider the intersection of the different ecosystems of; business, innovation, and knowledge.
7. What is a digitization of oceans reference architecture? - comprehensive diagram of the entities within the oceans reference architecture with detailed description of each item and their connections (digital or otherwise).
8. The importance of good governance - the dynamic nature of innovation within the digitization of oceans will cause many elements of the reference architecture to be changing. To encourage interoperability at all levels (technical and otherwise) having good governance will be paramount for success.
9. The economic value to be found in the oceans reference architecture - why is a reference architecture valuable for community, business, innovation, etc. And why Atlantic Canada should be a major contributor or primary steward of the reference architecture.
10. How to create the digitization of oceans reference architecture - what is the road map in completing the first release of a digitization of oceans reference architecture. I purposely say first release as this reference architecture will need constant tending as new technologies and capabilities come available.

Keep in mind this is meant to kick off a conversation about creating a reference architecture for the digitization of oceans. This is NOT something I want to do on my own, or believe I could do effectively on my own without contributions from others and a few years of focused effort. I really want engagement across Atlantic Canada to discuss the idea of creating this reference architecture and to become stewards of the reference architecture as it is used globally for the benefit of everybody.

Disclaimer - All views expressed on this site are my own and do not represent the opinions of any entity whatsoever with which I have been, am now, or will be affiliated. They are views created by my many years as an IT professional and, more importantly, an enterprise architect responsible for building large and distributed systems.

Reference architecture for the digitization of oceans

I strongly believe one of the cornerstones for the successful digitization of oceans is a reference architecture. I believe this also holds true for the digitization of oil and gas, but I see this digitization as a subset of the oceans. Or more specifically, I see the digitization of maritime oil and gas as a subset of the digitization of oceans. I regress... One of the most important aspects of the reference architecture is its openness (as opposed to proprietary). If we are wanting to spur innovation in Atlantic Canada we need every small, medium, and large organization to realize benefit from shared digital resources. We need a way for all these organizations to openly communicate and build this digital ecosystem. The digitization of oceans reference architecture will define (or utilize existing approaches) the "language" that all oceans technologies communicate with one another and remember their collective history.

An example; the reference architecture would specify the digital messaging structure for an ocean temperature event. Therefore, when a small startup (that specializes in ocean temperature sensors) needs to publish their data they need only comply with messaging structures for ocean temperature. This would allow everyone in the ecosystem to get at their temperature event data as soon as it is available. Also important, is the startups market for ocean temperature sensors customers includes everyone who is aligned with the oceans reference architecture and it's messaging structures.

Another example; the reference architecture would specify the underwater wireless communications approaches and suggested protocols and practices. All allowing the temperature event data to be broadcast and communicated to the historical repository for archiving.

Another example; the reference architecture would specify all the messaging structures and the data storage approaches so the data could be archived and be available through time. The historical archive would allow for retrieval, searching, research, planning, and analysis.

Over the next few months I will be publishing a series of blog posts describing, in more detail, all the aspects for building a successful digitization of oceans reference architecture. Next up is; "what is a reference architecture" with focus on oceans technology. Please follow along and make comment. For a table of contents of these coming posts please review a companion post; Digitization of Oceans Reference Architecture TOC

Disclaimer - All views expressed on this site are my own and do not represent the opinions of any entity whatsoever with which I have been, am now, or will be affiliated. They are views created by my many years as an IT professional and, more importantly, an enterprise architect responsible for building large and distributed systems.