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Lead by Intel Capital and Dell Technology Capital, venture capital investors set the EKG industry on fire! Photo by Jp Valery on Unsplash

The last few weeks have been busy in the Enterprise Knowledge Graph (EKG) space. This blog will review the key events and put them in context for a new generation of Graph Systems Thinkers trying to understand the big-picture trends that will dominate the computing industry for the next few years.

The first event was the announcement that TigerGraph got an additional $105M in venture capital in its Series C fundraising round. This should be no surprise since TigerGraph already has a wide lead in the EKG market because of three facts:

  1. It was designed from the ground up to…

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The Raspberry Pi Pico has 1/625th the cost/KB SRAM as the Arduino Uno. Artwork by author.

On January 21st, the Raspberry Pi Foundation (a UK-based Charity) announced a new silicon chip that it designed. This chip, called the RP2040, has become the hottest development in the Maker Movement in the last five years. The RP2040 is at the heart of the $4 Raspberry Pi Pico development board.

The Pico is revolutionary because it offers over 100x the capabilities of the industry-standard Arduino Uno system at 1/5th the cost. At 2 cents per KB of SRAM, the Pico has 1/625th the cost-per-KB cost that of the Arduino Uno. There are many other blogs that compare the “Speeds…


Unlike the Raspberry Pi, the NVIDIA Jetson Nano does not come with a builtin sound device. To get sound working you have to purchase an additional USB sound dongle for $7.99. Getting this working with the new NVIDIA Jetson Nano LXDE desktop is a non-trivial process.

TL;DR; Check the device is present with lssub. Use the pmac list-cards command to find the driver name. Then use the Sound & Video ->PulseAudio Volume Control to set the correct Output for sound. Plug you speaker into the Green jack.

Getting sound working on the NVIDIA Jetson Nano is required for you to…


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Some attributes of a sustainable scale-out enterprise knowledge graph (EKG). Drawing by the author.

Last week, I was discussing the key features of an Enterprise Knowledge Graph (EKG) with some colleagues, and I realized that although we were using the same words, we were talking about different things. We had a problem with the semantics of the word “Enterprise”. This is a bit ironic since many of these people I was talking to had a strong background in semantics.

Many people co-mingle the terms from open linked data world and the semantic web stack's role with the concepts related to sustainability and scalability of enterprise knowledge graphs. My assertion is these are independent and…


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My overall grades for GPT-3 in generating course content for STEM courses. The grades are more a reflection of my skills in generating the right prompts. Image by the author.

This blog is a description of how to use the OpenAI GPT-3 generative natural language processing model to generate lesson plan content for STEM courses. We will show you what components of a course are easy to generate and how to tune the prompts to get better results. We will also show how you can tune the generated content that is age-appropriate for your classrooms.

These processes should also work for generating content for non-STEM courses. However, my focus is to help content managers generate technical content such as sample code, math formula (LaTeX), charts, chemical symbols, and architectural drawings…


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One of our older desk-side server with two Nvidia GTX 1080Ti GPU cards. Each card has 11GiB RAM and 2,560 cores and currently originally retailed for around $700 each. These cards are now two generations old and the latest GTX 3080 cards are about 100x faster.

We are trying to make the process of setting up an AI Racing League event as turn-key as possible. Central to the event’s success is allowing students to be able to quickly train a deep learning model in under 10 minutes. Although the Raspberry Pi and Nvidia Nano are excellent for gathering driving images and running real-time driving inference, they are just too slow to train your model on 10,000 image files. There are two options:

  1. Get a high-speed internet connection at your events and give everyone cloud-based accounts for doing their training.
  2. Have an on-site GPU server that the…

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Three examples of central-nervous systems: Sponges, Octopuses, and the Human Brain. Will Enterprise Knowledge Graphs evolve specialized structures like the human brain? Artwork by the author.

This blog will speculate on how enterprise knowledge graphs (EKGs) will evolve to contain specialized functions and specialized subgraphs. We will use metaphors from the evolution of centralized nervous systems (CNS) in primitive life forms to make some key points about architectural trade-off analysis.

If EKGs are really going to become the centralized “Brain” of organizations, they will need to evolve from where they are today to take on new roles. We also need to understand the challenges of depending on central control of organizations.

EKG and CNS Metaphor: Sponges, Octopuses, and the Human Brain

We will be using the metaphor of the CNS in organisms to discuss how EKGs…


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Concept graph for my 2021 Enterprise Knowledge Graph trends report.

This is my third annual post on Enterprise Knowledge Graph (EKG) trends. You can also find my 2019 and 2020 posts on this blog, and I think you will find several consistent patterns in these three posts.

Graph Database Continue to Grow in Popularity

Interest continues to grow in EKGs. We can see from the DB-Engines popularity change chart below that Graph Databases still outpace interest growth of all other database types by a wide margin.


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A few of the many types of rules in the enterprise. Which ones will benefit from the 1,000x speedup in graph hardware?

Should you store business rules in your Enterprise Knowledge Graph (EKG)? This is a non-trivial question, and how you answer it might change your enterprise knowledge graph strategy.

A few weeks ago, I wrote an article on the incredible new Intel PIUMA chip architecture and how it will change the face of computing by offering a 1,000x improvement in knowledge graph traversal performance. These chips could be manufactured at a low-cost and integrated into many devices and portend many changes to the computing industry and software design. This is not just the end of the relational database era. …


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A sample of customer data in a knowledge graph and the embedding vector attached to the graph.

In the last year, graph embeddings have become increasingly important in Enterprise Knowledge Graph (EKG) strategy. Graph embeddings will soon become the de facto way to quickly find similar items in large billion-vertex EKGs. And as we have discussed in our prior articles, real-time similarity calculations are critical to many areas such as recommendation, next best action, and cohort building.

The goal of this article is to give you an intuitive feeling for what graph embeddings are and how they are used so you can decide if these are right for your EKG project. For those of you with a…

Dan McCreary

Distinguished Engineer with an interest in knowledge graphs, AI and complex systems. Big fan of STEM, Arduino, robotics, DonkeyCars and the AI Racing League.

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