back to article Graph databases to map AI in massive exercise in meta-understanding

Emerging from a niche in the database market, graph technology could actually be the thing to help us make sense of all the AI we're using to understand the world and our business in it, according to Gartner. No longer the last on the shopping list of new database trends, graph processing will grow 100 per cent annually to …

  1. Pascal Monett Silver badge

    "graph processing will grow 100 per cent annually to 2023"

    Easy prediction.

    When you're nowhere, doubling your market penetration isn't difficult.

    When the market is saturated, you don't increase by 100%. You'll be lucky to increase by 5%.

    Gartner remains Gartner.

  2. ThatOne Silver badge

    Almost there

    > the language generator has produced "factually wrong and grossly racist text"

    In short, almost human...

  3. Wowbagger42

    What time is it, you say?

    If there's one company I wouldn't even trust to tell time it's got to be Gartner...

    1. N2

      Re: What time is it, you say?

      And Forbes

  4. Paul Kinsler

    ... analyse network relationships

    .. at which point you might decide to start with some basics, like here:

    The Atlas for the Aspiring Network Scientist

    Michele Coscia

    Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets of nodes connected by sets of edges and a number of node and edge attributes. This deceptively simple object is the starting point of never-ending complexity, due to its ability to represent almost every facet of reality: chemical interactions, protein pathways inside cells, neural connections inside the brain, scientific collaborations, financial relations, citations in art history, just to name a few examples. If we hope to make sense of complex networks, we need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, and more.

    This book aims at providing the first access to all these tools. It is intended as an "Atlas", because its interest is not in making you a specialist in using any of these techniques. Rather, after reading this book, you will have a general understanding about the existence and the mechanics of all these approaches. You can use such an understanding as the starting point of your own career in the field of network science. This has been, so far, an interdisciplinary endeavor. The founding fathers of this field come from many different backgrounds: mathematics, sociology, computer science, physics, history, digital humanities, and more. This Atlas is charting your path to be something different from all of that: a pure network scientist.

  5. Steve Channell

    The next graph database is not a graph database

    Graph theory can be applied to any form of information (just like relational theory), but that does not mean you need necessarily to structure it as a network of nodes connected by edges.

    If you want to find out if Vladimir Putin is connected to Donald Trump on LinkedIn, if Boris Johnson is related to Joseph Stalin on a DNA tracking service, or if Taliban are financed by heroin; a graph database is an excellent solution because the “graph” is unbounded. Pandemic contact tracing is also an unbounded graph, but a graph database is not a good solution because of the rate of change.

    Language parsing and Bill-of-materials are also graph problems, but the best solutions is to assemble them in memory because they are bounded and finite in scale and apply complex constraints. Constraints are the weak point of graph database because “structure” is not included in meta-data – you can either apply constrains outside the database or suffer the performance problems of interpreting predicate logic.

    The next “graph database” will take advantage of increased computer memory and GPGPU to traverse graphs in parallel.

  6. This post has been deleted by its author

  7. Kevin McMurtrie Silver badge

    The key thing to keep in mind is that graphs are, indeed, everywhere, they are in our brains

    AHHH! Get it out! Get it out!

  8. RobLang

    This isn't new. None of this is new.

    Bayesian Belief Networks use Bayes conditional probability theory (1763) in graphs to infer knowledge and back in the late 1990s I knew people combining that with feed-forward neural networks to create "hybrid models". Each neural network is trained to do a specific thing and the graph network can be used to build meta relationships between inputs and outputs across the broad variety of networks. Graph databases are great for certain (uncommon and stationary) data problems but I don't feel like this is one of them.

    The difficulty then as now is that it depends on the quality of the data and training mechanism: "crap in, crap out" as we used to say. Adding a meta layer over the top isn't going to solve that.

    1. Tom 7

      Re: This isn't new. None of this is new.

      There are a group building networks based on real networks from wildlife. They've done flight control and visual from the Honey Bee and produced networks that are far far better than trained versions and 50 times faster. I am looking forward to the bit where they find the bit that does the graph optimisation things we need to know to manage all this old shit. I bet someone will try and patent it but I dare say they wont be able to cover all species with rounded corners.

  9. RLWatkins


    I'm sorry, but a headline like that reminds me of listening to a glib twelve-year-old who knows nothing about math or physics trying to explain string theory to a crowd of his high-school dropout relatives. It's catchy, but one struggles to find any actual meaning in it.

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