Looking Forward to 2019 in Graph Technologies

I started out 2018 with a new focus in graph technologies. This year I will continue to look at graph technologies and how they are disrupting the status quo. This report is an analysis of some of the key graph database trends in 2018 and my predictions for 2019.

Summary of key graph database events in 2018 and predictions for 2019

I should also put in a quick disclaimer. My employer (Optum Technologies) has a policy of not endorsing any vendors. This blog is only my personal observation of trends in the graph industry and is not an endorsement of any organization or product.

2018 — The year of the LPG knowledge graph

TigerGraph has gained widespread attention for their combination of scalability and security. In September TigerGraph won the Most Disruptive Startup Award at the Strata conference. The key fact here is that distributed graph processing is hard. How a vendor partitions a large highly interconnected graph database and how they keep queries performant while maintaining ACID compliance over a distributed cluster is a wicked hard problem. TigerGraph has also introduced innovations such as accumulators in their GSQL language that have attracted the interest of developers.

Neo4j’s Bloom product, although expensive on a per-user basis, is of interest because it is starting to blur the lines between graph visualization and natural language query processing. Many Bloom queries minimize the need for knowing Cypher and focus on allowing non-technical people to do complex analysis of graph databases. Noe4j’s Bloom product and vendors like Cambridge Intelligence (KeyLines) and companies like Linkurious will continue to make graphs easier to query for the non-programmer.

Neo4j continues to break new ground with making it easier for new developers to pickup graph technologies. In 2018 they made significant enhancements to their Neo4j Desktop as well as providing extensive documentation on their graph algorithms. My congratulations to both both Mark Needham and Amy Hodler in writing their book A Comprehensive Guide to Graph Algorithms in Neo4j. We need more high-quality writing like this.

The Open Algorithms Movement

One prediction for graph product managers is that in the future, unless your graph database can run thousands of standard graph algorithms you will be at a disadvantage. This leads to the question of will there be a standard for LPGs so that innovative graph algorithms can have a wider impact. That question, along with the question of should paths be treated as first-class citizens, will be addressed at the W3C Workshop on Web Standardization for Graph Data on March 4th-6th in Berlin Germany. If you read between the lines, you see the W3C is clearly aware that LPGs have a large majority of the market and that SPARQL based standards may no-longer be relevant. Let’s all wish the W3C “good luck” so that we can continue to have shared standardized repositories of knowledge and standard algorithms to traverse these repositories. Note that I am not saying that RDF is not important here. On-the wire semantic standards and things like namespaces and URIs are going to increase in importance as we get more data. It is the query language that encodes the algorithms that has innovated faster than the standards bodies could keep up.

Overlay Graph Products Stagnate

Graphs and AI

Although many people are enthusiastic about the ability of GPUs to quickly do matrix math, the real world does not do this. Our brains don’t have any circuits to do matrix multiplication and yet we reason far better than AI systems that use them. The key insight here is that both GPUs and our brains need to quickly do parallel non-linear transformations of data to seek insight into pattern recognition. GPUs just happen to be the most parallel devices around. I try to tell everyone around me that there is no clear binary division between graph-based rules engines and inference rules generated by deep-learning algorithms. Deep learning rules are just larger and harder to explain. In order to have explainable AI we need to bring both graph-rules engines together with machine-learning systems. Vendors that do this well with have a distinct advantage.

Graphs and Entity Resolution Rules

Graphs and Corporate Social Networks

Open Knowledge Graphs

Custom Graph Hardware, FPGAs and the Future of Graphs

Graph Writers Wanted

In summary, I think that 2019 could be a key inflection point for graph technologies. AI can’t make progress without strong knowledge representation. Distributed native graph databases with strong access control hold many promises, but it is still difficult to predict when a mature ecosystem of graph database add-ons will provide complete solutions. I try to remember the Paul Saffo quote: Never confuse a clear view with a short distance. It is clear to me that distributed and secure native LPGs are going to dominate the database market and replace not just SPARQL but many relational systems. I just can’t tell you how quickly this will happen.

Happy New Year everyone!

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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