Category "Graph Visualisation"

Why Knowledge Graphs belong to the Kindergarten

Aviation, Big Data, Graph Visualisation, Machine Learning, Press release, Visual Analytics, Visualisation

Knowledge graphs are dead for predictive graph analytics – long live dynamic knowledge graphs

Graph Analytics on Knowledge Graphs is currently hyped by many technology prediction companies. “Gartner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021” (Ref. 1) with a phenomenal growth at a CAGR of 32.64% from 2020 to 2027 and reaching a projected market size of ​​USD 5.4 Billion by Verified Market Research (Ref. 2).

They are right that graph analytics has demonstrated its impact already in scientific publication interconnection, innovation and collaboration spotting, know your clients (KYC) in banking, marketing and many other applications. They are also right that we just start to use graph analytics to its full potential and continuously identify new applications. But many insights that are currently promised are not deliverable with the toolsets we currently have in hand. Dynamic Knowledge Graph (DKG) systems are one possibility to meet the high expectations raised by the promises. 

Follow me for a second on a little journey. Read word by word, loud and slowly: spring :: temperature :: outside :: leaf :: strength :: energy :: coiled. 

Did something happen at the last word? Did your understanding change from spring, the season, to spring, the mechanical helix that stores energy? A simple and at the same time highly complex switch of meaning, depending on if one is a human or a knowledge graph algorithm.

Already in the kindergarten little children learn about homophones (words with exact same pronunciation, but different meaning). This happens often in forms of jokes: >>What do you call a deer with no eyes? – No idea!<< and >>What do you call a deer with no eyes and no legs? – Still no idea!<<. >>No idea? or No eye deer?<< A little later with learning writing in school as humans we learn about homonyms (words with exact same pronunciation and same spelling, but different meaning).

>>bow<< is a classical example of a homonym. Depending on context it can have many multiple meanings, or as one would say in the studies of history of words: The different meanings of >>bow<< represent multiple etymologically separate lexemes.

  • a weapon to shoot arrows
  • a front of a ship
  • a wooden stick with horse hair to play string instruments
  • a tied ribbon
  • bending forward at the waist
  • bending outward at the sides

Some languages are less phonetic than others and have more homophones, to the benefit of stand-up comedians. The effects of homonyms on the learning of children have been studied (see Ref.3) and it is interesting to notice that several studies show positive learning effects even for other languages e.g. “Homophones facilitate lexical development in a second language” (see Ref.4-6).

If you never heard the >>no eye deer<< joke before, your brain delivered an enormous transformative performance by understanding the controversial, unexpected twist. I like jokes that create moments and sometimes seconds of confusion where in the best case I even have to read the jokes once again before the meaning twist kicks in. I (or my brain itself?) congratulate and reward myself for that good transferformational performance by laughing. The transformational process differs from person to person based on culture, field of expertise and prior knowledge.

With understanding the meaning of >>spring<< in the word by word list example from above, again your brain performed something that is hard to achieve with a knowledge graph without some extensions. Depending on your background, job, culture most probably you started with ‘the season’ or the ‘natural water source’  meaning. It is very unlikely that you belong to the very small minority of coil spring manufacturers or mechanical engineers which would have maybe started with the mechanical helix device. The next two words >>temperature<< and >>outside<< are compatible with all three meanings, but in general knowledge texts they are much more often associated with ‘the season’ meaning, followed by the ‘natural water source’ meaning and only very rarely with the ‘helix device’.

What happened at >>leaf<<? With this highly ‘the season’ meaning connected word, most probably your understanding was now hardened towards this meaning. Important at this point is that you might have only heard or seen once in your life about a >>leaf spring<< on old carriages or cars. Most probably you have not even an active memory about it, but if you see a picture, you will recall. For the ‘natural water source’ meaning certainly you can imagine a leaf floating on a source. >>leaf<< is not incompatible with any of the three meanings, even if at this point of time you even did not have the ‘helix device’ as an meaning option actively on your radar. 

In common knowledge texts the next two words >>strength<< and >>energy<< are connected equally to the different meanings. And you can easily build a connection to the energizing or strengthening effect ‘the season’ or ‘the hot spring’ might have on people. Both words support the interpretation you are following at this stage, most probably the ‘season’ meaning.

>>coiled<< now is the game changer. Suddenly there is a conflict, there is an incompatibility with your current meaning interpretation. A transformative rethinking or reordering of information is required. Maybe you have spent a similar moment or second like when understanding a twisted joke. How this happens in our brains is subject to current research. 

This transformational process that you managed easily, is not possible with the knowledge graph tools we have today. 

Let us go back to information technology using knowledge graphs. If the number of concepts for >>spring<< that can be distinguished  is established at the beginning of the learning, a knowledge graph can be used to distinguish between these meanings. But with a non-dynamic non-transactional knowledge graph, the transformative process of extending or reducing the number of concepts is triggered by external additional information or human correction, and is not a transactional learning process. In most cases this can be only resolved by establishing the new context split-up as external input and relearning the entire knowledge graph or at least a significant sub-graph of it. 

Many Graph compute engines are non-transactional and provide read-only graph analytics. They come in node-centric and edge-centric shapes that have different advantages depending on the graph algorithm to perform. They are in contrast to graph databases that are transactional that scale better with graph size, but scale worse for complex graph analytics that need big parts of the graph and not just a limited sub-graph.

gluoNNet Headron is an in-memory, transactional, path-centric, dynamic knowledge graph analytics engine. By forked navigation histories, it allows to explore dynamically the different possible knowledge graph context projections of the ingested information fragments. It uses Human Readable Queries (HRC) to generate actionable insights for decision makers. It saves time and costs by accelerating the data analysis and decision iteration processes between executive decision makers and data analysis specialists. The gluoNNet Headron solution empowers them to accelerate and elevate the transformational process necessary to understand complex data and turn it into actionable insights.  
For more information on our graph analytics and visualisation solutions, please contact Daniel Dobos or info@gluonnet.com.


Ref. 1: Gartner, https://www.gartner.com/en/newsroom/press-releases/2021-03-16-gartner-identifies-top-10-data-and-analytics-technologies-trends-for-2021 

Ref. 2: VerifiedMarketResearch, https://www.verifiedmarketresearch.com/product/graph-analytics-market/ 

Ref. 3: J Speech Lang Hear Res. 2013 Apr; 56(2): 694–707. Published online 2012 Dec 28. doi: 10.1044/1092-4388(2012/12-0122)

Ref. 4: Homophones facilitate lexical development in a second language,  Jiang Liua  and Seth Wiener,https://www.sciencedirect.com/science/article/abs/pii/S0346251X19306670 

Ref. 5: The effect of homonymy on learning correctly articulated versus misarticulated words, Holly L. Storkel, Junko Maekawa, Andrew J. Aschenbrenner, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615102/ 

Ref. 6: The Effect of Semantic Similarity on Learning Ambiguous Words in a Second Language: An Event-Related Potential Study, Yuanyue Zhang, Yao Lu, Lijuan Liang and Baoguo Chen, https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01633/full

Date: Jan 18, 2022
AUTHOR: Daniel Dobos

The Headron Project – Connecting the Dots

Big Data, core, Graph Visualisation, Visual Analytics

Do you remember the connecting-the-dots puzzles from your childhood? Dinosaur or daisy — gradually transpiring from the jumble of dots and numbers as you’re counting upwards, connecting the lines. At gluoNNet, we are working on a software solution for connecting-the-dots puzzles of some sorts, still trying to make sense of a (much bigger) jumble.

Our solution is called ‘Headron’, gluoNNet’s analytical engine. The Headron Analytical Engine will less likely reward its user with a silhouette of an animal or object, but it can help them to see the bigger picture, nevertheless. Its visualisations have the potential to reveal surprising relationships in the data. The data-visualisation method depicts the data (nodes) and sets them into respective relations (connecting lines), based on metadata. The innovative aspect of Headron’s relational approach is its user friendliness. With its simple and intuitive ways of interaction the programme empowers users of all skill levels to make the most of their data.The fully functional beta version of the engine will be launched in early 2022.

The Headron Engine will eventually be integrated into ‘Sunflower’, a first-of-its-kind digital regulation system for the aviation industry gluoNNet co-develops. This innovative system aims to allow better management of aviation-related issues that require regulatory oversight, such as quickly verifying information, speeding up approval procedures, and stopping actions that are illegal or deceptive. Once the Headron Engine has been successfully integrated and sufficiently tested, the gluoNNet developers will work on a more general application.

The Headron visualisation can be applied to various purposes. Financial authorities could track the money flow among publicly registered companies in their purview. In another scenario from the public sector, authorities could benefit from the visualisation method in applying it to legal enforcement, highlighting correlations between committed crimes, eventually helping solve them. Furthermore, the Headron project can support humanitarian endeavours. For instance, it could help analyse and visualise the sanitation situation in third and second world countries, flagging up areas that need more attention from the government or humanitarian agencies.

“The application purposes are vast and manifold. The engine’s design allows running different sets of data from different domains and helps to find connections among those different domains. A universal instrument for data analysis that can be easily implemented into existing systems,” says Richárd Forster, the lead developer of the project.

Of course, the Headron Engine can also analyse scientific data. The underlying structure of the Headron visualisation is inspired by particle-track reconstruction algorithms developed by scientists and engineers at the European Laboratory for Particle Physics (CERN).

For more information on our data analysis and data visualisation solutions, please contact Richard Forster or info@gluonnet.com.

Date: Dec 2, 2021
AUTHOR: Hans Baechle

Unlock your data’s full potential

Aviation, Big Data, Graph Visualisation, Machine Learning, Visual Analytics, Visualisation

Do you think data analysis is extraordinarily complex? Is significant training and experience the only way to understand vast amounts of data? Whilst it was once the case, now with the right software, that is just not true. gluoNNet develops data analysis and data-visualisation solutions that unlock your data’s hidden value. Our sophisticated algorithms do the heavy lifting for you, and then our visualisations provide the clarity to make informed decisions. 

Our solutions provide you with a concise summary of all your data, laying bare all the intricacies and nuances by highlighting all the important relationships, bottlenecks, and other strategic insights. With this ‘x-ray vision’ for data, you can enhance your decision-making process, making your organisation more efficient and effective. 

Our modular UI (user interface), is simple to tailor to your needs, allowing an intuitive and clear overview of your organisation with the flexibility to put any aspect of the data under the microscope. The UI displays this data in the most user-friendly way, whether that is with a diagram, map, cluster/galaxy, gauge, bespoke method, or even a simple table — ensuring you can always see your data clearly. With a few clicks, the user can easily filter from large amounts of data to just show anomalies. Workforce optimisation is easy, with flexible views allowing for individual or role-based customisation focusing on relevant data and also restricting access to sensitive data to only those who require it. Each user can easily drag and rearrange the visualisations in a view, to produce a layout that suits them, saving it for later use and sharing with other users.


All gluoNNet UI solutions can be customised in a simple and dynamic way.

Our algorithms allow real-time or near real-time processing, no matter if the data is coming from scientific, industrial, financial, infrastructure, or other contexts. Our software can combine many input sources, and is compatible with the cloud, on-premise and disconnected networks. It also tracks and highlights any changes of interest, subject to your criteria, by providing custom alerts. This allows you to stop labour-intensive monitoring and relax, knowing the software will highlight important information. If our algorithms identify urgent alerts, you can receive an immediate notification, allowing you to tackle critical issues without delay. Our product allows for automated or user-controlled dossier and report creation to allow specific alerts or custom analysis to be circulated to a wider audience.

We have applied this innovative approach on aviation data, resulting in a first-of-its-kind digital regulation system for the aviation industry. The aim of this innovative system is to allow better management of aviation-related issues that require regulatory oversight, quickly verifying information, speeding up approval procedures, and stopping actions that are illegal or deceptive.  


Click on the images in the gallery to see them in full size.

Another use case we are working on is to better identify and maximise materiality when applied to ESG (Environmental, Social, and Governance) metrics. The user can run diagnostics on current internal processes, and identify gaps or missing areas, articulated and measured within a live dynamic decision-making environment. Regularly ingesting data and processing real-time financial information to continuously update a materiality framework. This allows the user to look at trends over time, discerning what is driving the market, and eventually positioning the company as a global citizen within the ESG future state.

For more information on our data analysis and data visualisation solutions, please contact Michael Denyer or info@gluonnet.com.

Date: Sep 17, 2021
AUTHOR: Hans Baechle