josipmrden

Hello jesprenj, I'm the writer of the article.

Yes, thank you for the reference. We're much appreciating comments, both positive and negative on the benchmarks, and are focused on even more workloads, and acknowledged datasets in the future

As for the title itself, I would not comment much on that.

Nothing is stopping you from displaying it like that. However, if you have a huge dataset, you might experience slow performance and being unfeasible to do your analytics job. If you have a small dataset, you can use whatever you want.

Sorry Aaronstotle if it feels that way, I'm actually the writer of this article. I wanted to get an overview to people that can connect dots in their minds using "graphs", but don't think of them when it comes to developing solutions and thinking of how the parts of the attack are connected.

I guess yes but the question is how do you model it and with what tools. And what are the questions you want to have answered with a Markov chains? Not to mention that they can likely explode with the number of data points. I also have no idea what I am talking about.

I have been getting a lot of requests from clients regarding impact analysis. The problem: how can I modify my data a bit, run analytics, and then see the results. Mostly the goal of all that is trial and error until something is done right and a decision can be done at a business level to increase value to the company.

Examples: Chemical industry - should we add another chemical reactor and will that boost our flow through the network, therefore maximizing profit?

Supply chain - if we do a few changes, will we achieve maximized production and not have excess reserves in our storage

The problem becomes more tricky the greater the risk is to the business if a wrong decision/investment has been made to resources that don't pay off.

From past experiences with our clients that optimize supply chain pipelines and try to maximize production, I have come up with a blog post which analyzes what kind of meaningful information you can discover by having the underlying representation of the supply chain as a network. The leading idea would be minimizing staff's time-consuming process of coming up with a feasible schedule and instead performing things which they are expert's in their domain. Please let me hear your thoughts if you found it interesting, or have any remarks about it!

If you look the article as a single piece of content, it may look like a clickbait. If combined into a Solutions page where several blog posts are outlined about how to solve particular problems, it actually makes sense.

Yes, the term itself is a bit confusing. However, it's an official term in the industry :')

I have a feeling like people like to render large graphs just for visualizations. I yet didn't encounter a use case where someone would look at a graph of a million visualized nodes and said: "Oh I see, this is what's happening!" or some other information. But then again, you don't do that at all when dealing with relational databases as well

But I guess the biggest advantage lies in shallow hop visualizations (up to 10), where there is a smaller number of nodes, let's say a 100, but you can clearly understand what's happening because you can process all of the connections at first glance.

And even in the real life use cases, such as fraud detection, it's never going to be a million person organization that steals you money and resources, but a group of people closely tied together, which you can quickly inspect and look at, whereas looking at tabular data does not connect the dots.

You can draw a parallel when detectives marked the maps with needles and strings to connect the murder cases. Of course they didn't put a million needles on the wall, but a 100 of them was sufficient for inference.

Seems cool, but what's your reasoning?