You have a choice of titles:
How to Use Graph Databases to Analyze Relationships, Risks and Business Opportunities (YouTube)
Graph Databases, Triple Stores and their uses… (slides of Jans Aasman at Enterprise Data World 2012)
From the description:
Graph databases are one of the new technologies encouraging a rapid re-thinking of the analytics landscape. By tracking relationships – in a network of people, organizations, events and data – and applying reasoning (inference) to the data and connections, powerful new answers and insights are enabled.
This presentation will explain how graph databases work, and how graphs can be used for a number of important functions, including risk management, relationship analysis and the identification of new business opportunities. It will use a case study in the manufacturing sector to demonstrate how complex relationships can be discovered and integrated into analytical systems. For example, what are the repercussions for the supply chain of a major flood in China? Which products are affected by political unrest in Thailand? Has a sub-subcontractor started selling to our competition and what does that mean for us? What happened historically to the price of an important sub-component when the prices for crude oil or any other raw material went up? Lots of answers can be provided by graph (network) analysis that cannot be answered any other way, so it is crucial that business and BI executives learn how to use this important new tool.
At time marks 18:30 to 19:09, major customers who are interested in graph databases.
An impressive list of potential customers.
If you wanted to find comments about this presentation you could search for:
How to Use Graph Databases to Analyze Relationships, Risks and Business Opportunities (YouTube) (9,530 “hits”)
Graph Databases, Triple Stores and their uses… (slides of Jans Aasman at Enterprise Data World 2012) (7 “hits”)
If you pick the wrong title as your search string, you will miss 9,523 mentions of this video on the WWW.
The same danger comes up when you rely on normalized data, the sort of data you saw in this video.
If the data you are searching has missed data that needs to be normalized, well, you just don’t find the data.
With a topic map based system, normalization isn’t necessary so long as there is mapping in the topic map.
Think of it this way, you can normalize data over and over again, making it unusable by its source, or you can create a mapping rule into a topic map once.
And the data remains findable by its original creator or source.
I would say yes, topic maps are graphs++, they don’t require normalization.
To return to an earlier subject of marketing topic maps: Maybe another lesson to take from this comparison of titles is that the interest in solutions to problems is more than 1,000 times greater than the interest in the technology behind the solution. 🙂
Comment by clemp — November 28, 2013 @ 9:35 am
Carl, please say it’s not so! That users are more interested in their small problems than the elegance of a finely crafted solution! 😉
Point taken.
Unshared monomania is never a good starting point for a sale (or adoption).
Comment by Patrick Durusau — November 30, 2013 @ 10:12 am