GDB for the Data Driven Age (STI Summit Position Paper) by Orri Erling.
From the post:
The Semantic Technology Institute (STI) is organizing a meeting around the questions of making semantic technology deliver on its promise. We were asked to present a position paper (reproduced below). This is another recap of our position on making graph databasing come of age. While the database technology matters are getting tackled, we are drawing closer to the question of deciding actually what kind of inference will be needed close to the data. My personal wish is to use this summit for clarifying exactly what is needed from the database in order to extract value from the data explosion. We have a good idea of what to do with queries but what is the exact requirement for transformation and alignment of schema and identifiers? What is the actual use case of inference, OWL or other, in this? It is time to get very concrete in terms of applications. We expect a mixed requirement but it is time to look closely at the details.
Interesting post that includes the following observation:
Real-world problems are however harder than just bundling properties, classes, or instances into sets of interchangeable equivalents, which is all we have mentioned thus far. There are differences of modeling (“address as many columns in customer table” vs. “address normalized away under a contact entity”), normalization (“first name” and “last name” as one or more properties; national conventions on person names; tags as comma-separated in a string or as a one-to-many), incomplete data (one customer table has family income bracket, the other does not), diversity in units of measurement (Imperial vs. metric), variability in the definition of units (seven different things all called blood pressure), variability in unit conversions (currency exchange rates), to name a few. What a world!
Yes, quite.
Worth a very close read.