I think we have an innate fear of losing things forever and that's why we sometimes let information/advice overload inundate us - we're afraid that if we don't read that blog post, that we're going to miss out on a life hack and never see it again.
I don't really have a set way I cut out the noise - but I definitely try to in as many ways as I can. It helps me to look at it as a fear of loss/fear of missing things and when I make note of that fear - I can ignore the noise more easily.
It's been a while since I looked into this myself, but I used to do a lot of GIS work and have a lot of experience with the tools for doing this kind of analysis. Doing a quick search around for a general purpose algorithm for the "traveling salesman problem" - I came across this: http://www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa03/cdrom/pap...
In my experience, the most important part of doing network analysis is to have a "topology model" for your road network data. It contains the information necessary to model turns, drive-time, traffic signals, etc.
Anyway, I used to know the 1000 ft view of the math behind this stuff but not anymore. If, however, you're trying to build it into an application - there are solutions like pgRouting https://github.com/pgRouting/pgrouting which utilizes PostGIS (PostgreSQL), ESRI's Network Analyst ($$), and Google Maps Directions API. These all leverage some underlying topology data like Open Street Map to do routing and figure out the subsequent times/schedule to get from point to point to point...
Something that stood out to me was their discussion about "Big Data technologies" (Hadoop, HBase...) and how they "support" data mining techniques. So sometimes I wonder if big data technologies are more for the processing of the data, like the ETL needed for data warehouses - but since it's big, we need those special technologies to process it (and do additional data-sciencey things on it - like calculate a probability of churn, probability of loan default, etc). End results can still be those high-performance in-memory objects that we can slice and dice, just like our data warehouses, if that's how we need to see them.
This is all coming from no practical experience with Hadoop / Big Data, just research so hopefully someone clarifies :)
I haven't actually built a cube myself, but I support a few right now. My boss, who built ours, always refers me to Ralph Kimball's "The Microsoft Data Warehouse Toolkit" (you mentioned Oracle, so maybe there are synonymous toolsets out there)
I know you said you're aware of Ralph Kimball, but the first ~100 pages are broken into 1) Defining the Business Requirements and 2) Designing the Business Process Dimensional Model. It's really helped me wrap my head around the original design of the Facts and Dimensions as they relate to the business.
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