i used to predict Datalog would win out eventually but sadly was wrong.
SQLite callbacks let you implement anything you please inside the scope of transactions, triggers, other events etc. To make an app run this way all you need is a starting event. You could implement an OS this way, heheh.
> The stuff I've read that touches on this idea is almost all from 2006 and onwards, mainly 2010s. The earliest example is a bit of an outlier: Douglas Hubbard's 1985 How to Measure Anything -- but it's also only tangentially related.
I didn’t keep good track of such things but a lot of my early reading in the 70’s was in operations research and decision support systems, mostly sort of what we call operational analytics these days with a big helping of statistical process control too. World War 2 logistics practices and ‘50s and ‘60s “scientific management” fads generated a lot of material, some insightful. Many medium-sized businesses could afford significant R&D then, so you’ll find e.g. furniture factories developing their own computer systems from PCBs to custom ASIC components, just to manage statistical process control and decision support systems.
> That said, I did mention it was obvious to me. I still find it hard to convince management and colleagues of its importance...
I think the reason I keep having to justify this every few years is the tendency towards abstractions in management which try to simplify things into “anecdotal analytics”, e.g. preferring a persuasive narrative over reality...for a good cynical perspective from the ‘50s I recommend C.M. Kornbluth’s “The Marching Morons” (<https://en.wikipedia.org/wiki/The_Marching_Morons>).
> It's a common misconception that risk management and risk reduction are synonyms. Risk management is about finding the right level of risk given external factors. Sometimes that means maintaining the current level of risk or even increasing it in favour of other properties.
What’s funny is I seem to have to explain this to senior management anew every 6-7 years. I know they teach it in management school, but it in the real world somehow people fall into the false equivalence when they get promoted. Often they adopt a cartoonish view of things because they can’t get the quantitative signals and everything decision effectively reduces to what I ironically term as anecdotal analytics.
I have this amusing heuristic for risk acceptance which I often use to help people approach decisions: you should kick the decision up to someone with higher authority if your signing authority is less than: risk coefficient times quantified exposure, less mitigation cost where mitigation is within signing authority AND/OR budgeted and authorized spend. I like to view mitigation and opportunity cost/benefit in a similar way so I have some idea of equivalences when evaluating tradeoffs.
It’s not original with me, I must have lifted it from some decades-past HBR article or 60’s rant on quantitative business management.
I could rant on various aspects of risk management application all day, thank goodness I've managed to quit before I really got started sharing...in my experience it’s been very helpful when applied in real world engineering implementations.
> History tidbit: Jim Clark, the founder of SGI, invented the GPU which was what made SGI machines so fast at 3D.
Even more trivial: When I moved to Silicon Valley I bought a used Porsche on a whim (my recruiter implied it was a proper consultant accessory in SV) and fixed it up a bit until it became my daily driver. When I researched the title I discovered Jim Clark was the original owner. So I’d like to credit Jim Clark for starting me on the road to speed up my commute.
> It's REALLY impressive that you only need to read 54 KB out of 700 MB, to fetch the records.
the harsh reality is that doing sensible queries that only reference and return the data actually needed always makes things faster. Even with server DBMS. Oh, how many times have I lamented the naive "select *" for forcing all the row contents even when there was index coverage for the actually needed data.