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                      Analysis of data management technology optimized for text data. Related subjects include:

                      June 27, 2019

                      How to beat “fake news”

                      Most observers hold several or all of the views:

                      And further:

                      But despite all those difficulties, I also believe that a good solution to news/opinion filtering is feasible; it just can’t be as simple as everybody would like.

                      Read more

                      June 20, 2018

                      Brittleness, Murphy’s Law, and single-impetus failures

                      In my initial post on brittleness I suggested that a typical process is:

                      In many engineering scenarios, a fuller description could be:

                      So it’s necesseary to understand what is or isn’t likely to go wrong. Unfortunately, that need isn’t always met.? Read more

                      November 23, 2016

                      MongoDB 3.4 and “multimodel” query

                      “Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. My clients at MongoDB of course had to join the train as well, but they’ve taken a clear and interesting stance:

                      When I pointed out that it would make sense to call this “multimodel query” — because the storage isn’t “multimodel” at all — they quickly agreed.

                      To be clear: While there are multiple ways to read data in MongoDB, there’s still only one way to write it. Letting that sink in helps clear up confusion as to what about MongoDB is or isn’t “multimodel”. To spell that out a bit further: Read more

                      October 21, 2016

                      Rapid analytics

                      “Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.

                      1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:

                      2. In early 2011, I coined the phrase investigative analytics, about which I said three main things: Read more

                      October 3, 2016

                      Notes on the transition to the cloud

                      1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:

                      Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.

                      This is a good example of Monash’s Laws of Commercial Semantics.

                      2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.

                      This fact now seems to be widely understood.

                      Read more

                      December 1, 2015

                      What is AI, and who has it?

                      This is part of a four post series spanning two blogs.

                      1. “Artificial intelligence” is a term that usually means one or more of:

                      But that covers a lot of ground, especially since reasonable people might disagree as to what constitutes “smart”.

                      2. Examples of what has been called “AI” include:

                      Read more

                      October 26, 2015

                      Sources of differentiation

                      Obviously, a large fraction of what I write about involves technical differentiation. So let’s try for a framework where differentiation claims can be placed in context. This post will get through the generalities. The sequels will apply them to specific cases.

                      Many buying and design considerations for IT fall into six interrelated areas:? Read more

                      September 14, 2015

                      DataStax and Cassandra update

                      MongoDB isn’t the only company I reached out to recently for an update. Another is DataStax. I chatted mainly with Patrick McFadin, somebody with whom I’ve had strong consulting relationships at a user and vendor both. But Rachel Pedreschi contributed the marvelous phrase “twinkling dashboard”.

                      It seems fair to say that in most cases:

                      Those generalities, in my opinion, make good technical sense. Even so, there are some edge cases or counterexamples, such as:

                      *And so a gas company is doing lightweight analysis on boiler temperatures, which it regards as hot data. ??

                      While most of the specifics are different, I’d say similar things about MongoDB, Cassandra, or any other NoSQL DBMS that comes to mind: Read more

                      September 10, 2015

                      MongoDB update

                      One pleasure in talking with my clients at MongoDB is that few things are NDA. So let’s start with some numbers:

                      Also >530 staff, and I think that number is a little out of date.

                      MongoDB lacks many capabilities RDBMS users take for granted. MongoDB 3.2, which I gather is slated for early November, narrows that gap, but only by a little. Features include:

                      There’s also a closed-source database introspection tool coming, currently codenamed MongoDB Scout.? Read more

                      May 26, 2015

                      IT-centric notes on the future of health care

                      It’s difficult to project the rate of IT change in health care, because:

                      Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:

                      These vastly greater amounts of data cited above will allow for greatly changed analytics.
                      Read more

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