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        Data types

        Analysis of data management technology optimized for specific datatypes, such as text, geospatial, object, RDF, or XML. 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 25, 2018

        New legal limits on surveillance in the US

        The United States has new legal limits on electronic surveillance, both in one specific way and — more important — in prevailing judicial theory. This falls far short of the protections we ultimately need, but it’s a welcome development even so.

        The recent Supreme Court case Carpenter v. United States is a big deal. Let me start by saying:

        *The Katz test basically says that that an individual’s right to privacy is whatever society regards as a reasonable expectation of privacy at that time.

        **The third-party doctrine basically says that any information of yours given voluntarily to a third party isn’t private. This includes transactional information such as purchases or telephone call detail records (CDRs)

        Key specifics include: 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

        March 12, 2017

        Introduction to SequoiaDB and SequoiaCM

        For starters, let me say:

        Also:

        Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.

        Read more

        December 18, 2016

        Introduction to Crate.io and CrateDB

        Crate.io and CrateDB basics include:

        In essence, CrateDB is an open source and less mature alternative to MemSQL. The opportunity for MemSQL and CrateDB alike exists in part because analytic RDBMS vendors didn’t close it off.

        CrateDB’s not-just-relational story starts:

        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

        May 30, 2016

        Adversarial analytics and other topics

        Five years ago, in a taxonomy of analytic business benefits, I wrote:

        A large fraction of all analytic efforts ultimately serve one or more of three purposes:

        • Marketing
        • Problem and anomaly detection and diagnosis
        • Planning and optimization

        That continues to be true today. Now let’s add a bit of spin.

        1. A large fraction of analytics is adversarial. In particular: Read more

        December 10, 2015

        Readings in Database Systems

        Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:

        I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.

        But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**

        *When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.

        **Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.

        Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as:? Read more

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