December 12, 2015

Abstract datatypes and extensible RDBMS

In my recent Stonebraker-oriented post about database theory and practice over the decades, I wrote

I used to overrate the importance of abstract datatypes, in large part due to Mike’s influence. I got over it. He should too. They’re useful, to the point of being a checklist item, but not a game-changer. A big part of the problem is [that] different parts of a versatile DBMS would prefer to do different things with memory.

and then left an IOU for a survey of abstract datatypes/RDBMS extensibility. Let’s get to it.

Perhaps the most popular term was actually object/relational DBMS, but I’ve never understood the etymolygy on that one.

Although I call RDBMS extensibility a “checklist item”, the list of products that can check it off is actually pretty short.

Surely there are more, but at the moment I can’t really think of which they are.

Read more

December 3, 2015

AI memories — expert systems

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

As I mentioned in my quick AI history overview, I was pretty involved with AI vendors in the 1980s. Here on some notes on what was going on then, specifically in what seemed to be the hottest area at the time — expert systems. Summing up:

First, some basics.  Read more

December 1, 2015

Historical notes on artificial intelligence

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

0. The concept of artificial intelligence has been around almost as long as computers — or even before, if you recall that robots were imagined by the 1920s. But for a while it was mainly academic and perhaps military/natural security research. There’s been a robotics industry for over 50 years. But otherwise, when I first became an analyst in 1981, AI commercialization efforts were rather new, and were concentrated in three main areas:

1. If I’ve ever gotten too close to a group of companies, it was probably the 1980s AI vendors. I unfortunately earned investment banking fees by encouraging people into money-losing investments in all three areas cited above, in Teknowledge, Artificial Intelligence Corporation and Symbolics respectively. I dated women who worked for Symbolics and Teknowledge. I wrote and performed a satirical song about Inference at an employee party for Intellicorp. Accordingly, when I write about individual companies in the sector, I fear that I may go on at self-indulgent length. So I’ll save all that for another time, and content myself now with a brief and dry survey that does little more than establish some context.

2. The 1980s also saw military-funded research into autonomous vehicles, as well as continued efforts in robotics and machine vision. Frankly, there wasn’t a lot of commercial overlap between these areas and the rest of AI at that time, and the rest of AI is what I tracked more closely.

But in one counterexample, a machine vision company named Machine Intelligence spun off a company that was building a PC DBMS with some natural language query capability. The spin-off company was Symantec. (Obviously, Symantec his pivoted multiple times since.) Machine Intelligence cofounder Earl Sacerdoti also wound up at expert system vendor Teknowledge for a while. So maybe there was more overlap in theory than there was in commercial practice.  Read more

November 11, 2015

Notes on the technology supporting packaged application software

This is part of a three-post series on enterprise application software over the decades, meant to serve as background to a DBMS2 post on issues in enterprise apps.

0. I’d like to discuss the technology underneath packaged application software. To create some hope of the discussion being coherent, let’s split apps into a few categories:

1. The idea of bundling ERP (or its predecessor MRP) with an underlying DBMS has been around for a long time.

And for smaller enterprises, it has been the norm, not the exception.

Read more

November 11, 2015

Enterprise application software — vertical and departmental markets

This is part of a three-post series on enterprise application software over the decades, meant to serve as background to a DBMS2 post on issues in enterprise apps.

1. When I started as an analyst in 1981, manufacturers seemed to still be over 40% of the IT market. For them, the distinction between “cross-industry” and “vertical market” application software wasn’t necessarily clear. Indeed, ERP (Enterprise Resource Planning) can be said to have grown out of the combination of MRP and accounting software, although it never was a manufacturing-specific industry category. ERP also quickly co-opted what was briefly its own separate category, namely SCM (Supply Chain Management) software.

2. Manufacturing aside, other important early vertical markets were banking, insurance and health care. It is no coincidence that these are highly regulated industries; regulations often gave a lot of clarity as to how software should or shouldn’t work. Indeed, the original application software package category was probably general ledger, and the original general ledger packages were probably for banks rather than cross-industry.

Read more

November 11, 2015

Enterprise application software — generalities

This is part of a three-post series on enterprise application software over the decades, meant to serve as background to a DBMS2 post on issues in enterprise apps.

1. There can actually be significant disagreement as to what is or isn’t an enterprise application. I tend to favor definitions that restrict the category to (usually) server software, which manages transactions, customer interactions, financial records and things like that. Some other definitions are even more expansive, including personal productivity software such as Microsoft Office, computer-aided engineering systems and the like.

2.  Historically, application software has existed mainly to record and route information, commonly from people to machines and back. Indeed, one could say that applications are characterized by (up to) five (overlapping) aspects, which may be abbreviated as:

The first four of those five items fit into my “record and route information” framework.

Read more

August 7, 2015

Application databases

In my recent post on data messes, I left an IOU for a discussion of application databases. I’ve addressed parts of that subject before, including in a 2013 post on data model churn and a 2012 post on enterprise application history, both of which cite examples mentioned below. Still, there’s a lot more that could be said, because the essence of an operational application is commonly its database design. So let’s revisit some history.

In many cases, installing an application allows enterprises to collect the underlying data, electronically, for the first time ever. In other cases the app organizes data that was already there in some previous form. Either way, applications tend to greatly change the way data is managed and stored.

Read more

September 22, 2014

Larry Ellison memories

Larry Ellison had an official job change, and will be CTO and Executive Chairman of Oracle — with the major product groups reporting to him — instead of CEO. I first met Larry 31 years ago, and hung out with him quite a bit at times. So this feels like time for a retrospective.

For starters, let me say:

Some anecdotes: Read more

July 11, 2014

20th Century DBMS success and failure

As part of my series on the keys to and likelihood of success, I’d like to consider some historical examples in various categories of data management.

A number of independent mainframe-based pre-relational DBMS vendors “crossed the chasm”, but none achieved anything resembling market dominance; that was reserved for IBM. Success when they competed against each other seemed to depend mainly on product merits and the skills of individual sales people or regional sales managers.

IBM killed that business by introducing DB2, a good product with very good strategic marketing from a still-dominant vendor. By “very good strategic marketing” I mean that IBM both truly invented and successfully market-defined the relational DBMS concept, including such conceptual compromises as:

In the minicomputer world, however, hardware vendors lacked such power, and independent DBMS vendors thrived. Indeed, Oracle and Ingres rode to success on the back of Digital Equipment Corporation (DEC) and other minicomputer vendors, including the payments they got to port their products to various platforms.* The big competitive battle was Oracle vs. Ingres, about which I can say for starters: Read more

March 24, 2014

IDG and me

I never met IDG founder Pat McGovern, who was the kind of tycoon that traveled around the world handing Christmas bonuses personally to every employee in his firm. Even so, McGovern’s passing seems like an occasion for recollections about IDG through the decades. And so:

1. My connections have always been much stronger with IDG (International Data Group) publications than with the analyst firm IDC that’s also part of the business.

2. I have at times been pretty connected to those pubs. For example:

3. Computerworld has probably always been the leading enterprise technology publication, including during the trade press’ glory years. Most memorably, pre-relational mainframe DBMS were claiming with some success to be “relational”. But when Computerworld reported Ted Codd’s “rules” for RDBMS, that was that — RDBMS were defined to be what Codd and Computerworld said they were, and the bottom dropped out of the market for DBMS that didn’t meet Codd’s criteria.

4. In line with its industry leadership, Computerworld had a classified ad section that ran dozens of pages. When I hired a research assistant in my stock analyst days, the obvious choice was to run the ad there.

5. To this day, if an ego-surf shows that I’ve been quoted in countries and languages around the world — Brazil, Australia, Iran or whatever — it’s usually something I said to IDG, which then translated and republished it around the world.

6. IDG is a big enough press organization not to be perfect. Read more

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