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.
- PostgreSQL has the granddaddy implementation.
- Its ideas were commercialized as Illustra, which was bought by Informix, which later was bought by IBM.
- Oracle has one of the major implementations.
- IBM has one of the major implementations.
- Sybase has struggled with implementing the technology.
- So did Microsoft SQL Server, which of course started with the Sybase code line.
Surely there are more, but at the moment I can’t really think of which they are.
| Categories: Database management systems, IBM, Informix, Ingres, Microsoft, Oracle, Sybase | 22 Comments |
AI memories — expert systems
This is part of a four post series spanning two blogs.
- One post gives a general historical overview of the artificial intelligence business.
- One post (this one) specifically covers the history of expert systems.
- One post gives a general present-day overview of the artificial intelligence business.
- One post explores the close connection between machine learning and (the rest of) AI.
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:
- The expert systems business never grew to be very large, but it garnered undue attention (including from me). In particular, the companies offering the technology didn’t prosper much.
- What commercial investment there was in expert system projects, successful or otherwise, foreshadowed some of what would be tried using other analytic technologies. Application areas included, among others, credit granting, financial trading, airline flight pricing and equipment maintenance.
- Technological reasons the industry failed included:
- The difficulties of debugging and maintaining a collection of rules.
- Lack of ability to crunch data, or to benefit from data crunching. (This is surely why few expert systems use cases were in the marketing area.)
- A paradigm that assumed the required rules pre-existed inside expert humans’ heads.
- There were some successful projects even so.
First, some basics. Read more
| Categories: Artificial intelligence, Fun stuff | 8 Comments |
Historical notes on artificial intelligence
This is part of a three post series spanning two blogs.
- One post (this one) gives a general historical overview of the artificial intelligence business.
- One post specifically covers the history of expert systems.
- One post gives a general present-day overview of the artificial intelligence business.
- One post explores the close connection between machine learning and (the rest of) AI.
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:
- Expert systems.
- Natural language query.
- General AI underpinnings (especially LISP machines).
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
| Categories: Artificial intelligence | 5 Comments |
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.
- The first lays out very general issues in understanding and subdividing this multi-faceted sector.
- The second calls out characteristics of specific application areas.
- The third (this one) discusses application software products’ underlying technology.
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:
- Major/core suite, large enterprises — e.g. ERP (Enterprise Resource Planning).
- Major/core suite, smaller enterprises — e.g., the province of Progress and Intersystems VARs (Value-Added Resellers).
- Remarkably distributed applications. This is where a lot of the more unusual technology choices cluster.
- Other point solutions. Sometimes, a guy just needs a catch-all category. 🙂
1. The idea of bundling ERP (or its predecessor MRP) with an underlying DBMS has been around for a long time.
- Cullinet and Cincom tried it, but with pre-relational DBMS. Oops.
- Oracle has always had that strategy.
- A sizable minority of SAP’s customers ran
And for smaller enterprises, it has been the norm, not the exception.
| Categories: Application software, Cullinet, Database management systems, IBM, Informix, Microsoft, Oracle, Pre-relational era, SAP, Sybase | 6 Comments |
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.
- The first lays out very general issues in understanding and subdividing this multi-faceted sector.
- The second (this one) calls out characteristics of specific application areas.
- The third discusses application software products’ underlying technology.
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.
| Categories: Application software, Pre-relational era | 8 Comments |
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.
- The first (this one) lays out very general issues in understanding and subdividing this multi-faceted sector.
- The second calls out characteristics of specific application areas.
- The third discusses application software products’ underlying technology.
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:
- Database design.
- Workflow/business process.
- User interface.
- Social/collaboration.
- Analytics.
The first four of those five items fit into my “record and route information” framework.
| Categories: Application software, Microsoft, Oracle, SAP | 6 Comments |
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.
| Categories: Application software, SAP | 5 Comments |
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:
- I met Larry Ellison the same year I learned of him, which was 1983. We were in fairly active touch until the late 1990s. Then we drifted apart. That period corresponds roughly to the eras I characterized in my Oracle history overview as Hypergrowth, Plateau, and Professionalism.
- With Larry as with other “larger than life” industry figures I’ve met, what you get in private and what you see in public are pretty similar. I’ve had high-intensity dinner conversations with Larry (numerous times), Bill Gates (a few times) and Ross Perot (once) that are quite in line with their public demeanors.
- With Larry, facts can be mutable things. The first time I met him, I came away with the impression he had a PhD. The second time, it was only a masters degree. Ten years later, he’d almost graduated from the University of Chicago, but had failed or not take a French exam. And I gather his educational resume has retreated a little further since.
- Larry is hilarious, in a scathing way, and an excellent story-teller. Unfortunately, his humor rarely translates well to out-of-context print.
Some anecdotes: Read more
| Categories: Database management systems, Oracle | 6 Comments |
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:
- Ted Codd’s 12 rules, not that anybody — even IBM — actually followed them all.
- SQL as the standard, rather than the probably superior QUEL.
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
| Categories: Database management systems, IBM, Informix, Ingres, Oracle, Sybase, Teradata | 9 Comments |
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:
- I’ve been a columnist for both Computerworld and Network World (the latter online-only).
- I’ve blogged for pay for both Computerworld and Network World.
- I’ve been outright interviewed by each, and quoted many times by them and other IDG publications as well.
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
| Categories: Database management systems | Leave a Comment |