February 1, 2014

More on public policy

Occasionally I take my public policy experience out for some exercise. Last week I wrote about privacy and network neutrality. In this post I’ll survey a few more subjects.

1. Censorship worries me, a lot. A classic example is Vietnam, which basically has outlawed online political discussion.

And such laws can have teeth. It’s hard to conceal your internet usage from an inquisitive government.

2. Software and software related patents are back in the news. Google, which said it was paying $5.5 billion or so for a bunch of Motorola patents, turns out to really have paid $7 billion or more. Twitter and IBM did a patent deal as well. Big numbers, and good for certain shareholders. But this all benefits the wider world — how?

As I wrote 3 1/2 years ago:

The purpose of legal intellectual property protections, simply put, is to help make it a good decision to create something.

Why does “securing … exclusive Right[s]” to the creators of things that are patented, copyrighted, or trademarked help make it a good decision for them to create stuff? Because it averts competition from copiers, thus making the creator a monopolist in what s/he has created, allowing her to at least somewhat value-price her creation.

I.e., the core point of intellectual property rights is to prevent copying-based competition. By way of contrast, any other kind of intellectual property “right” should be viewed with great suspicion.

That Constitutionally-based principle makes as much sense to me now as it did then. By way of contrast, “Let’s give more intellectual property rights to big corporations to protect middle-managers’ jobs” is — well, it’s an argument I view with great suspicion.

But I find it extremely hard to think of a technology industry example in which development was stimulated by the possibility of patent protection. Yes, the situation may be different in pharmaceuticals, or for gadgeteering home inventors, but I can think of no case in which technology has been better, or faster to come to market, because of the possibility of a patent-law monopoly. So if software and business-method patents were abolished entirely – even the ones that I think could be realistically adjudicatedI’d be pleased.

3. In November, 2008 I offered IT policy suggestions for the incoming Obama Administration, especially:  Read more

January 9, 2014

The games of Watson

IBM excels at game technology, most famously in Deep Blue (chess) and Watson (Jeopardy!). But except at the chip level — PowerPC — IBM hasn’t accomplished much at game/real world crossover. And so I suspect the Watson hype is far overblown.

I believe that for two main reasons. First, whenever IBM talks about big initiatives like Watson, it winds up bundling a bunch of dissimilar things together and claiming they’re a seamless whole. Second, some core Watson claims are eerily similar to artificial intelligence (AI) over-hype three or more decades past. For example, the leukemia treatment advisor that is being hopefully built in Watson now sounds a lot like MYCIN from the early 1970s, and the idea of collecting a lot of tidbits of information sounds a lot like the Cyc project. And by the way:

Read more

January 3, 2014

Notes on memory-centric data management

I first wrote about in-memory data management a decade ago. But I long declined to use that term — because there’s almost always a persistence story outside of RAM — and coined “memory-centric” as an alternative. Then I relented 1 1/2 years ago, and defined in-memory DBMS as

DBMS designed under the assumption that substantially all database operations will be performed in RAM (Random Access Memory)

By way of contrast:

Hybrid memory-centric DBMS is our term for a DBMS that has two modes:

  • In-memory.
  • Querying and updating (or loading into) persistent storage.

These definitions, while a bit rough, seem to fit most cases. One awkward exception is Aerospike, which assumes semiconductor memory, but is happy to persist onto flash (just not spinning disk). Another is Kognitio, which is definitely lying when it claims its product was in-memory all along, but may or may not have redesigned its technology over the decades to have become more purely in-memory. (But if they have, what happened to all the previous disk-based users??)

Two other sources of confusion are:

With all that said, here’s a little update on in-memory data management and related subjects.

And finally,

December 8, 2013

DataStax/Cassandra update

Cassandra’s reputation in many quarters is:

This has led competitors to use, and get away with, sales claims along the lines of “Well, if you really need geo-distribution and can’t wait for us to catch up — which we soon will! — you should use Cassandra. But otherwise, there are better choices.”

My friends at DataStax, naturally, don’t think that’s quite fair. And so I invited them — specifically Billy Bosworth and Patrick McFadin — to educate me. Here are some highlights of that exercise.

DataStax and Cassandra have some very impressive accounts, which don’t necessarily revolve around geo-distribution. Netflix, probably the flagship Cassandra user — since Cassandra inventor Facebook adopted HBase instead — actually hasn’t been using the geo-distribution feature. Confidential accounts include:

DataStax and Cassandra won’t necessarily win customer-brag wars versus MongoDB, Couchbase, or even HBase, but at least they’re strongly in the competition.

DataStax claims that simplicity is now a strength. There are two main parts to that surprising assertion. Read more

December 5, 2013

Vertica 7

It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:

Other Vertica 7 enhancements include:

Overall, two recurring themes in our discussion were:

Read more

November 29, 2013

SaaS appliances, SaaS data centers, and customer-premises SaaS

Conclusions

I think that most sufficiently large enterprise SaaS vendors should offer an appliance option, as an alternative to the core multi-tenant service. In particular:

How I reached them

Core reasons for selling or using SaaS (Software as a Service) as opposed to licensed software start:

Conceptually, then, customer-premises SaaS is not impossible, even though one of the standard Big Three SaaS benefits is lost. Indeed:

But from an enterprise standpoint, that’s all (relatively) simple stuff. So we’re left with a more challenging question — does customer-premises SaaS make sense in the case of enterprise applications or other server software?

Read more

November 24, 2013

Thoughts on SaaS

Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:

For smaller enterprises, the core outsourcing argument is compelling. How small? Well:

So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*

Read more

November 19, 2013

How Revolution Analytics parallelizes R

I talked tonight with Lee Edlefsen, Chief Scientist of Revolution Analytics, and now think I understand Revolution’s parallel R much better than I did before.

There are four primary ways that people try to parallelize predictive modeling:

One confusing aspect of this discussion is that it could reference several heavily-overlapping but not identical categories of algorithms, including:

  1. External memory algorithms, which operates on datasets too big to fit in main memory, by — for starters — reading in and working on a part of the data at a time. Lee observes that these are almost always parallelizable.
  2. What Revolution markets as External Memory Algorithms, which are those external memory algorithms it has gotten around to implementing so far. These are all parallelized. They are also all in the category of …
  3. … algorithms that can be parallelized by:
    • Operating on data in parts.
    • Getting intermediate results.
    • Combining them in some way for a final result.
  4. Algorithms of the previous category, where the way of combining them specifically is in the form of summation, such as those discussed in the famous paper Map-Reduce for Machine Learning on Multicore. Not all of Revolution’s current parallel algorithms fall into this group.

To be clear, all Revolution’s parallel algorithms are in Category #2 by definition and Category #3 in practice. However, they aren’t all in Category #4.

Read more

November 10, 2013

RDBMS and their bundle-mates

Relational DBMS used to be fairly straightforward product suites, which boiled down to:

Now, however, most RDBMS are sold as part of something bigger.

Read more

November 8, 2013

Comments on the 2013 Gartner Magic Quadrant for Operational Database Management Systems

The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:

Anyhow:  Read more

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