Scientific research

Discussion of how database and related technologies are used to support scientific research. Related subjects include:

October 10, 2014

Notes on predictive modeling, October 10, 2014

As planned, I’m getting more active in predictive modeling. Anyhow …

1. I still believe most of what I said in a July, 2013 predictive modeling catch-all post. However, I haven’t heard as much subsequently about Ayasdi as I had expected to.

2. The most controversial part of that post was probably the claim:

I think the predictive modeling state of the art has become:

  • Cluster in some way.
  • Model separately on each cluster.

In particular:

3. Nutonian is now a client. I just had my first meeting with them this week. To a first approximation, they’re somewhat like KXEN (sophisticated math, non-linear models, ease of modeling, quasi-automagic feature selection), but with differences that start: Read more

September 21, 2014

Data as an asset

We all tend to assume that data is a great and glorious asset. How solid is this assumption?

*”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.

This all raises the idea – if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include:  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

August 6, 2012

Notes, links and comments August 6, 2012

I haven’t done a notes/link/comments post for a while. Time for a little catch-up.

1. MySQL now has a memcached integration story. I haven’t checked the details. The MySQL team is pretty hard to talk with, due to the heavy-handedness of Oracle’s analyst relations.

2. The Large Hadron Collider offers some serious numbers, including:

3. One application area we don’t talk about much for analytic technologies is education. However: Read more

August 6, 2012

People’s facility with statistics — extremely difficult to predict

My recent post on broadening the usefulness of statistics presupposed two things about the statistical sophistication of business intelligence tool users:

Let me now say a little more on the subject. My basic message is — people’s facility with statistics is extremely difficult to predict.

If you DO have to make a point estimate, however, you could do worse than just putting quotation marks around the last four words of that sentence …

Suppose we measure people’s statistical understanding on a 5-point scale:

  1. People who haven’t clue what a p-value is.
  2. People who think a p-value of .05 signifies a 95% chance of truth.
  3. People who know better than that, but who still think that “statistically significant” is pretty close to the same as “true”.
  4. People who know better yet, but aren’t fluent in using statistical techniques correctly.
  5. People who are fluent in statistics.

Just knowing somebody’s job description, can you confidently predict their ranking to within, say, +/- 1 point? I suggest you can’t. People differ wildly in general numeracy and in specific statistical knowledge.

Even our guesses about average knowledge may be off, not least because education is changing things. Read more

July 15, 2012

Memory-centric data management when locality matters

Ron Pressler of Parallel Universe/SpaceBase pinged me about a data grid product he was open sourcing, called Galaxy. The idea is that a distributed RAM grid will allocate data, not randomly or via consistent hashing, but rather via a locality-sensitive approach. Notes include:

The whole thing is discussed in considerable detail in a blog post and a especially in a Hacker News comment thread. There’s also an error-riddled TechCrunch article. Read more

May 21, 2012

Cool analytic stories

There are several reasons it’s hard to confirm great analytic user stories. First, there aren’t as many jaw-dropping use cases as one might think. For as I wrote about performance, new technology tends to make things better, but not radically so. After all, if its applications are …

… all that bloody important, then probably people have already been making do to get it done as best they can, even in an inferior way.

Further, some of the best stories are hard to confirm; even the famed beer/diapers story isn’t really true. Many application areas are hard to nail down due to confidentiality, especially but not only in such “adversarial” domains as anti-terrorism, anti-spam, or anti-fraud.

Even so, I have two questions in my inbox that boil down to “What are the coolest or most significant analytics stories out there?” So let’s round up some of what I know. Read more

May 13, 2012

Notes on the analysis of large graphs

This post is part of a series on managing and analyzing graph data. Posts to date include:

My series on graph data management and analytics got knocked off-stride by our website difficulties. Still, I want to return to one interesting set of issues — analyzing large graphs, specifically ones that don’t fit comfortably into RAM on a single server. By no means do I have the subject figured out. But here are a few notes on the matter.

How big can a graph be? That of course depends on:

*Even if your graph has 10 billion nodes, those can be tokenized in 34 bits, so the main concern is edges. Edges can include weights, timestamps, and so on, but how many specifics do you really need? At some point you can surely rely on a pointer to full detail stored elsewhere.

The biggest graph-size estimates I’ve gotten are from my clients at Yarcdata, a division of Cray. (“Yarc” is “Cray” spelled backwards.) To my surprise, they suggested that graphs about people could have 1000s of edges per node, whether in:

Yarcdata further suggested that bioinformatics use cases could have node counts higher yet, characterizing Bio2RDF as one of the “smaller” ones at 22 billion nodes. In these cases, the nodes/edge average seems lower than in people-analysis graphs, but we’re still talking about 100s of billions of edges.

Recalling that relationship analytics boils down to finding paths and subgraphs, the naive relational approach to such tasks would be: Read more

November 1, 2011

MarkLogic 5, and why you might care

MarkLogic is releasing MarkLogic 5. Key elements of the announcement are:

Also, MarkLogic is early with a feature that most serious DBMS vendors will soon have – support for tiered storage, with writes going first to solid-state storage, then being flushed to disk via a caching-style algorithm.* And as befits a sometime search-engine-substitute, MarkLogic has finally licensed a large set of document filters, from an Australian company called Isys. Apparently, the special virtue of the Isys filters is that they’re good at extracting not only text, but metadata as well.

*If there’s a caching algorithm that doesn’t contain a major element of LRU (Least Recently Used), I don’t recall ever hearing about it.

MarkLogic seems to have settled on a positioning that, although distressingly buzzword-heavy, is at least partly based upon reality. The real part includes:

Based on that reality, MarkLogic talks a lot about Volume, Velocity, Variety, Big Data, unstructured data, semi-structured data, and big data analytics.

Read more

October 14, 2011

Commercial software for academic use

As Jacek Becla explained:

Even so, I think that academic researchers, in the natural and social sciences alike, commonly overlook the wealth of commercial software that could help them in their efforts.

I further think that the commercial software industry could do a better job of exposing its work to academics, where by “expose” I mean:

Reasons to do so include:

Read more

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