Discussion of how database and related technologies are used to support scientific research. Related subjects include:
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:
- MYCIN led to E-MYCIN, which led to the company Teknowledge, which raised a lot of money* but now has almost faded from memory.
- Cyc is connected to the computer science community’s standard unit of bogosity.
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:
- 1 petabyte/second.
- 6 x 109 collisions/second.
- Only 1 in 1013 collision records kept (which I guess knocks things down to a 100 byte/second average, from the standpoint of persistent storage).
- Real-time filtering by a cluster of several thousand machines, over a 25 nanosecond period.
3. One application area we don’t talk about much for analytic technologies is education. However: Read more
|Categories: Cache, memcached, Memory-centric data management, MySQL, Open source, Petabyte-scale data management, RDF and graphs, Scientific research||Leave a Comment|
My recent post on broadening the usefulness of statistics presupposed two things about the statistical sophistication of business intelligence tool users:
- It varies a lot.
- In many cases, it isn’t be very high.
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:
- People who haven’t clue what a p-value is.
- People who think a p-value of .05 signifies a 95% chance of truth.
- People who know better than that, but who still think that “statistically significant” is pretty close to the same as “true”.
- People who know better yet, but aren’t fluent in using statistical techniques correctly.
- 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
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 original technology was developed to track moving objects on behalf of the Israeli Air Force.
- The commercial product is focused on MMO (Massively MultiPlayer Online) games (or virtual worlds).
- The underpinnings are being open sourced.
- Ron suggests that, among other use cases, Galaxy might work well for graphs.
- Ron argues that one benefit is that when lots of things cluster together — e.g. characters in a game — there’s a natural way to split them elastically (shrink the radius for proximity).
- The design philosophy seems to be to adapt as many ideas as possible from the way CPUs manage (multiple levels of) RAM cache.
|Categories: Cache, Clustering, Complex event processing (CEP), Games and virtual worlds, GIS and geospatial, Open source, RDF and graphs, Scientific research||2 Comments|
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
|Categories: Analytic technologies, Google, Health care, Investment research and trading, Predictive modeling and advanced analytics, Scientific research, Telecommunications, Web analytics||6 Comments|
This post is part of a series on managing and analyzing graph data. Posts to date include:
- Graph data model basics
- Relationship analytics definition
- Relationship analytics applications
- Analysis of large graphs (this post)
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:
- The number of nodes. If the nodes of a graph are people, there’s an obvious upper bound on the node count. Even if you include their houses, cars, and so on, you’re probably capped in the range of 10 billion.
- The number of edges. (Even more important than the number of nodes.) If every phone call, email, or text message in the world is an edge, that’s a lot of edges.
- The typical size of a (node, edge, node) triple. I don’t know why you’d have to go much over 100 bytes post-compression*, but maybe I’m overlooking something.
*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:
- An intelligence scenario, perhaps with billions of nodes and hence trillions of edges.
- A telecom user-analysis case, with perhaps 100 million nodes and hence 100s of billions of edges.
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
|Categories: Analytic technologies, Aster Data, Data models and architecture, Hadoop, Health care, MapReduce, RDF and graphs, Scientific research, Telecommunications, Yarcdata and Cray||20 Comments|
MarkLogic is releasing MarkLogic 5. Key elements of the announcement are:
- More-of-the-same in line with MarkLogic’s core positioning.
- A new bi-directional Hadoop connector.
- A free MarkLogic Express edition, limited in license terms more than in actual features, as per Slide 27 of the deck MarkLogic graciously supplied for me to post.
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:
- MarkLogic is a serious, enterprise-class DBMS (see for example Slide 12 of the MarkLogic deck) …
- … which has been optimized from the getgo for poly-structured data.
- MarkLogic can and does scale out to handle large amounts of data.
- MarkLogic is a general-purpose DBMS, suitable for both short-request and analytic tasks.
- MarkLogic is particularly well suited for analyses with long chains of “progressive enhancement” (MarkLogic’s favorite term when talking about derived data).
- MarkLogic often plays the role of a content assembler and/or search engine, and the people who use MarkLogic in those ways are commonly doing things that can be described as research and analysis.
Based on that reality, MarkLogic talks a lot about Volume, Velocity, Variety, Big Data, unstructured data, semi-structured data, and big data analytics.
|Categories: Hadoop, Market share and customer counts, MarkLogic, Scientific research, Solid-state memory, Structured documents, Text||1 Comment|
As Jacek Becla explained:
- Academic scientists like their software to be open source, for reasons that include both free-like-speech and free-like-beer.
- What’s more, they like their software to be dead-simple to administer and use, since they often lack the dedicated human resources for anything else.
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:
- Give your stuff to academics for free.
- Call their attention to your free offering.
Reasons to do so include:
- Public benefit. Scientific research is important.
- Training future customers. There’s huge academic/commercial crossover, especially as students join the for-profit workforce.
|Categories: Business intelligence, Data warehousing, Infobright, Petabyte-scale data management, Predictive modeling and advanced analytics, Scientific research||7 Comments|
IBM is acquiring Platform Computing, a company with which I had one briefing, last August. Quick background includes: Read more
|Categories: Hadoop, IBM and DB2, Investment research and trading, MapReduce, Parallelization, Scientific research||5 Comments|
I’m not a big fan of conferences, but I really like XLDB. Last year I got a lot out of XLDB, even though I couldn’t stay long (my elder care issues were in full swing). The year before I attended the whole thing — in Lyon, France, no less — and learned a lot more. This year’s XLDB conference is at SLAC — the organization formerly known as the Stanford Linear Accelerator Center — on Sand Hill Road in Menlo Park, October 18-19. As of right now, I plan to be there, at least on the first day. XLDB’s agenda and registration details (inexpensive) can be found on the XLDB conference website.
The only reason I wouldn’t go is if that turned out to be a lousy week for me to travel to California.
The people who go XLDB tend to be really smart — either research scientists, hardcore database technologists, or others who can hold their own with those folks. Audience participation can be intense; the most talkative members I can recall were Mike Stonebraker, Martin Kersten, Michael McIntire, and myself. Even the vendor folks tend to the smart — past examples include Stephen Brobst, Jeff Hammerbacher, Luke Lonergan, and IBM Fellow Laura Haas. When we had a datageek bash on my last trip to the SF area, several guys said they were planning to attend XLDB as well.
XLDB stands for eXtremely Large DataBases, and those are indeed what gets talked about there. Read more
|Categories: Data warehousing, Predictive modeling and advanced analytics, Scientific research||5 Comments|