Why Is Data Mining Still A Frontier?

bbsguru writes "How much do we know that we still don't know? A story in The Register points out that little has changed since Francis Bacon proposed combining knowledge to learn new things 400 years ago, despite all the computer power we now have. Scientific (and other) data is still housed in unrelated collections, waiting for some enterprising Relational Database Programmer to unlock the keys to understanding. Is RDBMS still a Brave New Frontier, or will Google make the art obsolete once they finish indexing everything?"

Re:Shot in the dark:

[Daniel Dvorkin]

Neither of those is quite true -- a lot of entities public and private are throwing a lot of money at data mining research, reasonably expecting a big payoff, and sometimes it gets very good results indeed. The basic problem is that, as with any worthwhile CS question, doing it well is hard. It is very easy to come up with false connections between data. Sorting the wheat from the chaff in any kind of automated or even semi-automated fashion, OTOH, is an enormous challenge.

Analogies like this are always dangerous, but I'd say data mining now is about where language development was in the mid-1950's, when FORTRAN was first being developed. IOW, we have a set of tools that kind of work, most of the time, for certain applications -- but we can pretty much guarantee that they're not the best possible tools, and that we will build better ones. Consider how much work is still going on in language development half a century later, and you can see how much room there is for further development.

Data mining is DIFFICULT

[GlobalEcho]

The blurb hit on a fundamental reason data mining is still at (or beyond) the horizon...defining relations between the various elements is hard. Available datasets are not themselves in anything like normal relational form, and so have potential internal inconsistencies. And that gets in the way before you even have the chance to try to form intelligent inferences based on relations between data sets, which of course are terribly inconsistent.

Consider the following boring but difficult task I was given: two large organizations were to merge, each with a portfolio of about 100,000 items. Each item had a short history, some descriptive information, and some data such as internal quality ratings or sector assignments. This data was available (for various reasons) as big CSV file dumps. Questions to answer were: (1) how much overlap did the portfolios have? (2) were the sector distributions similar?

These are very simple, concrete questions. But you can imagine that since the categorizations differed, and descriptors differed within the CSV files, let alone between the two, the questions were difficult to answer. It required a lot of approximate matching, governed intelligently (or so I flatter myself).

Contrast this situation with what people typically think of as data-mining: answering interesting questions, and you can appreciate that without a whole lot of intelligence, artificial or otherwise, those questions will be unanswerable.

Nothing to do with Technology

[wdavies]

This is a hoary chestnut. I have a masters in AI, and a PhD in machine learning (and had a lot of interest in machine discovery).

The ultimate problem, is that for most datasets, there are an infinite (at least), set of relations that can be induced from the data. This doesn't even address the issue, that the choice of available data is a human task. However, going back to assuming we have all the data possible, you still need to have a specific performance task in mind.

Think of this in terms of permutations. Lets say you have variable A, B, and C. They are all binary (have values 1 or 0). Now, you are given a set of these assigments (eg A=1, B=1,C=1, A=1,B=1, C=1, and so on). Now, try to tell me what the correct partition is. Sort them in to two sets of any size. See the problem ? I didn't tell you what I wanted as characteristics of those sets - so in effect, they are all possible good partitions.

So, data-mining ultimately relies on human's deciding what they want to read from the tea-leaves of the data.

Now, give it up, and start addressing issues of efficient algorithms given that you have a specific performance task :)

Winton

Re:Shot in the dark:

[Coryoth]

a lot of entities public and private are throwing a lot of money at data mining research, reasonably expecting a big payoff, and sometimes it gets very good results indeed. The basic problem is that, as with any worthwhile CS question, doing it well is hard. It is very easy to come up with false connections between data. Sorting the wheat from the chaff in any kind of automated or even semi-automated fashion, OTOH, is an enormous challenge.

I would suggest that, in practice, the real difficulty is that the problems that need to really be solved for data mining to be as effective as some people seem to wish it was are, when you actually get down to it, issues of pure mathematics. Research in pure mathematics (and pure CS which is awfully similar really) is just hard. Pretending that this is a new and growing field is actually somewhat of a lie. It's avery very old field which people have been working on for a very long time, to the point where the problems that remain to be solved are incredibly difficult. What is new is someone other than pure mathematicians taking much interest in these problems. Do a search for "non linear manifold learning" on Google and you'll see what I mean.

Jedidiah.