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Finding a Needle in a Haystack of Data

Posted by ScuttleMonkey on Wed Dec 07, 2005 03:44 PM
from the mathematical-sieve dept.
Math Technology
Roland Piquepaille writes "Finding useful information in oceans of data is an increasingly complex problem in many scientific areas. This is why researchers from Case Western Reserve University (CWRU) have created new statistical techniques to isolate useful signals buried in large datasets coming from particle physics experiments, such as the ones run in a particle collider. But their method could also be applied to a broad range of applications, like discovering a new galaxy, monitoring transactions for fraud or identifying the carrier of a virulent disease among millions of people." Case Western has also provided a link to the original paper. [PDF Warning]
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  • Google (Score:4, Interesting)

    by biocute (936687) on Wednesday December 07 2005, @03:45PM (#14204954) Homepage
    Does Google have the technology to do this kind of scientific searches yet?

    If it does, it sure can save these researchers a lot of time; If it doesn't, I'm sure Google will be keen to get involved, especially on the "isolate useful signals buried in large datasets" part.
    • Does Google have the technology to do this kind of scientific searches yet?

      It's only in Beta thus it's not useful ;-)
      • But can it find potential girlfriends for Slashdotters?

        Wow. There really are't any out there. Check it out on google [google.com] yourselves.

        The same results come back in images, groups, news, etc. Man. What a sad bunch.

  • The most obvious application (Score:5, Interesting)

    by Billosaur (927319) * <wgrotherNO@SPAMoptonline.net> on Wednesday December 07 2005, @03:48PM (#14204983) Journal

    I see this as being a boon to SETI [berkeley.edu]. If there was ever a needle in a haystack, it's trying to tease a possible intelligent signal out of the cosmic background noise. If you have an idea what the background is like in general, then it's far easier to detect an abnormality in that background noise. The question will end up being, are we simply detecting more false positives or are these real signals?

    • Also the first "usefull" application for this kind of technique which popped up in my head. Actually, the process in my head to make this one item popup is maybe usefull too (-: Lot of random data, and this one is being associated with the article.
    • it's been a while since i last did much perl, but shouldn't the last line of your sig be:

      ($world = $world) =~ s/bad/good/g;

      otherwise you're making your world better but not ever doing anything with it...

  • Ya' know... (Score:3, Funny)

    by jacobcaz (91509) on Wednesday December 07 2005, @03:49PM (#14204988) Homepage
    82.67% of all statistics are made up anyway...
    • "82.67% of all statistics are made up anyway..."

      Well yeah, 50% of all statisticians finished in the bottom half of their class.
      • Not necessarily... only works if there are an even number of statisticians, and if nobody scored the mean score.

        eg. if there 100 statisticians, the mean score is 37 and 10 statisticians scored that, only 45% of statisticians are techincally in the bottom half (and 45% in the top half). 10% are exactly in the middle.

        You could say that the 10% are in both the bottom and top half... in which case 55% are in the bottom half and 55% are in the top half!!
  • I can't even find my keys some days.

  • The Real Challenge is Further Off (Score:4, Funny)

    by AthenianGadfly (798721) on Wednesday December 07 2005, @03:50PM (#14205000)

    "But their method could also be applied to a broad range of applications, like discovering a new galaxy, monitoring transactions for fraud or identifying the carrier of a virulent disease among millions of people."

    When asked about more advanced applications for the technology, researchers replied it will probably be "quite a while" before the technology could be used for extremely high noise environments. Said one, "I mean, it's going to be a long time before we're up to finding finding useful comments on Slashdot or something."

  • Sounds like they've been watching Numb3rs ;-)
  • The Case team discovered a technique that is built on the principle of comparing a set of summary characteristics for any sub region of the observations with the background variation. From these characteristics, attempts are made to find small regions that appear significantly different from the background--a difference that cannot simply be attributed to random chance

    So, basically its the one search engine that can only find the words "horny teen nekkid" if it is NOT on a pr0n-page. I can see uses for that

  • Case Western Reserve University (Score:4, Interesting)

    by tomzyk (158497) on Wednesday December 07 2005, @03:57PM (#14205063) Homepage Journal
    FYI: Its abbreviation is not "CWRU" anymore. As of about 2 years ago, they changed it to simply "Case" and gave it the silly new logo of 2 paperclips stuck together.

    Why? I have no idea. Some "university branding" thing that some people thought was important to the growth of the campus or something. Apparently it ticked a bunch of alumni (from the original Western Reserve University) too.

    Knowing is half the battle.
    • The name of the school is still Case Western Reserve University.

      Despite the fact that its OK to officially call it 'Case' now (it wasnt OK to do so in '97), CWRU is still a valid abbreviation. Plus I paid so much money to that place that I'll call it whatever I damn well please.

      - '02

    • Re:Case Western Reserve University (Score:2, Funny)

      by Anonymous Coward
      Actually, its not two paper clips together. It's a fat man holding a surf board. Look for yourself [case.edu]
    • I have to say, I'm glad that my alma mater (Case School of Engineering, 2000) is actually still doing real science. I'm kind of disappointed at all the folks above who posted about "finding useful information in the noise of internet information" though; that type of information gathering is not quite the same as discerning between special-cause and random-cause fluctuations in a signal (mostly because the Internet consists mostly of special-cause variation: i.e., things people have written or created). Dis

  • Speaking of needle in a haystack ... (Score:5, Funny)

    by airrage (514164) on Wednesday December 07 2005, @03:59PM (#14205081) Homepage Journal
    Someone asked me to give ten different ways to find a needle in a haystack, these are my thoughts:

    1) INDUSTRIAL MAGNENT
    2) BLIND LUCK
    3) BURN THE HAY, PICK UP THE NEEDLE
    4) STATISTICAL ANALYSIS (SINCE NEEDLES IN HAYSTACKS ARE NOT PLACED AT RANDOM, THEY ARE SUBJECT TO REGRESSION ANALYSIS)
    5) OFFSHORE TO CHINA WHERE LABOR IS CHEAPER, SEARCH THE HAY WITH 10000 OF WORKERS.
    6) WAIT YEARS UNTIL THE HAY DECAYS, PICK UP THE NEEDLE
    7) SPREADOUT THE HAY, HIRE BAREFOOT HAY WALKERS
    8) TAKE ALL THE HAY, PUT IN A POOL OF WATER - HAY WILL FLOAT, AND NEEDLE WILL SINK
    9) LET COWS EAT THE HAY, X-RAY ALL THE COWS!
    10) TRIAL AND ERROR - ONE PERSON

  • Would this be useful to reduce the computations needed for the SETI@Home folks too? Seems they have a bit of data to sort through... Hell, genetic enginering too. Look for useful patterns in hundreds of DNA strands.
  • Mythbusters did this one already. They built two machines/processes to find needles in haystacks. One used a process to burn away the hay leaving the needles and the other used magnets and gravity to separate the needles from the hay.

    Oh, wait. Their talking about data. Never mind.

  • Significant % of patterns in randomness (Score:3, Informative)

    by G4from128k (686170) on Wednesday December 07 2005, @04:32PM (#14205350)
    Looking for possible patterns in large volumes of data is dangerous because of the high chance that random data will fit some of the myriad patterns tried. If you test data against a thousand possible patterns, then about 50 of them will be found to be present at a statistical significance level of 5% (even if the data is 100% random). "Cancer clusters" are an excellent example of this -- if you slice a dice a population enough different ways you are bound to find some geographic/demographic/ethnographic subgroup with a very high chance of some cancer.


    Its better to either have a a priori hypothesis to look for one specific, pre-defined pattern in a mound data than to see if any pattern is in the data. Or, if one insists on looking for many patterns, then the standards for statistical significance must be correspondingly higher.

    • Re:Significant % of patterns in randomness (Score:5, Informative)

      by zex (214881) on Wednesday December 07 2005, @04:54PM (#14205567) Homepage
      If you test data against a thousand possible patterns, then about 50 of them will be found to be present at a statistical level of 5% (even if the data is 100% random).


      If you're not correcting for multiple hypothesis testing, you are correct. If you do have 100% random data that holds to perfect randomness at all scales (which I'm not sure is even possible) and correct for multiple hypothesis testing, then you'll find exactly what you "should" find: no significant pattern.

      You mention "Cancer clusters" as an example of attribution of significance to insignificant findings. However, these clusters are often found (at least in the genetics research realm) by hierarchical clustering, which is self-correcting for multiple hypothesis testing. If you're speaking of demographic surveys which find that (e.g.) "black females in Tahiti who were exposed to .... are more susceptible to brain cancer", then you're probably right. I too see those as examples of restricting the domain of samples until you find a pattern - but the pattern nonetheless exists.
    • Looking for possible patterns in large volumes of data is dangerous because of the high chance that random data will fit some of the myriad patterns tried.

      No, God put the figure of Jesus in the sky, but made it not look too much like Jesus just to test the difference between the believers and non-believers. Trust me, it was not easy to do all that with nobody looking.

  • Current fraud detection systems in use in the financial industry are based on two primary knowledge bases:

    1. A knowledge of your purchasing pattern as a consumer. To wit, having a statistically significant sample of what are valid transactions as well as knowing your credit score and income.

    Do you shop at high-end stores? Do you use your card for primarily travel and entertainment? Do you use your card for everyday purchases? How much of your line-of-credit do you tend to use?

    2. A comparison of recent

  • Hey, wait a minute! (Score:4, Funny)

    by $RANDOMLUSER (804576) on Wednesday December 07 2005, @04:40PM (#14205434)
    An article posted by Roland Piquepaille with no links back to his site???
    WTF? Roland? You feeling OK?

  • As a particle physicist (Score:5, Interesting)

    by Lord Byron II (671689) on Wednesday December 07 2005, @05:48PM (#14205962)
    As a particle physicist I know exactly the kind of challenge that this is. The SNR is horrible, you've got tons of data, and the data may be distorted by all sorts of sources (background, misalignment, the wrong reaction, etc).

    I also know that these sorts of algorithms are created all of the time. In fact, someone in my lab got his Ph.D. for applying a neural network to this problem. Furthermore, these algorithms are not "plug-n-play". They must be manually adjusted, by a team with a deep in-depth knowledge of the system in order to be useful.

    So trust me when I say that Roland has blown this out of proportion. Congratulations to the CWRU team for getting the PRL paper published, but this is hardly the kind of ground-breaking news that deserves to be on Slashdot.

  • I don't want to rain on the parade (Score:4, Interesting)

    by martin-boundary (547041) on Wednesday December 07 2005, @08:27PM (#14206837)
    I don't want to rain on the parade, but the result is quite possibly wrong.

    If you download the linked paper, on the second page they talk about the Breit-Wigner (Cauchy) density psi, and later they claim that their score process has zero expectation. Now, everyone knows that the Breit-Wigner does not *have* an expectation, and it's often used as an example where the asymptotic normal (Gaussian) distribution approximation doesn't hold. But still, they derive all sorts of distribution formulas involving a chi squared and a Gaussian process, as if there was no problem at all with the Breit-Wigner tails.

    I think their derivation is quite possibly wrong.

      • That's a good point. In the paper, the formula (2) is finite only if the tails of f dominate the tails of psi, so that means that f would have to be at least as fat tailed as the Cauchy. However, the paper doesn't attempt to state any assumptions, so it's hard to see which parts are solid and where there might be handwaving.

        Funnily enough, the density f they use in the monte carlo simulation appears to be truncated to be in the interval [0,2] (otherwise it wouldn't be integrable). That suggests that in pr

    • They are trying to efficiently find a signal in random and chaotic data. Random and chaotic data isn't easy to index.
      • But that's the trick. Finding a good way to index the data.
        • I don't see that there would be any point in indexing it...In an index you're atomizing it down to it's individual meaningless parts. Each and every part is therefore solitary in an index, and cannot be related to any other part of the index in a meaningful way, because all the other parts are equally unrelated to anything and meaningless as well.

          It would be more useful to transform the apparently random data in some way so as to make signals or discrepancies buried in it obvious. There are all kinds of fun
    • Random has NO pattern what so ever. By detecting a pattern, however small, implies non-random data. QED

        -Charles
      • Not really.

        The more you constrain your allegedly random process, such as by insisting that it produce output without "patterns" -- whatever those are -- the less random it actually is.

        To put it in more concrete terms, which is more random -- a coin which flips 50-50 heads/tails with no other constraints whatsoever, or a coin which flips 50-50 but will never, say, flip 100 heads in a row, and will never exactly alternate, and will never produce the bit sequence corresponding to the ASCII encoding of the text
      • If you have an infinite amount of random data, every pattern will be in there somewhere. At least, that's what I was led to believe.
        • If you have an infinite amount of random data, every pattern will be in there somewhere. At least, that's what I was led to believe.

          Yes, but only if you look at smaller segments, which changes your dataset. For example, if you spot the first 30 digits of Pi in an infinitely random set, the question becomes is your random set Pi? If not, the pattern only applies to those 30 digits and thus your set changes and is no longer the infinite set of random data.

          And they aren't dealing with an "infinite" set, but

    • Re:9...9...9...9... (Score:5, Insightful)

      by flynt (248848) on Wednesday December 07 2005, @04:04PM (#14205117)
      Whether you "know" or not is always up for debate, but that's usually for epistemology class. In classical hypothesis testing in statistics, you make a distributional assumption about your data, and then calculate a probability from the data you observed (the p-value) given your initial assumption. If this probability is very low (also an interpretation), you assume your initial distributional assumption was incorrect. There are finer points to it of course, but classical hypothesis testing in statistics is pretty much a reductio ad absurdem in logic.
    • I know the warranty will be void if you shave off the pubic hair yourself (intentional damage to the product), but you might want to try it anyway. Buy the hairless variety next time and you should be in good shape.

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