Originally posted by: TonyPearson
The IBM Challenge was a big success. One of the contestants, Ken Jennings, [welcomes our new computer overlords]. Congratulations are in order to the IBM Research team who pulled off this Herculean effort!
Some folks have poked fun at some of the odd responses and wager amounts from the IBM Watson computer during the three-day tournament. Others were surprised as I was that the impressive feat was done with less than 1TB of stored data. Here is what John Webster wrote in CNET yesterday, in hist article [What IBM's Watson says to storage systems developers]:
"All well and good. But here's what I find most interesting as a result of what IBM has done in response to the Grand Challenge that motivated Watson's creators. We know, from Tony Pearson's blog, that the foundation of Watson's data storage system is a modified IBM SONAS cluster with a total of 21.6TB of raw capacity. But Pearson also reveals another very significant, and to me, surprising data point: "When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, the actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1 Terabyte."
What Pearson just said is that the data set Watson actually uses to reach his push-the-button decision would fit on a 1TB drive. So much for big data?"
To better appreciate how difficult the challenge was, and how a small amount of data can answer a billion different questions, I thought I would cover Business Intelligence, Data Retrieval and Text Mining concepts.
Let's start with Business Intelligence.
[Seth Grimes] pointed me to this quote from [A Business Intelligence System], written by Hans Peter Luhn back in October 1958 IBM Journal.
"In this paper, business is a collection of activities carried
on for whatever purpose, be it science, technology,
commerce, industry, law, government, defense, et cetera.
The communication facility serving the conduct of a business
(in the broad sense) may be referred to as an intelligence
system. The notion of intelligence is also defined
here, in a more general sense, as the ability to apprehend
the interrelationships of presented facts in such a way as
to guide action towards a desired goal."
Ideally, when you need "Business Intelligence" to help you make a better decision, you perform data retrieval from a structured database for the specific information you are looking for. In other cases, you might be looking for insight, patterns or trends. In that case, you go "data mining" against your structured databases.
| Apples | Oranges |
| Men | 42 | 25 |
| Women | 21 | 38 |
|
Here's a simple example. John runs a fruit stand. One day, he kept track of how many apples and oranges were bought by men and women. How many questions can we ask against this small set of data? Let's count them:
- How many apples were sold to men?
- How many apples were sold to women?
- How many oranges were sold to men?
- How many oranges were sold to women?
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But wait! For each row and column, we can combine them into totals.
- How many apples were sold in total?
- How many oranges were sold in total?
- How many fruit in total were sold to men?
- How many fruit in total were sold to women?
- How many fruit in total were sold?
|
|
| Apples | Oranges | Total |
| Men | 42 | 25 | 67 |
| Women | 21 | 38 | 59 |
| Total | 63 | 63 | 126 |
|
| Apples | Oranges | Total |
| Men | 42 | 63% | 25 | 37% | 67 |
| 67% | 33% | 40% | 20% | 53% |
| Women | 21 | 36% | 38 | 64% | 59 |
| 33% | 17% | 60% | 30% | 47% |
| Total | 63 | 50% | 63 | 50% | 126 |
| But wait, there's more! Each row and column can be evaluated for relative percentages, as well as percentages of each cell compared to the total. You could make five relevant pie-charts from this data. This results in 16 more questions, such as:
...
- Of the fruit purchased by men, what percentage for apples?
- Of all the apples purchased, what percentage by women?
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And that's not including more ethereal questions, such as:
- Are there gender-specific preferences for different types of fruit?
- What type of fruit do men prefer?
This is just for a small set, two market segments (by gender) and two products (apples and oranges). However, if you have many market segments (perhaps by age group, zip code, etc.) and many products, the number of queries that can be supported is huge. For small sets of data, you can easily do this with a spreadsheet program like IBM Lotus Symphony or Microsoft Excel.
(Photo courtesy of [OLAP, Cubes and Multidimensional Analysis] by Andrew Fryer.)
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But why limit yourself to two dimensions? The above example was just for one day's worth of activity, if John captures this data for every day for historical and seasonal trending, it can be represented as a three-dimensional cube. The number of queries becomes astronomical. This is the basis for Online Analytical Processing (OLAP), and three-dimensional tables are often referred to as [OLAP cubes].
Back in 1970, IBM invented the Structured Query Language [SQL], and today, nearly all modern relational databases support this, including IBM DB2, Informix, Microsoft SQL Server, and Oracle DB. SQL poses two challenges. First, you had to structure the data in advance to the way you expect to perform your ad-hoc queries. Deciding the groups and categories in advance can limit the way information is recorded and captured.
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Second, you had to be skilled at SQL to phrase your queries correctly to retrieve the data you are after. What ended up happening was that skilled SQL programmers would develop "canned reports" with fixed SQL parameters, so that less-skilled business decision makers could base their decisions from these reports.
IBM has fully integrated stacks to help process structured data, combining servers, storage, and advanced analytics software into a complete appliance. IBM offers the [Smart Analytics System] for robust, customized deployments, and recently acquired [Netezza] for pre-configured, and more rapid deployments.
However, the bigger problem is that more than 80 percent of information is not structured!
Semi-structured data like email provides some searchable fields like From and Subject. The rest of the information is unstructured, such as text files, photographs, video and audio. To look for specific information in unstructured sources can be like looking for a needle in a haystack, and trying to get insight, patterns or trends involves text mining.
IBM is a leader in Business Analytics and has made great progress in dealing with unstructured data. This includes [IBM OmniFind Enterprise Edition], [IBM e-Discovery Manager] and [IBM Cognos Business Intelligence].
This, in effect, is what IBM Watson was able to perform so well this week. Finding the needle in the haystacks of unstructured data from 200 million pages of text stored in its system, combined with the ability to apprehend the interrelationships of meaning and subtle nuance, resulted in an impressive technology demonstration. Certainly, this new technology will be powerful for a variety of use cases across a broad set of industries!
To learn more, read the Arizona Daily Star's article [After 'Jeopardy!' win, IBM program steps out].
technorati tags: IBM, Watson, Jeopardy, Challenge, John Webster, CNET, BI, data mining, Text Mining, OLAP, Arizona, Daily Star
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