DM(X)

I will try to continue the series started by Jamie, presenting the other set of queries issued by the Microsoft Association Rules viewer. Recently, a question on these queries appeared on the MSDN Data Mining Forums and the poster raised a very good point: while the stored procedures were intended as internal calls for the built-in viewers, external applications and viewers may want to employ them.

So, here is how the rest of the Association Rules viewer works.

Once the viewer is loaded, the first call is something like:

CALL System.Microsoft.AnalysisServices.System.DataMining.AssociationRules.GetStatistics(’Customers’)

The single parameter of this stored procedure is the mining model name.  The result is a one-row table containing the following columns:

[Read more →]

Tags: DMX by bogdan
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On Saturday, November 15th, the connection to the Table Analysis in the Cloud URL is broken. Until the problem is identified and fixed, here are some workarounds:

- For the web interface, use the http://www.sqlserverdatamining.com/cloud URL

- For the Excel add-in, please change the services connection URL. To do that, click the Connections button in the "Analyze (in the Cloud)" ribbon and change the destination URL to

http://131.107.181.101/CloudDM/TATServices/

NOTE: This temporary solution does not support SSL. Your data is transmitted in clear

I’ll post here an update as soon as the servers are up again

Tags: DMX by bogdan
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The “Data Mining with SQL Server 2008″ book  now has a blog. You can check it out at http://www.SqlDataMiningBook.com

Soon enough, there will be some content there:

- various data mining related postings from Jamie and me — all the product related postings on this blog will be replicated on the book’s blog . With a bit of discipline, all the postings will be tagged with the relevant chapter number

- errata (well, hopefully that won’t be the main topic of the blog )

- any new downloads or other information that may be relevant for the readers

Tags: DMX by bogdan
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The new version of the SQL Data Mining book is finally available, at least at Amazon. If you are currently SQL DM user, you have an opportunity to get a free autographed copy  by filling out a short survey about the way you use SQL Server Data Mining. For more details about the survey, check Jamie’s blog post.

More details about the book here: Data Mining with SQL Server 2008

Tags: DMX by bogdan
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This week, at the KDD (Knowledge Discovery and Data Mining) conference, we (as in Microsoft SQL Server Data Mining team) presented the Table Analysis Tools for the Cloud, a preview for a technology that enables anybody to play with some of the Microsoft’s data mining tools, without any bulky downloads and with zero configuration effort.

Around May this year I practically entered some sort of sabbatical: 3 months to work on an incubation project of my choice (yes, the Microsoft SQL Server organization does this kind of things! if it sounds appealing,  check out our recruiting site or, even better, contact directly our SQL Server Data Mining recruiter, Melsa Clarke - melsac AT microsoft DOT com). With some help from Jamie, various management levels and some nice guys in the SQL Server Data Services team, I gathered the infrastructure for a Software as a Service incubation and set up a web incarnation of the Table Analysis Tools add-in for Excel.

Now it is up and running. The entry page is at sqlserverdatamining.com/cloud, so if I got you bored already and you don’t want to read the rest of this stuff, go ahead and browse that page.

What it is

TAT Cloud is a set of canned data mining tasks that you can use without having SQL Server installed on your machine.  It consists of encapsulations of some common data mining problems, such as detecting key influencers, forecasting, generating predictive scorecards or doing market basket analysis. The tasks can be executed directly from your browser: just go to the web page, upload your data (in CSV) format and run a tool from the toolbar. Even better, the tasks can be executed directly from Excel. For this, however, you will need to have Excel 2007 and install an add-in which can be downloaded from here.

All tasks work on a table (Excel table or a table in CSV format that you upload to the web interface). All tasks produce reports that can be used to learn more about the analyzed data.

Here is a complete list of features:

• - Analyze Key Influencers: it detects the columns that impact your target column. It presents a report of those values in other columns that correlate strongly with values in your target column.
• - Detect Categories (clustering, for data miners) — identifies groups of table rows that share similar characteristics. A categories report is generated, which details the characteristics of each category
• - Fill From Example — to some extent, similar to Excel’s Autofill feature: it learns from a few examples and extends the learned patterns to the remaining rows in the table
• - Forecasting — analyzes vertical series of numeric data, detects periodicity, trends and correlations between series and produces a forecast for those series
• - Highlight Exceptions — finds the interesting (or unusual, or out-of-ordinary) rows in your table
• - Scenario Analysis — What-If and Goal-Seek tools based on a probabilistic model built on top of your data.
• - Prediction Calculator — a tool for generating prediction scorecards
• - Market Basket Analysis — analyzes transaction tables to identify groups of items that appear together in transactions

All features work from Excel. However, not all the features are implemented in the web interface. They will show up eventually!

A really nice presentation of how to use the tools was written by Brent Ozar on SQLServerPedia. Thanks, Brent!

How it works

Your data (CSV file or Excel spreadsheet) is uploaded to the web service site. There, Analysis Services crunches it and produces the reports you get either in the browser or in a different spreadsheet. The data and the mining model used for analyzing it are  deleted immediately after processing. If something bad happens and your session does not conclude successfully (fancy wording for "if it crashes") then both data and models are removed automatically after 15-20 minutes.

You will notice in the Excel add-in (as well as in the web interface) a strange IP address, 131.107.181.99 — this is IP of the service. It will be changed to something cleaner very soon.

Excel uses HTTPS to connect to the service, in an effort to protect your data. However, you should not use this technology on sensitive data.

What it is not

This is not an official shipping Microsoft technology. It is actually less than even a beta. It may crash, it may produce incorrect results, it may be shot down at any time. BTW, I would appreciate if you posted note here or on the Microsoft data mining forums if this happens (particularly about crashes, I should probably know if it gets shut down)!

What next

Well, try the tools: sqlserverdatamining.com/cloud . I recommend downloading the Excel 2007 add-in, rather than using the web application, as it is more functional. Post any questions you might have on the Microsoft data mining forums. And check out this blog periodically for announcements (typically new additions to the web interface) or for more details on how this stuff works

Tags: Cloud, DMX, Excel Add-ins by bogdan
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Today I discovered a very nice Analysis Services client tool. Produced by SQLMinds, the tool seems a great addition to Analysis Services. It provides many components, among them a performance tuning service, an OLAP cube browser for the web and a very nice web front end for data mining, the DM Companion tool which can be launched at http://x32.sqlminds.com/dmcompanion.

So, here are a few really nice things about the DM Companion tool (BTW, a fully working demo is running at the aforementioned URL).

The tool works to some extent like a Data Mining -specific SQL Server Management Studio for the web. Therefore, it allows you connect to the server of your choice (through the Analysis Services HTTP pump). The demo seems to allow anonymous connections (pretty safe  as all the interactions offered by the tool are read-only).  Next you get to chose your AS catalog and the mining model you want to use.

For each model you have the option to browse the content or execute predictions.

The prediction feature provides a nice interface for defining singleton predictions, as you can see below:

The interface supports specifying multiple nested table keys as input, so the tool can perform associative predictions as well. It reminds me of the XMLA Thin Miner sample running on SQLServerDatamining,com, however, it looks much better and is nicely integrated with the rest of the application. While prediction functions do not seem to be directly supported, the application is able to predict a cluster for clustering models.

The model browsing features are really nice. Analysis Services includes a set of sample web viewers for Naive Bayes, Trees and Clustering. This application provides some seriously better looking viewers for these algorithms, and extends the suite at least for Neural Networks (a really nice viewer), Sequence Clustering and Association Rules.  The DM Companion viewers offer all the features in the sample viewers, with a nicer implementation which uses AJAX and has better graphics, plus a solid set of new features, the most spectacular being the interactive dependency net browser and the pie chart visualization for decision trees, which you can see below.

Overall, DM Companion looks like a really nice tool for sharing your data mining implementation on the web

Tags: DMX by bogdan
4 Comments »

Some very interesting, although time-consuming project kept me away from posting in the last month. Mea maxima culpa, I will try to catch up and put some new and interesting content here more often.

Recently, trying to make sense of some prediction results, I realized that the DMX NATURAL PREDICTION JOIN statement has some obvious and some not so obvious dangers, which deserve some clarification.

To summarize, the PREDICTION JOIN operator is used to apply patterns from a trained mining model against new data. The general syntax is

SELECT EducationLevel

FROM MyModel

PREDICTION JOIN

OPENQUERY(MyDataSource, ”SELECT Height, , HairColor, ShoeSize FROM CustomerInfo’) AS T

ON

T.Height = MyModel.Height AND

T.HairColor = MyModel.HairColor AND

T.ShoeSize= MyModel.ShoeSize

In the standard PREDICTION JOIN statement, the ON clauses specify how columns from the new data are to mapped to the input columns of the mining model.

The statement supports a simplified format:

SELECT EducationLevel

FROM MyModel

NATURAL PREDICTION JOIN

OPENQUERY(MyDataSource, ”SELECT Height, HairColor, ShoeSize FROM CustomerInfo’) AS T

In the simplified format, the columns from the new data are mapped to the input columns of the mining model by name. Much easier to type, resulting in a more readable statement!

Now, the natural mapping may end up in three different scenarios. In the first scenario, all the model’s input columns find a corresponding data column in the input. The mapping is performed correctly and everything works as expected. Nothing to blog about in this case!

The second case: some of the inputs are mapped correctly to model columns, some are not, although you would expect them to be mapped correctly. More than a simple user error, this situation is actually “facilitated” by the way the BI Dev Studio works. Assume you start with a table containing 4 columns: Height, HairColor, ShoeSize, EducationLevel and use BI Dev Studio to create a mining structure and a model. BI Dev Studio will generate more friendly names for the columns and your mining model ends up with the structure below:

MyModel

(

Height

[Hair Color],

[Shoe Size],

[Education Level]

)

[Read more →]

Tags: DMX by bogdan
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This an actual question I had to answer in one of the previous Tech Ready conferences: when does one know that feeding more data to the mining model will stop improving accuracy? Or: how much training data is enough? With the new accuracy procedures in SQL Server 2008, I think I have now a better answer.

Algorithms learn from data and, generally, feeding them with more data will improve the accuracy. This improvement, however, is asymptotical. If the training data is statistically representative for the problem space, then a classifier will stop learning after a certain limit: new data points will support existing patterns and will help reducing the risk of over-training,  but will not add more information to the model. The solution I suggest for SQL Server 2008 allows you to figure out if your mining model is likely too gain from adding new data or is likely to maintain the same accuracy.

The solution is based on the cross validation mechanism, so I’ll start with a small presentation of this mechanism. The cross-validation takes a data set, splits it into a number of (roughly equal size) partitions, called folds, then builds a set of mining models (as many as there are folds). Each mining model is built on top of the whole data set minus one of the folds and then the accuracy of the model is measured on the fold that was left out. Effectively, all data is used in training and all data is left out (by at least one mining model). Also, each model is validated against data that was not seen during training. The accuracy measures for each model are then evaluated across all the folds. A good mining solution would provide compact accuracy measurements (i.e. no big differences between the measurements on different folds). Spikes in the measurements may indicate problems with the data. Note that cross validation does not improve the accuracy of the model, it just provides an “honest” estimation of that, reducing the risk of over-training.

In SQL Server 2008, cross validation allows the user to specify how many cases should be used for the procedure. This is mainly for performance purposes — it allows a quick comparison between different mining models/algorithms on the same data set.

Now back to the original problem: would adding new data benefit the accuracy?

[Read more →]

Tags: 2008, DMX, SQL Server by bogdan
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This second post on model filtering deals with nested table filters. The most common use of nested tables in SQL Server Data Mining is to model transaction tables. That is, model Customer entities together with the “bag” of products they purchased.

Nested table filters primarily serve two purposes:

• - Use (in the model) only those nested table rows with a certain property (e.g. consider only products that are not “Coca Cola”, because most people do buy “Coca Cola”, so it is not that interesting)
• - Use (in the model) only those cases where the associated nested tables have certain properties (e.g. - build a prediction model on customers that have purchased Coca Cola)

Notice the difference between the filters: while the first filter removes products from shopping baskets before modeling, the second removes customers from the model (those that have not purchased Coca Cola).

[Read more →]

Tags: 2008, DMX, Modeling, SQL Server by bogdan
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This is the first post in a small series describing my favorite data mining feature in SQL Server 2008, model filtering. Filtering allows you to take full advantage of the mining structure / mining model dichotomy, separating the data staging (in the structure) from the modeling (in the mining model).

SQL Server 2008 allows specifying, for each mining model, a filter to be applied on the training data. The filter acts as a partitioning mechanism inside the mining structure and it is applied on top of any training/testing partitioning already existing at the structure level.  Furthermore, filters can be applied to data in nested tables, but more about this in the next post. Filters allow you to build different models for various partitions of the data with minimal effort, either to obtain better overall accuracy (think of it as manually specifying the first split in a decision tree), or to compare the patterns between different partitions.

So, let’s start with a simple scenario, using the same old Adventure Works data - building a mining model that predicts how likely a customer is to purchase a bike based on various demographic information. Let’s also assume that you know, based on your experience with the data that the Geography factor has in important role and you don’t want your North American patterns to hide (because of volume) the peculiarities of the Pacific market.

Suppose you start with a mining structure defined like below:

[Read more →]

Tags: 2008, DMX, SQL Server by bogdan
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