Delivering Business Intelligence With Microsoft SQL Server 2005 - Part 2

1
Delivering Business Intelligence With Microsoft
SQL Server 2005- Part 2
2
Summary of Topics

• Topic 1 What Is Data Mining?
• Topic 2 SQL Server Data Mining Architecture
  Overview
• Topic 3 Tasks Accomplished by Data Mining
• Topic 4 Steps for Data Mining
• Topic 5 Data Mining Algorithms
• Topic 6 Data Mining Structure Model

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Topic 1 What Is Data Mining?

• Data mining works in a manner similar to our
  topographic map. It makes connections within the
  data that may not be readily apparent to the
  human observer. These connections make the data
  easier to understand. Using this new
  understanding, we can efficiently analyze the
  data and set direction, so our organizations do
  not tumble over a cliff.

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Making Connections
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Making Connections -- Planning a route using a
topographic map
                                        
6
Making Predictions

• Data mining provides us with a similar
  capability. We can use the patterns found in our
  data to make predictions of what will happen
  next. Data mining may find patterns in the way
  our clients make use of our services. Based on
  these patterns, we can predict which clients may
  need additional services in the future. Data
  mining may find patterns in the valid data
  entered for a particular screen. Based on these
  patterns, we can predict whether a newly entered
  set of data is accurate or contains a data entry
  error.

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Basic Data Mining Terminology

• The data mining industry uses a lot of terms
  fairly loosely, so its helpful for us to define
  a few of these terms early on. This is not an
  exhaustive list of data mining terms, only the
  relevant ones for our discussion.
• Algorithm The programmatic technique used to
  identify the relationships or patterns in the
  data.
• Model The definition of the relationship
  identified by the algorithm, which generally
  takes the form of a set of rules, a decision
  tree, a set of equations, or a set of
  associations.
• Case The collection of attributes and
  relationships (variables) that are associated
  with an individual object, usually a customer.
  The case is also known as an observation.

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Basic Data Mining Terminology

• Case set A group of cases that share the same
  attributes. Think of a case set as a table with
  one row per unique object (like customer). Its
  possible to have a nested case set when one row
  in the parent table, like customer, joins to
  multiple rows in the nested table, like
  purchases. The case set is also known as an
  observation set.
• Dependent variable(s) (or predicted attribute or
  predict column) The variable the algorithm will
  build a model to predict or classify.

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Basic Data Mining Terminology

• Independent variable(s) (or predictive attribute
  or input column) The variable with descriptive
  information used to build the model. The
  algorithm creates a model that uses combinations
  of independent variables to define a grouping or
  predict the dependent variable.

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Basic Data Mining Terminology

• Discrete or Continuous Variables Numeric columns
  that contain continuous or discrete values. A
  column in the Employee table called Salary that
  contains the actual salary values is a continuous
  variable. You can add a column to the table
  during data preparation called SalaryRange,
  containing integers to represent encoded salary
  ranges (1 0 to 25,000 2 between 25,000
  and 50,000 and so on). This is a discrete
  numeric column. Early data mining and statistical
  analysis tools required the conversion of strings
  to numeric values like the encoded salary ranges.
  Most tools, including most of the SQL Server data
  mining algorithms, allow the use of character
  descriptions as discrete values. The string 0 to
  25,000 is easier to understand than the number
  1. Discrete variables are also known as
  categorical. This distinction between discrete
  and continuous is important to the underlying
  algorithms in data mining, although its
  significance is less obvious to those of us who
  are not statisticians.

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Basic Data Mining Terminology

• Regression A statistical technique that creates
  a best-fit formula based on a dataset. The
  formula can be used to predict values based on
  new input variables. In linear regression, the
  formula is the equation for a line.
• Deviation A measure of how well the regression
  formula fits the actual values in the dataset
  from which it was created.

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Basic Data Mining Terminology

• Mining Structure A Microsoft data mining term
  used as a name for the definition of a case set
  in Analysis Services. The mining structure is
  essentially a metadata layer on top of a data
  source view that includes additional data
  mining-related flags and column properties, like
  the field that identifies a column as input,
  predict, both, or ignore. A mining structure can
  be used as the basis for multiple mining models.
• Mining Model The specific application of an
  algorithm to a particular mining structure. You
  can build several mining models with different
  parameters or different algorithms from the same
  mining structure.

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Topic 2 SQL Server Data Mining Architecture
Overview
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SQL Server Data Mining Architecture Overview

• Point A in Architecture Figure shows how data
  mining models are built using the dimensional
  engine, leveraging its ability to load data and
  quickly perform the base statistical calculations
  like sums, averages, and counts. The data mining
  server can also easily pull case data from both
  relational Analysis Services databases as seen at
  point B in ArchitectureFigure.

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SQL Server Data Mining Architecture Overview

• Point C in Architecture Figure shows how the
  typical developer will first experience data
  mining by creating a BI Studio Analysis Services
  project and then using the Data Mining Wizard to
  create a new data mining structure and an initial
  data mining model. The mining structure is a new
  construct that provides a metadata layer allowing
  several mining models to work with the same input
  data. Each mining model in a mining structure can
  have different algorithms and parameters. The
  wizard provides model building guidance with auto
  selection and adjustment of variables based on
  the algorithm selected. The wizard also helps you
  create case sets, including complex, nested
  queries.

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Build, Deploy, and Process

• Most of the functions in the Data Mining Designer
  work with the actual model as it exists in
  Analysis Services. This means once the wizard is
  complete, the developer must build and deploy the
  project (which includes processing the model
  cubes) before any more progress can be made.
  Building the project writes the metadata out to
  project files in the development environment. The
  actual model does not come into being until the
  project is deployed to an Analysis Services
  instance. At that point, BI Studio creates a
  database for the project in Analysis Services. It
  writes out the model structure metadata and the
  definition of each model. Finally, it creates a
  cube for each model and processes the models,
  inserting the training data so the algorithm can
  calculate the rules, correlations, and other
  relationships. Until the project is deployed and
  a model is processed, it cannot be viewed in the
  viewers.

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Build, Deploy, and Process

• Most of the functions in the Data Mining Designer
  work with the actual model as it exists in
  Analysis Services. This means once the wizard is
  complete, the developer must build and deploy the
  project (which includes processing the model
  cubes) before any more progress can be made.
  Building the project writes the metadata out to
  project files in the development environment. The
  actual model does not come into being until the
  project is deployed to an Analysis Services
  instance. At that point, BI Studio creates a
  database for the project in Analysis Services. It
  writes out the model structure metadata and the
  definition of each model. Finally, it creates a
  cube for each model and processes the models,
  inserting the training data so the algorithm can
  calculate the rules, correlations, and other
  relationships. Until the project is deployed and
  a model is processed, it cannot be viewed in the
  viewers.

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Accessing the Mining Models

• As you see at Point D in ArchitectureFigure, Data
  Mining eXtensions to SQL language (DMX) is at the
  core of all the Microsoft data mining API. As the
  name suggests, DMX is an extension to SQL
  designed to create, train, modify, and query data
  mining models. DMX was introduced with SQL Server
  2000 as part of the OLE DB for Data Mining APIs.
  It has been enhanced in SQL Server 2005 with
  additional options for data sources and more
  flexible SELECT functionality. An easy way to
  begin learning about DMX is to use the Mining
  Model Prediction tab in the Data Mining Designer
  and examine the syntax it generates for DMX
  queries. The code can be copied to a DMX query
  window in SQL Studio for further exploration.
  Although DMX is an extension to SQL, queries are
  submitted to the Analysis Services serverthats
  where the data mining services are.

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Accessing the Mining Models

• When the development environment submits its DMX
  commands and queries to Analysis Services, it
  uses the XML for Analysis (XMLA) APIs. In fact,
  because Analysis Services is a native XMLA
  server, the data mining models are available to
  any application as a web service by using SOAP
  protocols. It is still possible to access the
  server with old OLE DB APIs, or ADO and ADO.NET.

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Integration Services and Data Mining

• Integration Services can play a major role in the
  data mining process as shown in
  ArchitectureFigure. Many of the standard
  transforms used for data cleansing and data
  integration are particularly valuable for
  building the training and test case sets. Besides
  the obvious tasks, like Data Conversion and
  Derived Column, tasks like the Percentage
  Sampling, Row Sampling, Conditional Split,
  Lookup, and Merge Join are powerful components
  the data miner can use to build a set of packages
  to prepare the case sets for the data mining
  process. This is shown at point E in
  ArchitectureFigure. In addition to the standard
  tasks, there are two Integration Services tasks
  that directly interact with the data mining
  models, shown at point F in ArchitectureFigure.

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Integration Services and Data Mining

• The Data Mining Model Training destination task
  is the target for a training set used to train
  (or re-train) an existing mining model. This
  capability is perfect for the ongoing re-training
  required to keep certain mining models
  currentrecommendation models, for example. The
  Data Mining Query task is specifically designed
  to do prediction joins against a model in the IS
  pipeline, once the model has been trained and
  tested. This could be used to add scores to the
  Customer table or identify significant data
  anomalies during the nightly ETL process. It
  could also be used in a real-time mode to flag
  transactions that were potentially fraudulent.

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Additional Features

• There are several additional features that
  will be important for certain applications. Most
  of these can be found at point G in
  ArchitectureFigure and are listed briefly here
• Extensibility Microsoft has provided a set of
  COM APIs that allow developers to integrate
  additional data mining algorithms into the data
  mining engine. They can integrate custom viewers
  into the data mining designer as well. Someone
  could even create a new viewer for an existing
  Microsoft algorithm.
• Analysis Management Objects (AMO) AMO is a new
  API for managing the creation and maintenance of
  data mining objects, including creating,
  processing, backing up, restoring, and securing.

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Additional Features

• Stored procedures and user-defined functions A
  developer can create what are essentially stored
  procedures or user-defined functions and load
  them as managed assemblies into Analysis
  Services. This allows clients to work with large
  mining models through the intermediate layer of
  the server-based managed assembly.
• Text mining It is possible to do some
  interesting data mining on unstructured text
  data, like the text in HTML files in a set of web
  directories, or even text fields in a database.
  For example, use the Integration Services Term
  Extraction transformation to build a dictionary
  of terms found in the text files. Then use data
  mining to create classification rules to
  categorize the documents according to the terms
  they contain. This is essentially a data mining
  application, not a new data mining algorithm, but
  it has value in certain areas.

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Architecture Summary

• The previous slidfes show how data mining fits
  into the overall SQL Server 2005 architecture,
  and to show how well data mining has been
  integrated into the SQL Server environment. It
  should be clear by now that data mining is a
  serious element of the BI toolset. Data mining
  and application developers will have a lot more
  to learn before they are proficient with all the
  data mining components. Fortunately, Microsofts
  documentation is heavily weighted toward the
  development community. Additional help should be
  easy to find. A good place to start is a web site
  called www.sqlserverdatamining.com, maintained by
  the SQL Server data mining development team.

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Topic 3 Tasks Accomplished by Data Mining

• Classification
• Regression
• Segmentation
• Association
• Sequence Analysis
• Probability Predictions

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Classification

• we may need to classify a potential customer as
  someone who is likely to need our services or
  someone who is not. If we know how to classify an
  individual, an entity, or a thing, we can make
  more intelligent decisions as we deal with that
  individual, entity, or thing.

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Example of Classification using Tree Induction
Customer renting property gt 2 years
No
Yes
Rent property
Customer agegt45
No
Yes
Rent property
Buy property
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Example of Classification using Neural Induction
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Regression

• Classification is used to predict the value for a
  discrete attribute, meaning an attribute that has
  one of a set number of distinct values.
  Regression, on the other hand, is used to predict
  a continuous value.
• To predict a continuous value, regression looks
  at trends likely to continue and repeat over
  time.
• Like classification, regression also looks at
  relationships between the value being predicted
  and other continuous values available in the
  data.

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Regression - Value Prediction

• Linear regression attempts to fit a straight line
  through a plot of the data, such that the line is
  the best representation of the average of all
  observations at that point in the plot.
• Problem is that the technique only works well
  with linear data and is sensitive to the presence
  of outliers (i.e.., data values, which do not
  conform to the expected norm).

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Segmentation

• Segmentation is the "divide and conquer" approach
  to data analysis. Segmentation divides data into
  groups with similar characteristics. We can then
  analyze the characteristics of each group for
  insights.
• Applications of database segmentation include
  customer profiling, direct marketing, and cross
  selling.

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Example of Database Segmentation using a Scatter
plot
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Association

• Association requires we have some type of
  grouping in our data. Multiple items could be
  grouped together in a single sales transaction.
  Multiple services could be provided to a single
  family unit.
• Classification, segmentation, and association can
  sometimes run together. They seem similar. The
  difference is this With classification, we pick
  the attribute that defines the grouping and the
  algorithm determines what most distinguishes
  those groupings. With segmentation, the algorithm
  itself creates the grouping, based on what it
  determines as distinguishing characteristics.
  With association, the underlying data already
  contains some type of grouping. The algorithm
  makes determinations about items likely to be in
  the group, rather than attributes they are likely
  to have in common.

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Sequence Analysis

• Sequence analysis is often applied to navigation
  on a website. A user is on Page A. The user is
  then most likely to navigate to Page B, with a
  somewhat smaller probability of navigating to
  Page C.
• Finds patterns between events such that the
  presence of one set of items is followed by
  another set of items in a database of events over
  a period of time.
• e.g. Used to understand long term customer buying
  behaviour.

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Probability Predictions

• It mathematically analyzes what has occurred in
  the past and determines what is most probable to
  occur if present trends continue.

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Topic 4 Steps For Data Mining

• Problem Definition
• Data Preparation
• Training
• Validation
• Deploy

37
Problem Definition

• The first step for a data mining solution is to
  define the problem we are trying to solve. Data
  mining is great at finding patterns and
  relationships that are not apparent to human
  eyes. However, it cannot solve problems unless we
  point that analytical power in a specific
  direction.
• We need to define the business goal we are
  working toward. Perhaps we want to do a better
  job at identifying perspective customers who are
  a bad credit risk. Perhaps we want to identify
  customers who are likely to switch to our
  competitor's products or services. Perhaps we
  want to predict future sales to put in place the
  capacity to meet those needs.

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Problem Definition

• Once the business goal is defined, we determine
  what we need
• data mining to do for us. We select which of the
  five data mining
• tasks we are looking to accomplish
• Classification
• Regression
• Segmentation
• Association
• Sequence Analysis
• Our choice of task influences our choice of data
  mining
• Algorithm to be discussed in topic 3 .

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Data Preparation

• When we learned about OLAP cube design, one of
  our main concerns
• was where the data to populate the cube was going
  to come from.
• With data mining, we have the same concern where
  are we going to
• get the data to be fed into our data mining
  algorithms?

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Training

• With algorithms selected and our data in place,
  we begin the process of working with the data
  mining model. You can think of a data mining
  model as the combination of one or more data
  mining algorithms with some real, live data. The
  model applies the algorithms to the data and
  creates the classifications, associations,
  regression formulas, and so forth that we use to
  solve our business problem.

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Training

• This step is known as training the data mining
  model. We provide our data to the data mining
  algorithms, so they can be trained on a
  particular aspect of our organization. They
  "learn" what is unique about our data, as well as
  the operations and entities, customers, products,
  and sales transactions that produced that data.

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Validation

• We spoke previously in this chapter about
  providing a data mining algorithm with past data,
  so it could find the classifications,
  associations, and so forth contained within. In
  actuality, we often divide our past data into two
  sets. One set of data is used to train the data
  model. The second set is used to validate the
  model after it is trained. Let's return to our
  credit risk example for an illustration.

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Deploy

• Once we test the data mining model and determine
  which data mining algorithm gives us the most
  accurate information, we can put it to work. We
  place the model where it can be accessed by the
  front-end tools that will put it to work. We use
  the Data Mining Extensions (DMX) query language
  to feed new records to our algorithm and have it
  generate information.

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Topic 5 Data Mining Algorithms

• SQL Server 2005 provides us with seven data
  mining algorithms. Most of these algorithms
  perform several different tasks. Having a
  detailed understanding of the inner workings of
  each algorithm is unnecessary. Instead, this
  section provides a brief explanation of each to
  give you some background information. More
  important is the knowledge of what each can be
  used to accomplish.

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Data Mining Algorithms

• Microsoft Decision Trees
• Microsoft Naïve Bayes
• Microsoft Clustering
• Microsoft Association
• Microsoft Sequence Clustering
• Microsoft Time Series
• Microsoft Neural Network

46
5.1 Microsoft Decision Trees

• Function The Microsoft Decision Trees
  algorithm is one of the easiest algorithms to
  understand because it creates a tree structure
  during its training process. (You probably
  already guessed that from the name.) The tree
  structure is then used to provide predictions and
  analysis. .

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The Microsoft Decision Trees Algorithm
                                               

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Tasks

• The main purpose of the Microsoft Decision Trees
• algorithm is Classification.
• It can also be used for Regression Association.
  

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5.2 Microsoft Naïve Bayes

• Microsoft's SQL Product Manager, Donald Farmer,
  claims because there is a Naïve Bayes algorithm,
  there must be a "deeply cynical" Bayes algorithm
  out there somewhere in data mining land. I guess
  it is needed to bring balance to data mining's
  version of the "force." We will try not to be too
  Naïve as we explore the benefits and shortcomings
  of this algorithm.

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Function

• The Naïve Bayes algorithm looks at each attribute
  of the entity in question and determines how that
  attribute, on its own, affects the attribute we
  are looking to predict. Figure 5.2 shows a Naïve
  Bayes algorithm being used to predict whether a
  customer is a good credit risk. One by one, the
  Naïve Bayes algorithm takes a single attribute of
  a customer, size of company, annual revenue, and
  so forth and looks at the training data to
  determine its effect on credit risk.

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Figure 5.2 The Naïve Bayes algorithm
                                               

52
Function

• In our diagram, 57 of companies with a size
  attribute of small are bad credit risks. Only 14
  of companies with a size attribute of large are
  bad credit risks. In this particular example, it
  looks pretty cut and dried we should never
  extend credit to small companies and we should
  always extend credit to large companies.

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Tasks

• The Naïve Bayes algorithm can only be used for
  Classification.

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5.3 Microsoft Clustering

• The Microsoft Clustering algorithm builds
  clusters of entities as it processes the training
  data set. This is shown in Figure 5-3. Once the
  clusters are created, the algorithm analyzes the
  makeup of each cluster. It looks at the values of
  each attribute for the entities in the cluster.
• Cluster analysis lets the data miner assemble
  data into unforeseen groups containing similar
  characteristics. Also known as "segmentation,"
  this type of data
• mining is probably the most widely used.

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Figure 5.3 The Microsoft Clustering Algorithm
                                               

56
Tasks

• The main purpose of the Microsoft
• Clustering algorithm is
• Segmentation
• Regression
• ClassificationTree Induction Neural Induction

57
Database Segmentation

• Aim is to partition a database into an unknown
  number of segments, or clusters, of similar
  records.
• Uses unsupervised learning to discover
  homogeneous sub-populations in a database to
  improve the accuracy of the profiles.

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Example of Database Segmentation using a Scatter
plot
59
Example of Classification using Tree Induction
60
Example of Classification using Neural Induction
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5.4 Microsoft Association

• The Microsoft Association algorithm creates its
  own sets of entities, and then determines how
  often those sets occur in the test data set. This
  is shown in Figure 5.4. In this set, we are
  looking at groupings of products purchased
  together in a single purchase transaction. To
  simplify our example, we are only looking at
  products in the World War II product subtype.

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Figure 5.4 The Microsoft Association algorithm
                                               

63
Tasks

• The Microsoft Association algorithm can
• only be used for Association.

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5.5 Microsoft Sequence Clustering

• The Microsoft Sequence Clustering algorithm
  examines the test data set to identify
  transitions from one state to another. The test
  data set contains data, such as a web log showing
  navigation from one page to another or perhaps
  routing and approval data showing the path taken
  to approve each request. The algorithm uses the
  test data set to determine, as a ratio, how many
  times each of the possible paths is taken.

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Tasks

• The main purpose of the Microsoft Sequence
  Clustering algorithm is Sequence Analysis
• Segmentation.
• It can also be used for Regression
  Classification.

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5.6 Microsoft Time Series

• The Microsoft Time Series algorithm starts with
  time-related data in the test data set. In Figure
  5.6, this is sales data for each month. To
  simplify things, we are only looking at data for
  two products in our example.

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Figure 5.6 The Microsoft Time Series algorithm
                                               

68
Tasks

• The Microsoft Time Series algorithm can only be
  used for Regression.

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5.7 Microsoft Neural Network

• Neural networks were developed in the 1960s to
  model the way human neurons function. Microsoft
  has created the Microsoft Neural Network
  algorithm, so we can use neural networks for such
  mundane activities as predicting product sales.
  Of course, predicting product sales might not
  seem so mundane if your future employment is
  dependent on being correct.

70
Tasks

• The main purpose of the Microsoft Neural Network
• algorithm is
• Classification
• Regression

71
Topic 6 Data Mining Structure Model

• The data mining structure enables us to define
  the data we will use, either from a relational
  database or from an OLAP cube, as the source for
  the mining. It also lets us define one or more
  data mining algorithms to apply to that data.

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Data Columns

• The first step in doing any mining is to select
  the data being mined. We select a data source
  either relational data or an OLAP cube. Once this
  is done, we need to select a table if this is a
  relational data source, or a dimension if this is
  an OLAP data source. Finally, we must select the
  table columns or dimension attributes to be used
  as the data columns for our data mining.

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Data Columns

• Although an initial table or dimension must be
  selected for data mining, it is possible to
  select additional related tables or dimensions
  for inclusion. These are known as nested tables.
  If a relational database is being used, the
  initial table and the nested table must be linked
  by a foreign key relationship (or a chain of
  foreign key relationships). If a cube is being
  used, the initial dimension and the nested
  dimension must be linked through the fact table.

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Data Mining Model

• The data mining model combines the data columns
  with a data mining algorithm. Additionally, we
  must determine how each data column should be
  used by the data mining algorithm. This process
  determines how the data mining algorithm
  functions and what it predicts.

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Data Column Usage

• Each column in the data mining structure must
  have one
• of the following usages
• Key The key is the unique identifier for a
  table or a dimension. The key is not used to
  uniquely identify records or members and it is
  not used by the data mining algorithm to predict
  values.
• Input Input columns are used by the data mining
  algorithm when making a prediction. The input
  values are the values that influence the
  prediction made by the algorithm.

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Data Column Usage

• Predict A predict is a data column whose value
  is being predicted by the data mining algorithm.
  This column can also be used as an input column.
  If data column A and data column B are both
  predict, then data column A will serve as an
  input when the algorithm is predicting data
  column B, and vice versa.

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Data Column Usage

• Predict Only A predict only is a data column
  whose value is being predicted by the data mining
  algorithm. This column cannot be used as an input
  column when predicting other columns.
• Ignore This data column is not used by the data
  mining algorithm.

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Using Cube SliceAs a Training Set

• The fact that data must be divided into training
  and
• testing data sets. The cube slice enables us to
  specify an
• expression that divides the cube into two parts.
  The
• portion of the data that satisfies the expression
  is fed into
• the data mining algorithm for training. The rest
  of the
• data will be testing data set.

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Defining a Data Mining Model

• Steps
• Business Need
• Select the data mining algorithms
• Determine where the data source

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Mining Model Viewer

• Now that we have several data mining models
  created and trained, let's look at how we begin
  to analyze the information. We do this by using
  the data mining model viewers, which are found on
  the Mining Model Viewer tab on the Data Mining
  Design tab.
• Each data mining algorithm has its own set of
  viewers, enabling us to examine the trained data
  mining algorithm. These viewers present the
  mining information graphically for ease of
  understanding. If desired, we can also use the
  Microsoft Mining Content Viewer to look at the
  raw data underlying the graphical presentations.

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Mining Model Viewer

• Microsoft Tree Viewer
• Microsoft Mining Content Viewerused to see the
  detailed data underlying the diagrams

82
The SQL Server Management Studio Windows

• Registered Servers Window
• Object Explorer Window
• Summary Window
• Query Windows

83
Query Windows

• One of the functions of the SQL Server Management
  Studio is the creation and execution of queries.
  These queries may be Transact-SQL for relational
  databases, MDX or XMLA for Analysis Services
  databases, DMX for data mining, or SQL Mobile for
  hand-held devices. Each type of query has its own
  specific type of query window. We discuss how to
  use these query windows throughout the rest of
  this book.

84
MDX Definition

• Multidimensional Expression (MDX) language
  provides the programming language for OLAP Cube
  navigation and over 50 mathematical functions for
  calculating cube measures. It can be called and
  used in the relational database language of
  Transact-SQL.

85
DMX Definition

• Data Mining Expression (DMX) language is a new
  language in SQL Server 2005, which provides the
  commands to easily set up data mining. It can be
  called and used in the relational database
  language of Transact-SQL

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XML Definition

• XML for Analysis Services (XMLA) is an open,
  XML-based standard protocol for interacting with
  Microsoft SQL Server 2005 Analysis Services data
  over an HTTP connection, such as an intranet or
  the Internet. XMLA uses the Simple Object Access
  Protocol (SOAP).

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Mining Accuracy Chart

• Before we put a data mining model to use in a
  production environment, we need to insure the
  model is making predictions with the desired
  accuracy. This is done using the Mining Accuracy
  Chart tab in the Business Intelligence
  Development Studio. The Mining Accuracy Chart tab
  provides three tools for determining the accuracy
  of our mining models the Lift Chart, the Profit
  Chart, and the Classification Matrix.

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Deployment

• As you see in Figure 6.1, the deployment step in
  the Business Dimensional Lifecycle is where the
  three parallel development tracks come back
  together. This is the great unveiling of the
  DW/BI system to the business community. The
  quality of this first impression will strongly
  influence the acceptance of the systemand you
  get only one shot at it. Like any big event,
  there are a lot of details that must fall into
  place in order for the show to be successful.

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Figure 6.1 The Deployment step in the Business
Development Lifecycle
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Deployment

• What needs to be tested in the DW/BI system
  before deployment?
• How and where should you perform that testing?
• Whats the easiest way to deploy the completely
  new DW/BI system, and how can you safely test and
  deploy changes to an existing system?
• Why is DW/BI documentation important, and what
  documentation does your team need to write?
• What kind of support and training will your users
  need?