Data Mining Query Languages

Why??Data mining query language(need of)

Three Different Answers

DMQL: A Data Mining Query Language for Relational Databases (Han et al, Simon Fraser University)

Integrating Data Mining with SQL Databases: OLE DB for Data Mining (Netz et al, Microsoft)

MSQL: A Query Language for Database Mining (Imielinski & Virmani, Rutgers University)

Some Common Ground

Create and manipulate data mining models through a SQL-based interface (“Command-driven” data mining)

Abstract away the data mining particulars Data mining should be performed on data in

the database (should not need to export to a special-purpose environment)

Approaches differ on what kinds of models should be created, and what operations we should be able to perform

DMQL

Commands specify the following: The set of data relevant to the data mining

task (the training set) The kinds of knowledge to be discovered

• Generalized relation• Characteristic rules• Discriminant rules• Classification rules• Association rules

DMQL

Commands Specify the following:Background knowledge

• Concept hierarchies based on attribute relationships, etc.

Various thresholds• Minimum support, confidence, etc.

DMQL

Syntaxuse database <database_name>{use hierarchy <hierarchy_name> for <attribute>}

<rule_spec>related to <attr_or_agg_list>from <relation(s)>[where <conditions>][order by <order list>]{with [<kinds of>] threshold = <threshold_value> [for <attribute(s)>]}

Specify background knowledge

Specify rules to be discovered

Collect the set of relevant data to mine

Specify threshold parameters

Relevant attributes or aggregations

DMQL

Syntax <rule_spec>

find classification rules [as <rule_name>]

[according to <attributes>]

Find association rules [as <rule_name>]

generalize data [into <relation_name>]

others

DMQL

use database Hospital

find association rules as Heart_Health

related to Salary, Age, Smoker, Heart_Disease

from Patient_Financial f, Patient_Medical m

where f.ID = m.ID and m.age >= 18

with support threshold = .05

with confidence threshold = .7

DMQL

DMQL provides a display in command to view resulting rules, but no advanced way to query them

Suggests that a GUI interface might aid in the presentation of these results in different forms (charts, graphs, etc.)

MSQL

Focus on Association Rules Seeks to provide a language both to

selectively generate rules, and separately to query the rule base

Expressive rule generation language, and techniques for optimizing some commands

MSQL

Get-Rules and Select-Rules Queries Get-Rules operator generates rules over

elements of argument class C, which satisfy conditions described in the “where” clause

[Project Body, Consequent, confidence, support]

GetRules(C) [as R1][into <rulebase_name>][where <conds>][sql-group-by clause][using-clause]

MSQL

<conds> may contain a number of conditions, including: restrictions on the attributes in the body or

consequent• “rule.body HAS {(Job = ‘Doctor’}”• “rule1.consequent IN rule2.body”• “rule.consequent IS {Age = *}”

pruning conditions (restrict by support, confidence, or size)

Stratified or correlated subqueries

in, has, and is are rule subset, superset,

and equality respectively

MSQL

GetRules(Patients)where Body has {Age = *}and Support > .05 and Confidence > .7and not exists ( GetRules(Patients)

Support > .05 and Confidence > .7and R2.Body HAS R1.Body)

Retrieve all rules with descriptors of the form “Age = x” in the body, except when there is a rule with equal or greater support and confidence with a rule containing a superset of the descriptors in the body

MSQL

GetRules(C) R1

where <pruning-conds>

and not exists ( GetRules(C) R2

where <same pruning-conds>

and R2.Body HAS R1.Body)

correlated

stratified

GetRules(C) R1where <pruning-conds>and consequent is {(X=*)}and consequent in (SelectRules(R2)

where consequent is {(X=*)}

MSQL

Nested Get-Rules Queries and their optimization Stratified (non-corrolated) queries are

evaluated “bottom-up.” The subquery is evaluated first, and replaced with its results in the outer query.

Correlated queries are evaluated either top-down or bottom-up (like “loop-unfolding”), and there are rules for choosing between the two options

MSQL

GetRules(Patients)where Body has {Age = *}and Support > .05 and Confidence > .7and not exists ( GetRules(Patients)

Support > .05 and Confidence > .7and R2.Body HAS R1.Body)

MSQL

GetRules(Patients)where Body has {Age = *}and Support > .05 and Confidence > .7

Top-Down Evaluation

For each rule produced by the outer, evaluate the inner

not exists ( GetRules(Patients)Support > .05 and

Confidence > .7and R2.Body HAS R1.Body)

MSQL

not exists ( GetRules(Patients)Support > .05 and

Confidence > .7and R2.Body HAS R1.Body)

Bottom-Up Evaluation

For each rule produced by the inner, evaluate the outer

GetRules(Patients)where Body has {Age = *}and Support > .05 and Confidence > .7

MSQL

Choosing between the two In general, evaluate the expression with more

restrictive conditions first Heuristic rules

• Evaluate the query with higher support threshold first• Next consider confidence threshold• A (length = x) expression is in general more restrictive

than (length > x), which is more restrictive than (length < x)

• “Body IS (constant expression)” is more restrictive than “Body HAS”, which is more restrictive than “Body IN”

• Next consider “Consequent IN” expressions• Descriptors of for (A = a) are more restrictive than

wildcards such as (A = *)

Meant to prevent unconstrained queries from being evaluated first

OLE DB for DM

An extension to the OLE DB interface for Microsoft SQL Server

Seeks to support the following ideas: Define a model by specifying the set of

attributes to be predicted, the attributes used for the prediction, and the algorithm

Populate the model using the training data Predict attributes for new data using the

populated model Browse the mining model (not fully

addressed because it varies a lot by model type)

None of the others seemed to support this

OLE DB for DM

Defining a Mining Model Identify the set of data attributes to be

predicted, the set of attributes to be used for prediction, and the algorithm to be used for building the model

Populating the Model Pull the information into a single rowset

using views, and train the model using the data and algorithm specified

Supports complex objects, so rowset may be hierarchical (see paper for more complex examples)

OLE DB for DM

Using the mining model to predictDefines a new operator prediction join.

A model may be used to make predictions on datasets by taking the prediction join of the mining model and the data set.

OLE DB for DM

CREATE MINING MODEL [Heart_Health Prediction]

[ID] Int Key,

[Age] Int,

[Smoker] Int,

[Salary] Double discretized,

[HeartAttack] Int PREDICT, %Prediction column

USING [Decision_Trees_101]

Identifies the source columns for the training data, the column to be predicted, and the data mining algorithm.

OLE DB for DM

INSERT INTO [Heart_Health Prediction]

([ID], [Age], [Smoker], [Salary])

SELECT [ID], [Age], [Smoker], [Salary] FROM Patient_Medical M, Patient_Financial F

WHERE M.ID = F.ID

The INSERT represents using a tuple for training the model (not actually inserting it into the rowset).

OLE DB for DM

SELECT t.[ID], [Heart_Health Prediction].[HeartAttack]

FROM [Heart_Health Prediction]PREDICTION JOIN (SELECT [ID], [Age], [Smoker], [Salary]FROM Patient_Medical M, Patient_Financial FWHERE M.ID = F.ID) as tON [Heart_Health Prediction].Age = t.Age AND

[Heath_Health Prediction].Smoker = t.Smoker AND [Heart_Health Prediction].Salary = t.Salary

Prediction join connects the model and an actual data table to make predictions

Key Ideas

Important to have an API for creating and manipulating data mining models

The data is already in the DBMS, so it makes sense to do the data mining where the data is

Applications already use SQL, so a SQL extension seems logical

Key Ideas

Need a method for defining data mining models, including algorithm specification, specification of various parameters, and training set specification (DMQL, MSQL, ODBDM)

Need a method of querying the models (MSQL)

Need a way of using the data mining model to interact with other data in the database, for purposes such as prediction (ODBDM)