Overview of Data Warehousing and OLAP

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Advanced Database Management
Systems
Data Mining Concepts
What is data mining?
• It refers to the mining or discovery of new
information in terms of patterns or rules
from vast amount of data.
• It should be carried out efficiently on larger
files or database.
• But, it is not well integrated with DBMS.
• Types of Knowledge Discovered during data
mining:
– Two types of knowledge
• Deductive knowledge : deduces new information based on prespecified logical rules of deduction on the given data.
• Inductive knowledge : discovers new rules and pattern from
supplied data.
– Knowledge can be represented in
• Unstructured – represented by rules or propositional Logic
• Structured – represented in decision tree, semantic networks,
neural networks.
• Result of mining may be to discover new
information:
• Association Rules
• Sequential patterns
• Classification trees
• Goals of Data mining
•
•
•
•
Prediction
Optimization
Classification
Identification
Association Rules
• Market Based Model, Support and
Confidence
• Apriori Algorithm
• Sampling Algorithm
• Frequent – Pattern Algorithm
• Partition Algorithm
Market Based model, Support and
confidence
• Major technology in data mining involves
discovery of association rules.
• XY where X ={x1,x2………,xn},
Y={y1,y2,….,yn}
• If rule: LHSRHS then item set := LHS U
RHS
• Association rule should satisfy some
interest measures: Support and Confidence
• Support for rule LHSRHS with respect to the itemset.
-It refers to how frequently a specific itemset
occurs in
a database
-is the percentage of transactions that contain all of the
items in the itemset.
-support is sometimes called “prevalence” of the rule.
• Confidence of implication shown in rule
– is computed as Support(LHS U RHS)/Support(LHS).
-is the probability that the items in RHS will be purchased
given the item in LHS are purchased.
-confidence is also called the “strength” of the rule.
•
Generate all rules that exceed user specified
confidence and support thresholds by:
(a) Generate all itemsets of support>threshold
(called “large” itemsets)
(b) For each large itemset, generate rules of min
confidence, by: For large itemset X and Y subset
of X, let Z=X-Y. If
support(X)/support(Z)>min_confidence THEN
Z  Y is valid rule.
Problem: combinatorial explosion on # of itemsets
• To solve combinatorial explosion use
properties:
-download closure (A subset of large
itemset must also be large, i.e. each subset
of a large itemset exceeds the min required
support)
-antimontonicity ( A superset of small
itemset is also small:. It does not have
enough support) .
Apriori Algorithm
Algorithm for finding large itemsets
• Let minimum support threshold be 0.5
• Transaction –ID
Items- Bought
101
milk, bread, cookies,
juice
792
milk, juice
1130
milk, egg
1735
bread, cookies, coffee.
• Candidate 1-itemsets are C1 = {milk, bread, juice,
cookies, eggs, coffee}with respective supports =
{0.75,0.5, 0.5,0.5,0.25.0.25}
• Frequent 1-itemset is L1 = {milk, bread, juice,
cookies} since support >= 0.5.
• So, candidate 2-itemsets are: C2 = {{milk, bread},
{milk, juice}, {milk, cookies}, {bread, juice},
{bread, cookies}, {juice, cookies}}, with supports
= {0.25, 0.5, 0.25, 0.25, 0.5, 0.25}
• So, frequent 2-itemsets are: L2 = {{milk, juice},
{bread, cookies}} > =0.5
• Next, construct candidate frequent 3itemsets by adding additional items to sets
in L2.
• For example, {milk, juice, bread}. But
{milk, bread} is not a frequent 2-itemset in
L2, so by downward closure property {milk,
juice, bread} cannot be a frequent 3-itemset.
• Here all 3-extensions will fail, so process
terminates
Sampling Algorithm
(for VLDB-very large databases)
• Sampling algorithm selects a sample of the database transactions, small
enough to fit in main memory, and then determines the frequent
itemsets from that sample.
• If these frequent itemsets form a super set of the frequent itemsets for
the entire database, then the real frequent itemsets can be determined
by scanning the remainder of the database in order to compute the
exact support values for the superset itemsets.
• A superset of the frequent itemsets can be found from the sample by
using Apriori algorithm with a lowered minimum support.
• In some cases frequent item sets may be missed, so the concept of
negative border is used to decide if any were missed.
• The basic idea is that the negative border of a set of frequent itemsets
contains the closest itemsets that could also be frequent.
• Negative border for itemset S and set of items I, is the minimal
itemsets contained in Powerset(I) and not in S.
(continued)
• Consider set of Items I = {A,B,C,D,E}
• Let combined frequent itemsets of size 1 to 3 be S=
{A,B,C,D,AB,AC,BC,AD,CD,ABC}
• Then Negative Border = {E,BD,ACD}
• Where {E} is the only 1-itemset not contained in S, {BD}
is the only 2-itemset not in S but whose 1-itemset subsets
are, and ACD is the only 3-itemset not in S whose 2itemset subsets are all in S.
• Scan remaining database to find support of negative
border. If an itemset X is found in the negative border
which belongs to set of all frequent itemsets, then maybe a
superset of X could also be frequent, which can be
determined by a second pass over the database.
Frequent Pattern Tree algorithm
• It is improves the Apriori algorithm by reducing
number of candidate itemsets that need to be
generated and tested whether they are frequent.
• First produces a compressed version of the
database in the form of a Frequent Pattern (FP)tree
• The FP-tree stores relevant itemset information
and allows for efficient discovery of frequent
itemsets.
• Divide-and-conquer strategy: mining is
decomposed into a set of smaller tasks that each
operate on a smaller, conditional FP-tree which is
a subset of original tree.
• Database is first scanned and frequent 1-itemsets with their support are
computed.
• Here, support is defined as the count of transactions containing the item
rather than fraction of transactions that contain it as in Apriori.
• To construct the FP-tree from transaction table, for a minimum suppor of
say =2:
-Frequent 1-itemsets are stored in Non increasing order of support
{{(milk,3)},{(bread,2)},{(cookies,2)},{(juice,2)}}
-For each transaction, construct sorted list of its frequent items and expand
tree as needed and update the frequent item index table:
First transaction sorted list is T = {milk,bread,cookies,juice}
Second Transaction={milk, juice}
Third transaction= {milk}(since eggs is not a frequent item)
Fourth transaction={bread, cookies}
Resulting FP-tree is as follows:
item support
Milk
3
Bread
2
Link
NULL
Cookies 2
Juice
2
Milk:3
Bread:1
Cookies:1
Juice:1
bread:1
Juice:1
Cookies:1
FP-tree for minimum support
equal to 2. FP represents the
original transactions in a
compressed format.
•
Given the FP-tree and a minimum support, s, the FP-growth algorithm is used to find
frequent itemsets:
• Let s=null;
method FP-growth(original_tree, s)
IF tree contains a single path P then
for each combo, b, of the nodes in the path
generate pattern (b U s) with support=minimum support
of nodes in b;
ELSE
for each item, I, in reverse order of items in frequent itemset list, do
{generate pattern b=(I U s) with support=I.suport;
construct the conditional pattern base for b, following links in FP-tree;
//[example for b=juice: (milk, bread, cookies) and (milk)]
construct b’s conditional FP-tree, beta_tree, by keeping only items of
support greater than min_support;
//example for b=juice, beta_tree only has milk:2 as node, since cookies and //bread have
support=1<2
If beta_tree is not empty then recursively call FP-growth(beta_tree, b);
}
-----------------------------------------------------------------------------------------
Result of FP-growth algo for minimum support of 2:
frequent itemsets are = { (milk:3), (bread:2), (cookies:2), (juice:2), (milk, juice:2), (bread, cookies:2)}
Partition Algorithm
• If a database has a small number of potential frequent
itemsets then their support can be found in one scan using
Partitioning techniques.
• Partitioning divides the database into non overlapping
subsets.
• It can be accommodated in main memory.
• Partition is read only once in each pass.
• Support used is different from the original value.
• Global candidate Large itemset that are identified in pass 1
are verified in pass 2 with support measured for entire
database.
• At the end, all global large itemsets are identified.
Classification
• Learning a model that describes different
classes of data.
• Classes are pre-determined.
• Each model that are designed will be in the
form of decision tree or set of rules.
• Decision tree is constructed from the
training data set
Algorithm for decision tree induction
INPUT :
Set of training records R1,R2,….Rm and set
Procedure Build_tree (Records ,Attributes);
of Attributes A1,A2,…..An.
BEGIN
Create a node N;
If all records belong to the same class ,C then
Return N as a leaf node with class label C;
If Attributes is empty then
Return N as a leaf node with class label C , such that the majority of
Records belong to it;
Select attribute Ai (with the highest information again) from Attributes;
Label node N with Ai;
For each known value , Vj of Ai do
begin
add a branch from node N for the condition Ai = Vj;
Sj = subset of Records where Ai = Vj;
if Sj is empty then
add a leaf , L, with class lable C ,such that the majority
of Records belong to it and Return L
else add the node returned by Build_tree (Sj , Attributes , Aj
);
end;
End;
• Eg : customer who apply for credit card may be classified as “poor
risk”, “fair risk”, “good risk”.
• If customer is married, salary>=50k then good risk.this rules describes
class “good risk”.
Married
yes
no
Salary
<20K
Acct balance
>=5k
>=50k
>=20k
<5k
<50k
Poor risk
good
risk
fair
risk
Poor risk
<25
fair risk
age
>=25
good risk
Clustering
• The goals of clustering is to place records
into groups, such that records in a group are
similar to each other and dissimilar to
records in other groups.
• Groups are usually disjoint.
• Important facet of clustering is similarity
function that is used.
• If data is numeric, similarity function based
on distance is used.
K-means clustering algorithm
Input:
A database D ,of m records r1,r2,…..rm and a desire number
of clusters k
Begin
randomly choose k records as the centroids for the k clusters;
Repeat
assign each record , ri, to a cluster such that the
distance between ri and the cluster centroid (mean) is the smallest among the
k clusters;
recalculate the centroid (mean) for each cluster based on the records assigned
to the cluster;
until no change;
End;
Salary
>=50k
<20k
20k -50k
age
Class is “no”
>=25
Class is “yes”
<25
Class is “no”
Class is “yes”
Approaches to other data mining problem
• Discovery of sequential Pattern
•
•
•
•
Discovery of patterns in Time series
Regression
Neural Networks
Genetic Algorithms
Application of Data mining
• Marketing
Analysis of consumer behaviour based on buying
patterns.
• Finance
Analysis of creditworthiness of clients, finance
investments like stocks, bonds, mutual funds.
• Manufacturing
Optimization of resources like machines, manpower and
materials.
• Health Care
Discovering patterns in radiological images, analyzing
Side effect of drugs
Overview of Data Warehousing and
OLAP
What is Data Warehousing?
• Collection of information as well as
supporting system.
• Designed precisely to support efficient
extraction, processing and presentation for
analytic and decision making purpose
Characteristics of Data Warehouse
•
•
•
•
•
•
•
•
•
•
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Multi dimensional conceptual view
Generic dimensionality
Unlimited dimensions and aggregation levels
Unrestricted cross – dimensional operations
Client-server architecture
Multi user support
Accessibility
Transparency
Intuitive data manipulation
Consistent reporting performance
Flexible reporting
Data modeling for data warehouses
• Multidimensional models – populate data in
multidimensional matrices called data cubes.
– Hierarchical views
• Roll up display
• Drill down display
– Tables
• Dimension table - tuples
• Fact table – attributes.
– Schemas
• Star schema
• Snow flake schema
Building Data Warehouse
• Specifically support ad-hock querying
• Factors:
– Data -Extracted from multiple, heterogeneous
sources
– Formatted for consistency within the warehouse
– Cleaned to validity
– Fitted into the data model
– Loaded into warehouse
Functionality of Data Warehouse
•
•
•
•
•
•
•
Roll - up
Drill – down
Pivot
Slice and Dice
Sorting
Selection
Derived attributes
Difficulties of implementing Data
warehouse
• Operational issues with data warehousing
– Construction
– administration
– quality control.
Data Mining versus Data
warehousing
• Data warehouse – support decision making with
data.
• Data mining with data warehousing help with
certain types of decision.
• To make data mining efficient, data warehousing
should have a summarized collection of data. Data
mining extracts meaningful new patterns just by
processing and querying data in data warehouse.
• Hence, in large database running in terra bytes,
successful application of data mining depends on
construction of data warehouse.
DATA WAREHOUSING
AND OLAP
Overview
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Introduction, Definitions, and Terminology
Characteristics of Data Warehouses
Data Modeling for Data Warehouses
Building a Data Warehouse
Typical functionality of a Data Warehouse
Data Warehouse vs. Views
Problems and Open Issues in Data Warehouses
Questions / Comments
INTRODUCTION,
DEFINITIONS, AND
TERMINOLOGY
Data Warehouse
 A data warehouse is also a collection of information
as well as supporting system.
 They are mainly intended for decision support
applications.
 Data warehouses provide access to data for complex
analysis, knowledge discovery and decision making.
 Data warehouses support efficient extraction,
processing, and presentation for analytic and
decision–making purposes.
CHARACTERISTICS OF
DATA WAREHOUSES
Characteristics of Data Warehouse
 Data warehouse is a store for integrated
data from multiple sources, processed for
storage in a multi dimensional model.
 Information in data warehouse changes
less often and may be regarded as non-real
time with periodic updates.
 Warehouse update are handled by the
warehouse’s acquisition component that
provides all required preprocessing.
cont…
Characteristics of Data Warehouse
Overview conceptual structure of data
warehouse
Back
flushing
Data Warehouse
OLAP
Databases
Cleaning
Reformatting
Meta
Data
DSSI
EIS
DATA
MINING
Updates/New Data
Other Data Inputs
Cont…
Characteristics of Data Warehouse
OLAP (Online Analytical Processing) : Is a
term used to describe the analysis of
complex data from data warehouse.
DSS (decision-support system) also known
as EIS (Executive Information System):
Supports an organization’s leading decision
making with higher level data foe complex
and important decisions.
Data mining: The process of searching data
for unanticipated new knowledge.
cont…
Characteristics of Data Warehouse
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It has multidimensional conceptual view.
It has unlimited dimensions and aggregate levels.
Client server architecture.
Multi-user support.
Accessibility.
Transparency.
Intuitive data manipulation.
Unrestricted cross-dimensional operations.
Flexible reporting.
Cont…
Characteristics of Data Warehouse
 They encompass large volumes of data
which is an issue that has been dealt with
 Enterprise-wide warehouse: Are huge
projects requiring massive investment of
time and resources.
 Virtual data warehouse: Provides views of
operational databases that are materialized
for efficient access.
 Data marts: Are targeted to a subset of the
organization, such as a department and are
more tightly focused.
DATA MODELING OF
DATA WAREHOUSES
Data Modeling for Data Warehouses
 Data can be populated in multi dimensional
matrices called data cubes.
 Query processing in multidimensional model
can be much better than in the relational data
model.
 Changing from one dimensional hierarchy to
another is easily accomplished in a data cube
by a technique called pivoting.
 Data cube can be thought of as rotating to
show a different orientation of the axes.
cont..
Data Modeling for Data Warehouses
 Multidimensional model lend themselves
readily to hierarchical views in what is known
as roll-up display and drill-down display.
 Roll-up display moves up the hierarchy,
grouping into larger units along a dimension.
 Drill-down display gives a fine-grained view.
 A multidimensional storage model involves
two types of tables dimension table and fact
table.
Cont…
Data Modeling for Data Warehouses
 A dimension table consists of tuples of
attributes of the dimension.
 A fact table can be thought of as having
tuples, one per a recorded fact.
 Fact table contains some measured or
observed variable (s) and identifies it with
pointers to dimension table.
 A fact table contains the data and the
dimensions identify each tuple in that data.
Cont…
Data Modeling for Data Warehouses
Two common multidimensional schemas are
 Star schema
 Snowflake schema
 Star schema consists of a fact table with a
single table for each dimension.
 In a snowflake schema the dimensional tables
from a star schema are organized into a
hierarchy by normalizing them.
 A fact constellation is a set of fact tables that
share some dimension tables.
Cont…
Data Modeling for Data Warehouses
Star schema
DIMENSION TABLE
DIMENSION TABLE
FISCAL QUATER
FACT TABLE
BUSINESS RESULTS
PRODUCT
PROD. NO.
PROD. NAME.
PROD. DESCR.
PROD. STYLE
PROD. LINE
QTR
YEAR
BEG DATE
PRODUCT
END DATE
QUARTER
REGION
DIMENSION TABLE
SALES REVENUE
REGION
SUBREGION
Cont..
Data Modeling for Data Warehouses
SNOWFLAKE SCHEMA
DIMENSION TABLE
FISCAL QUATER
DIMENSION TABLES
PNAME
PRODUCT
PROD. NAME
PROD. DESCR
FACT TABLE
BUSINESS RESULTS
PROD. NO.
PROD. NAME.
PROD. DESCR.
PROD. STYLE
PROD. LINE
PLINE
PROD. LINE NO.
PROD. LINE NAME
PRODUCT
QTR
BEG. DATA
YEAR
END DATA
BEG DATE
END DATE
QUARTER
REGION
DIMENSION TABLE
SALES REVENUE
REGION
SUBREGION
Cont…
Data Modeling for Data Warehouses
Join indexes relate the values of a dimension of
a star schema to rows in the fact table.
Data warehouse storage can facilitate access to
summary data .There are 2 approaches
 Smaller tables including summary data such as
quarterly sales or revenue by product line.
 Encoding of level into existing tables.
BUILDING A DATA
WAREHOUSE
Acquisition of Data
 Extracted from multiple, heterogeneous
sources
 Formatted for consistency within the
warehouse
 Cleaned to ensure validity
 Back flushing – process of returning cleaned data to the
source
 Fitted into the data model of the warehouse
 Loaded into the warehouse
Data Storage
 Storing the data according to the data model of
the warehouse
 Creating and maintaining required data structures
 Creating and maintaining appropriate access paths
 Providing for time-variant data as new data are
added
 Supporting the updating of warehouse data
 Refreshing the data
 Purging the data
Design Considerations
 Usage projections
 The fit of the data model
 Characteristics of available sources
 Design of the metadata component
 Modular component design
 Design for manageability and change
 Considerations of distributed and parallel
architecture
TYPICAL FUNCTIONALITY OF A
DATA WAREHOUSE
Preprogrammed Functions
 Roll-up
 Data is summarized with increasing generalization
 Drill-down
 Increasing levels of details are revealed
 Complement of roll-up
 Pivot
 Cross tabulation or rotation is performed
 Slice and Dice
 Performing projection operations
Preprogrammed Functions
(contd…)
 Sorting
 Data is sorted by ordinal value
 Selection
 Data is available by value or range
 Derived (Computed) Attributes
 Attributes are computed by operations on stored and
derived values
Other Functions
 Efficient query processing
 Structured queries
 Ad hoc queries
 Data mining
 Materialized views
 Enhanced spreadsheet functionality
DATA WAREHOUSE vs.
VIEWS
Data Warehouses vs. Views
 Data warehouses exist as a persistent storage
but views are being materialized on demand
 Data warehouses are not usually relational but
multidimensional. Views of a Relational
Database are relational
 Data warehouses can be indexed to optimize
performance. Views cannot be indexed
independent of the underlying databases
Data Warehouses vs. Views
(contd…)
 Data warehouses provide specific support of
functionality but views cannot
 Data warehouses provide large amounts of
integrated and often temporal data, generally
more than is contained in one database,
whereas views are an extract of a database
PROBLEMS AND OPEN
ISSUES IN DATA
WAREHOUSES
Implementation Difficulties
 Project Management
 Design
 Construction
 Implementation
 Administration
 Quality control of data
 Managing a data warehouse
Open Issues
 Data cleaning
 Indexing
 Partitioning
 Views
 Incorporation of domain and business rules
into warehouse creation and maintenance
process making it more intelligent, relevant
and self governing
Open Issues (contd…)
 Automating aspects of the data warehouse
 Data acquisition
 Data quality management
 Selection and construction of appropriate access
paths and structures
 Self-maintainability
 Functionality
 Performance optimization
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