Chapter 4. Data Warehousing and On-Line Analytical Processing.ppt

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Data Mining:
Concepts and Techniques
(3rd ed.)
Chapter 4
Subrata Kumer Paul
Assistant Professor, Dept. of CSE, BAUET
sksubrata96@gmail.com

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Chapter 4: Data Warehousing and On-line
Analytical Processing
 Data Warehouse: Basic Concepts
 Data Warehouse Modeling: Data Cube and OLAP
 Data Warehouse Design and Usage
 Data Warehouse Implementation
 Data Generalization by Attribute-Oriented
Induction
 Summary

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What is a Data Warehouse?
 Defined in many different ways, but not rigorously.
 A decision support database that is maintained separately from
the organization’s operational database
 Support information processing by providing a solid platform of
consolidated, historical data for analysis.
 “A data warehouse is a subject-oriented, integrated, time-variant,
and nonvolatile collection of data in support of management’s
decision-making process.”—W. H. Inmon
 Data warehousing:
 The process of constructing and using data warehouses

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Data Warehouse—Subject-Oriented
 Organized around major subjects, such as customer,
product, sales
 Focusing on the modeling and analysis of data for
decision makers, not on daily operations or transaction
processing
 Provide a simple and concise view around particular
subject issues by excluding data that are not useful in
the decision support process

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Data Warehouse—Integrated
 Constructed by integrating multiple, heterogeneous data
sources
 relational databases, flat files, on-line transaction
records
 Data cleaning and data integration techniques are
applied.
 Ensure consistency in naming conventions, encoding
structures, attribute measures, etc. among different
data sources
 E.g., Hotel price: currency, tax, breakfast covered, etc.
 When data is moved to the warehouse, it is
converted.

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Data Warehouse—Time Variant
 The time horizon for the data warehouse is significantly
longer than that of operational systems
 Operational database: current value data
 Data warehouse data: provide information from a
historical perspective (e.g., past 5-10 years)
 Every key structure in the data warehouse
 Contains an element of time, explicitly or implicitly
 But the key of operational data may or may not
contain “time element”

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Data Warehouse—Nonvolatile
 A physically separate store of data transformed from the
operational environment
 Operational update of data does not occur in the data
warehouse environment
 Does not require transaction processing, recovery,
and concurrency control mechanisms
 Requires only two operations in data accessing:
 initial loading of data and access of data

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OLTP vs. OLAP
OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response

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Why a Separate Data Warehouse?
 High performance for both systems
 DBMS— tuned for OLTP: access methods, indexing, concurrency
control, recovery
 Warehouse—tuned for OLAP: complex OLAP queries,
multidimensional view, consolidation
 Different functions and different data:
 missing data: Decision support requires historical data which
operational DBs do not typically maintain
 data consolidation: DS requires consolidation (aggregation,
summarization) of data from heterogeneous sources
 data quality: different sources typically use inconsistent data
representations, codes and formats which have to be reconciled
 Note: There are more and more systems which perform OLAP
analysis directly on relational databases

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Data Warehouse: A Multi-Tiered Architecture
Data
Warehouse
Extract
Transform
Load
Refresh
OLAP Engine
Analysis
Query
Reports
Data mining
Monitor
&
Integrator
Metadata
Data Sources Front-End Tools
Serve
Data Marts
Operational
DBs
Other
sources
Data Storage
OLAP Server

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Three Data Warehouse Models
 Enterprise warehouse
 collects all of the information about subjects spanning
the entire organization
 Data Mart
 a subset of corporate-wide data that is of value to a
specific groups of users. Its scope is confined to
specific, selected groups, such as marketing data mart
 Independent vs. dependent (directly from warehouse) data mart
 Virtual warehouse
 A set of views over operational databases
 Only some of the possible summary views may be
materialized

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Extraction, Transformation, and Loading (ETL)
 Data extraction
 get data from multiple, heterogeneous, and external
sources
 Data cleaning
 detect errors in the data and rectify them when possible
 Data transformation
 convert data from legacy or host format to warehouse
format
 Load
 sort, summarize, consolidate, compute views, check
integrity, and build indicies and partitions
 Refresh
 propagate the updates from the data sources to the
warehouse

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Metadata Repository
 Meta data is the data defining warehouse objects. It stores:
 Description of the structure of the data warehouse
 schema, view, dimensions, hierarchies, derived data defn, data
mart locations and contents
 Operational meta-data
 data lineage (history of migrated data and transformation path),
currency of data (active, archived, or purged), monitoring
information (warehouse usage statistics, error reports, audit trails)
 The algorithms used for summarization
 The mapping from operational environment to the data warehouse
 Data related to system performance
 warehouse schema, view and derived data definitions
 Business data
 business terms and definitions, ownership of data, charging policies

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Induction
 Summary

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From Tables and Spreadsheets to
Data Cubes
 A data warehouse is based on a multidimensional data model
which views data in the form of a data cube
 A data cube, such as sales, allows data to be modeled and viewed in
multiple dimensions
 Dimension tables, such as item (item_name, brand, type), or
time(day, week, month, quarter, year)
 Fact table contains measures (such as dollars_sold) and keys
to each of the related dimension tables
 In data warehousing literature, an n-D base cube is called a base
cuboid. The top most 0-D cuboid, which holds the highest-level of
summarization, is called the apex cuboid. The lattice of cuboids
forms a data cube.

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Cube: A Lattice of Cuboids
time,item
time,item,location
time, item, location, supplier
all
time item location supplier
time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,supplier
time,location,supplier
item,location,supplier
0-D (apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D (base) cuboid

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Conceptual Modeling of Data Warehouses
 Modeling data warehouses: dimensions & measures
 Star schema: A fact table in the middle connected to a
set of dimension tables
 Snowflake schema: A refinement of star schema
where some dimensional hierarchy is normalized into a
set of smaller dimension tables, forming a shape
similar to snowflake
 Fact constellations: Multiple fact tables share
dimension tables, viewed as a collection of stars,
therefore called galaxy schema or fact constellation

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Example of Star Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
state_or_province
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch

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Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
item
branch_key
branch_name
branch_type
branch
supplier_key
supplier_type
supplier
city_key
city
state_or_province
country
city

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Example of Fact Constellation
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
province_or_state
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_key
shipper_name
location_key
shipper_type
shipper

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A Concept Hierarchy:
Dimension (location)
all
Europe North_America
Mexico
Canada
Spain
Germany
Vancouver
M. Wind
L. Chan
...
...
...
... ...
...
all
region
office
country
Toronto
Frankfurt
city

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Data Cube Measures: Three Categories
 Distributive: if the result derived by applying the function
to n aggregate values is the same as that derived by
applying the function on all the data without partitioning
 E.g., count(), sum(), min(), max()
 Algebraic: if it can be computed by an algebraic function
with M arguments (where M is a bounded integer), each of
which is obtained by applying a distributive aggregate
function
 E.g., avg(), min_N(), standard_deviation()
 Holistic: if there is no constant bound on the storage size
needed to describe a subaggregate.
 E.g., median(), mode(), rank()

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View of Warehouses and Hierarchies
Specification of hierarchies
 Schema hierarchy
day < {month <
quarter; week} < year
 Set_grouping hierarchy
{1..10} < inexpensive

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Multidimensional Data
 Sales volume as a function of product, month,
and region
Product
Month
Dimensions: Product, Location, Time
Hierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day

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A Sample Data Cube
Total annual sales
of TVs in U.S.A.
Date
Country
sum
sum
TV
VCR
PC
1Qtr 2Qtr 3Qtr 4Qtr
U.S.A
Canada
Mexico
sum

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Cuboids Corresponding to the Cube
all
product date country
product,date product,country date, country
product, date, country
0-D (apex) cuboid
1-D cuboids
2-D cuboids
3-D (base) cuboid

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Typical OLAP Operations
 Roll up (drill-up): summarize data
 by climbing up hierarchy or by dimension reduction
 Drill down (roll down): reverse of roll-up
 from higher level summary to lower level summary or
detailed data, or introducing new dimensions
 Slice and dice: project and select
 Pivot (rotate):
 reorient the cube, visualization, 3D to series of 2D planes
 Other operations
 drill across: involving (across) more than one fact table
 drill through: through the bottom level of the cube to its
back-end relational tables (using SQL)

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Fig. 3.10 Typical OLAP
Operations

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A Star-Net Query Model
Shipping Method
AIR-EXPRESS
TRUCK
ORDER
Customer Orders
CONTRACTS
Customer
Product
PRODUCT GROUP
PRODUCT LINE
PRODUCT ITEM
SALES PERSON
DISTRICT
DIVISION
Organization
Promotion
CITY
COUNTRY
REGION
Location
DAILY
QTRLY
ANNUALY
Time
Each circle is
called a footprint

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Browsing a Data Cube
 Visualization
 OLAP capabilities
 Interactive manipulation

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Induction
 Summary

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Design of Data Warehouse: A Business
Analysis Framework
 Four views regarding the design of a data warehouse
 Top-down view
 allows selection of the relevant information necessary for the
data warehouse
 Data source view
 exposes the information being captured, stored, and
managed by operational systems
 Data warehouse view
 consists of fact tables and dimension tables
 Business query view
 sees the perspectives of data in the warehouse from the view
of end-user

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Data Warehouse Design Process
 Top-down, bottom-up approaches or a combination of both
 Top-down: Starts with overall design and planning (mature)
 Bottom-up: Starts with experiments and prototypes (rapid)
 From software engineering point of view
 Waterfall: structured and systematic analysis at each step before
proceeding to the next
 Spiral: rapid generation of increasingly functional systems, short
turn around time, quick turn around
 Typical data warehouse design process
 Choose a business process to model, e.g., orders, invoices, etc.
 Choose the grain (atomic level of data) of the business process
 Choose the dimensions that will apply to each fact table record
 Choose the measure that will populate each fact table record

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Data Warehouse Development:
A Recommended Approach
Define a high-level corporate data model
Data
Mart
Data
Mart
Distributed
Data Marts
Multi-Tier Data
Warehouse
Enterprise
Data
Warehouse
Model refinement
Model refinement

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Data Warehouse Usage
 Three kinds of data warehouse applications
 Information processing
 supports querying, basic statistical analysis, and reporting
using crosstabs, tables, charts and graphs
 Analytical processing
 multidimensional analysis of data warehouse data
 supports basic OLAP operations, slice-dice, drilling, pivoting
 Data mining
 knowledge discovery from hidden patterns
 supports associations, constructing analytical models,
performing classification and prediction, and presenting the
mining results using visualization tools

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From On-Line Analytical Processing (OLAP)
to On Line Analytical Mining (OLAM)
 Why online analytical mining?
 High quality of data in data warehouses
 DW contains integrated, consistent, cleaned data
 Available information processing structure surrounding
data warehouses
 ODBC, OLEDB, Web accessing, service facilities,
reporting and OLAP tools
 OLAP-based exploratory data analysis
 Mining with drilling, dicing, pivoting, etc.
 On-line selection of data mining functions
 Integration and swapping of multiple mining
functions, algorithms, and tasks

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Induction
 Summary

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Efficient Data Cube Computation
 Data cube can be viewed as a lattice of cuboids
 The bottom-most cuboid is the base cuboid
 The top-most cuboid (apex) contains only one cell
 How many cuboids in an n-dimensional cube with L
levels?
 Materialization of data cube
 Materialize every (cuboid) (full materialization),
none (no materialization), or some (partial
materialization)
 Selection of which cuboids to materialize
 Based on size, sharing, access frequency, etc.
)
1
1
( 



n
i
i
L
T

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The “Compute Cube” Operator
 Cube definition and computation in DMQL
define cube sales [item, city, year]: sum (sales_in_dollars)
compute cube sales
 Transform it into a SQL-like language (with a new operator cube
by, introduced by Gray et al.’96)
SELECT item, city, year, SUM (amount)
FROM SALES
CUBE BY item, city, year
 Need compute the following Group-Bys
(date, product, customer),
(date,product),(date, customer), (product, customer),
(date), (product), (customer)
()
(item)
(city)
()
(year)
(city, item) (city, year) (item, year)
(city, item, year)

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Indexing OLAP Data: Bitmap Index
 Index on a particular column
 Each value in the column has a bit vector: bit-op is fast
 The length of the bit vector: # of records in the base table
 The i-th bit is set if the i-th row of the base table has the value for
the indexed column
 not suitable for high cardinality domains
 A recent bit compression technique, Word-Aligned Hybrid (WAH),
makes it work for high cardinality domain as well [Wu, et al. TODS’06]
Cust Region Type
C1 Asia Retail
C2 Europe Dealer
C3 Asia Dealer
C4 America Retail
C5 Europe Dealer
RecID Retail Dealer
1 1 0
2 0 1
3 0 1
4 1 0
5 0 1
RecIDAsia Europe America
1 1 0 0
2 0 1 0
3 1 0 0
4 0 0 1
5 0 1 0
Base table Index on Region Index on Type

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Indexing OLAP Data: Join Indices
 Join index: JI(R-id, S-id) where R (R-id, …)  S
(S-id, …)
 Traditional indices map the values to a list of
record ids
 It materializes relational join in JI file and
speeds up relational join
 In data warehouses, join index relates the values
of the dimensions of a start schema to rows in
the fact table.
 E.g. fact table: Sales and two dimensions city
and product
 A join index on city maintains for each
distinct city a list of R-IDs of the tuples
recording the Sales in the city
 Join indices can span multiple dimensions

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Efficient Processing OLAP Queries
 Determine which operations should be performed on the available cuboids
 Transform drill, roll, etc. into corresponding SQL and/or OLAP operations,
e.g., dice = selection + projection
 Determine which materialized cuboid(s) should be selected for OLAP op.
 Let the query to be processed be on {brand, province_or_state} with the
condition “year = 2004”, and there are 4 materialized cuboids available:
1) {year, item_name, city}
2) {year, brand, country}
3) {year, brand, province_or_state}
4) {item_name, province_or_state} where year = 2004
Which should be selected to process the query?
 Explore indexing structures and compressed vs. dense array structs in MOLAP

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OLAP Server Architectures
 Relational OLAP (ROLAP)
 Use relational or extended-relational DBMS to store and manage
warehouse data and OLAP middle ware
 Include optimization of DBMS backend, implementation of
aggregation navigation logic, and additional tools and services
 Greater scalability
 Multidimensional OLAP (MOLAP)
 Sparse array-based multidimensional storage engine
 Fast indexing to pre-computed summarized data
 Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer)
 Flexibility, e.g., low level: relational, high-level: array
 Specialized SQL servers (e.g., Redbricks)
 Specialized support for SQL queries over star/snowflake schemas

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Induction
 Summary

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Attribute-Oriented Induction
 Proposed in 1989 (KDD ‘89 workshop)
 Not confined to categorical data nor particular measures
 How it is done?
 Collect the task-relevant data (initial relation) using a
relational database query
 Perform generalization by attribute removal or
attribute generalization
 Apply aggregation by merging identical, generalized
tuples and accumulating their respective counts
 Interaction with users for knowledge presentation

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Attribute-Oriented Induction: An Example
Example: Describe general characteristics of graduate
students in the University database
 Step 1. Fetch relevant set of data using an SQL
statement, e.g.,
Select * (i.e., name, gender, major, birth_place,
birth_date, residence, phone#, gpa)
from student
where student_status in {“Msc”, “MBA”, “PhD” }
 Step 2. Perform attribute-oriented induction
 Step 3. Present results in generalized relation, cross-tab,
or rule forms

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Class Characterization: An Example
Name Gender Major Birth-Place Birth_date Residence Phone # GPA
Jim
Woodman
M CS Vancouver,BC,
Canada
8-12-76 3511 Main St.,
Richmond
687-4598 3.67
Scott
Lachance
M CS Montreal, Que,
Canada
28-7-75 345 1st Ave.,
Richmond
253-9106 3.70
Laura Lee
…
F
…
Physics
…
Seattle, WA, USA
…
25-8-70
…
125 Austin Ave.,
Burnaby
…
420-5232
…
3.83
…
Removed Retained Sci,Eng,
Bus
Country Age range City Removed Excl,
VG,..
Gender Major Birth_region Age_range Residence GPA Count
M Science Canada 20-25 Richmond Very-good 16
F Science Foreign 25-30 Burnaby Excellent 22
… … … … … … …
Birth_Region
Gender
Canada Foreign Total
M 16 14 30
F 10 22 32
Total 26 36 62
Prime
Generalized
Relation
Initial
Relation

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Basic Principles of Attribute-Oriented Induction
 Data focusing: task-relevant data, including dimensions,
and the result is the initial relation
 Attribute-removal: remove attribute A if there is a large set
of distinct values for A but (1) there is no generalization
operator on A, or (2) A’s higher level concepts are
expressed in terms of other attributes
 Attribute-generalization: If there is a large set of distinct
values for A, and there exists a set of generalization
operators on A, then select an operator and generalize A
 Attribute-threshold control: typical 2-8, specified/default
 Generalized relation threshold control: control the final
relation/rule size

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Attribute-Oriented Induction: Basic Algorithm
 InitialRel: Query processing of task-relevant data, deriving
the initial relation.
 PreGen: Based on the analysis of the number of distinct
values in each attribute, determine generalization plan for
each attribute: removal? or how high to generalize?
 PrimeGen: Based on the PreGen plan, perform
generalization to the right level to derive a “prime
generalized relation”, accumulating the counts.
 Presentation: User interaction: (1) adjust levels by drilling,
(2) pivoting, (3) mapping into rules, cross tabs,
visualization presentations.

50
Presentation of Generalized Results
 Generalized relation:
 Relations where some or all attributes are generalized, with counts
or other aggregation values accumulated.
 Cross tabulation:
 Mapping results into cross tabulation form (similar to contingency
tables).
 Visualization techniques:
 Pie charts, bar charts, curves, cubes, and other visual forms.
 Quantitative characteristic rules:
 Mapping generalized result into characteristic rules with quantitative
information associated with it, e.g.,
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47
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x
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
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51
Mining Class Comparisons
 Comparison: Comparing two or more classes
 Method:
 Partition the set of relevant data into the target class and the
contrasting class(es)
 Generalize both classes to the same high level concepts
 Compare tuples with the same high level descriptions
 Present for every tuple its description and two measures
 support - distribution within single class
 comparison - distribution between classes
 Highlight the tuples with strong discriminant features
 Relevance Analysis:
 Find attributes (features) which best distinguish different classes

52
Concept Description vs. Cube-Based OLAP
 Similarity:
 Data generalization
 Presentation of data summarization at multiple levels of
abstraction
 Interactive drilling, pivoting, slicing and dicing
 Differences:
 OLAP has systematic preprocessing, query independent,
and can drill down to rather low level
 AOI has automated desired level allocation, and may
perform dimension relevance analysis/ranking when
there are many relevant dimensions
 AOI works on the data which are not in relational forms

53
Induction
 Summary

54
Summary
 Data warehousing: A multi-dimensional model of a data warehouse
 A data cube consists of dimensions & measures
 Star schema, snowflake schema, fact constellations
 OLAP operations: drilling, rolling, slicing, dicing and pivoting
 Data Warehouse Architecture, Design, and Usage
 Multi-tiered architecture
 Business analysis design framework
 Information processing, analytical processing, data mining, OLAM (Online
Analytical Mining)
 Implementation: Efficient computation of data cubes
 Partial vs. full vs. no materialization
 Indexing OALP data: Bitmap index and join index
 OLAP query processing
 OLAP servers: ROLAP, MOLAP, HOLAP
 Data generalization: Attribute-oriented induction

55
References (I)
 S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S.
Sarawagi. On the computation of multidimensional aggregates. VLDB’96
 D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data
warehouses. SIGMOD’97
 R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDE’97
 S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM
SIGMOD Record, 26:65-74, 1997
 E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July
1993.
 J. Gray, et al. Data cube: A relational aggregation operator generalizing group-by, cross-tab
and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997.
 A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and
Applications. MIT Press, 1999.
 J. Han. Towards on-line analytical mining in large databases. ACM SIGMOD Record, 27:97-107,
1998.
 V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently.
SIGMOD’96
 J. Hellerstein, P. Haas, and H. Wang. Online aggregation. SIGMOD'97

56
References (II)
 C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design: Relational and
Dimensional Techniques. John Wiley, 2003
 W. H. Inmon. Building the Data Warehouse. John Wiley, 1996
 R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional
Modeling. 2ed. John Wiley, 2002
 P. O’Neil and G. Graefe. Multi-table joins through bitmapped join indices. SIGMOD Record, 24:8–
11, Sept. 1995.
 P. O'Neil and D. Quass. Improved query performance with variant indexes. SIGMOD'97
 Microsoft. OLEDB for OLAP programmer's reference version 1.0. In
http://www.microsoft.com/data/oledb/olap, 1998
 S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. ICDE'94
 A. Shoshani. OLAP and statistical databases: Similarities and differences. PODS’00.
 D. Srivastava, S. Dar, H. V. Jagadish, and A. V. Levy. Answering queries with aggregation using
views. VLDB'96
 P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987.
 J. Widom. Research problems in data warehousing. CIKM’95
 K. Wu, E. Otoo, and A. Shoshani, Optimal Bitmap Indices with Efficient Compression, ACM Trans.
on Database Systems (TODS), 31(1): 1-38, 2006

58
Compression of Bitmap Indices
 Bitmap indexes must be compressed to reduce I/O costs
and minimize CPU usage—majority of the bits are 0’s
 Two compression schemes:
 Byte-aligned Bitmap Code (BBC)
 Word-Aligned Hybrid (WAH) code
 Time and space required to operate on compressed
bitmap is proportional to the total size of the bitmap
 Optimal on attributes of low cardinality as well as those of
high cardinality.
 WAH out performs BBC by about a factor of two

Chapter 4. Data Warehousing and On-Line Analytical Processing.ppt

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