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Data Warehousing: and OLAP BIS 541 Chapter 2

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Title: Data Warehousing: and OLAP BIS 541 Chapter 2


1
Data Warehousing and OLAP BIS 541 Chapter
2
  • Summer
  • 2005

2
Chapter 2 Data Warehousing and OLAP Technology
for Data Mining
  • What is a data warehouse?
  • A multi-dimensional data model
  • Data warehouse architecture
  • Data warehouse implementation
  • Further development of data cube technology
  • From data warehousing to data mining

3
What is Data Warehouse?
  • Defined in many different ways, but not
    rigorously.
  • A decision support database that is maintained
    separately from the organizations 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 managements
    decision-making process.W. H. Inmon
  • Data warehousing
  • The process of constructing and using data
    warehouses

4
Data WarehouseSubject-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.

5
Data WarehouseIntegrated
  • 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.

6
Data WarehouseTime 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.

7
Data WarehouseNon-Volatile
  • 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.

8
Data Warehouse vs. Heterogeneous DBMS
  • Traditional heterogeneous DB integration
  • Build wrappers/mediators on top of heterogeneous
    databases
  • Query driven approach
  • When a query is posed to a client site, a
    meta-dictionary is used to translate the query
    into queries appropriate for individual
    heterogeneous sites involved, and the results are
    integrated into a global answer set
  • Complex information filtering, compete for
    resources
  • Data warehouse update-driven, high performance
  • Information from heterogeneous sources is
    integrated in advance and stored in warehouses
    for direct query and analysis

9
Data Warehouse vs. Operational DBMS
  • OLTP (on-line transaction processing)
  • Major task of traditional relational DBMS
  • Day-to-day operations purchasing, inventory,
    banking, manufacturing, payroll, registration,
    accounting, etc.
  • OLAP (on-line analytical processing)
  • Major task of data warehouse system
  • Data analysis and decision making
  • Distinct features (OLTP vs. OLAP)
  • User and system orientation customer vs. market
  • Data contents current, detailed vs. historical,
    consolidated
  • Database design ER application vs. star
    subject
  • View current, local vs. evolutionary, integrated
  • Access patterns update vs. read-only but complex
    queries

10
OLTP vs. OLAP
11
Why Separate Data Warehouse?
  • High performance for both systems
  • DBMS tuned for OLTP access methods, indexing,
    concurrency control, recovery
  • Warehousetuned 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

12
Chapter 2 Data Warehousing and OLAP Technology
for Data Mining
  • What is a data warehouse?
  • A multi-dimensional data model
  • Data warehouse architecture
  • Data warehouse implementation
  • Further development of data cube technology
  • From data warehousing to data mining

13
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
  • Dimensions
  • A sale data warehouse with respect to dimension
  • time, item, branch, location
  • Dimension tables, such as
  • item (item_name, brand, type), or
  • time(day, week, month, quarter, year)
  • Facts numerical measures
  • dollars_sold sales amount in dollars
  • units_sold number of units sold
  • Fact table contains measures (such as
    dollars_sold) and keys to each of the related
    dimension tables

14
A 2-D data cube A table or spreadsheet for
sales from AllElectronics
AllElectronics sales data for items sold per
quarter in the city of Istanbul
Time dimension organized in quarters item
dimension by types of items sold The fact or
measure is dollar_sold
15
A three dimensional cube
Dimensions time, item, location for the cities
16
A Data Cube Representation of the same data
  • Sales volume as a function of product, month, and
    region

Dimensions Product, Location, Time Hierarchical
summarization paths
izmir
locations
ankara
istabbul
items
phon
comp
Quarters
Q1
17
A 4-D Cube as a series of 3-D cubes
Dimensions item,time,location,supplier
From Supplier A
From Supplier B
From Supplier C
locations
items
Quarters
18
n-Dimensional Cube
  • Any n-D data as a series of (n-1)-D cubes
  • In data warehousing literature,
  • A data cube is referred to as a cuboid
  • The lattice of cuboids forms a data cube.
  • The cuboid holding the lowest level of
    summarization is called a base cuboid.
  • the 4-D cuboid is the base cuboid for the given
    four dimensions
  • The top most 0-D cuboid, which holds the
    highest-level of summarization, is called the
    apex cuboid.
  • Here this is the total sales, or dollars_sold
    summarized over all four dimensions
  • typically denoted by all

19
Cube A Lattice of Cuboids
all
0-D(apex) cuboid
time
item
location
supplier
1-D cuboids
time,item
time,location
item,location
location,supplier
2-D cuboids
time,supplier
item,supplier
time,location,supplier
time,item,location
3-D cuboids
item,location,supplier
time,item,supplier
4-D(base) cuboid
time, item, location, supplier
20
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

21
Example of Star Schema

Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
22
(No Transcript)
23
Example of Snowflake Schema
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
24
A unnormalized City Table
City province and Country is repeated For every
steet in Istanbul
A Normalized City Table
Unnecessary repitations of province end country
are eliminated Memory gain but complex queries
25
Example of Fact Constellation
Shipping Fact Table
time_key
Sales Fact Table
item_key
time_key
shipper_key
item_key
from_location
branch_key
to_location
location_key
dollars_cost
units_sold
units_shipped
dollars_sold
avg_sales
Measures
26
Example
  • The relational database scheme for AE
  • time(time_key,day,day_of_week,month,quarter,year)
  • item(item_key,item_name,brand,type,supplier_type)
  • branch(branch_key,branch_name,branch_type)
  • location(location_key,street,city,province_or_stat
    e,country)
  • sales(time_key,item_key,branch_key,location_key,nu
    mber_of_units_sold,price)

27
Example cont.
  • Select s.time_key,s.item_key,s.branch_key,s.locati
    on_key,
  • sum(s.number_of_units_solds.price),
  • sum(s.number_of_units_sold)
  • from time t, item i,branch b,location l,sales s,
  • where s.time_keyt.time_key and
  • s.item_keyi.item_key and
  • s.branch_keyb.branch_key and
  • s.location_keyl.location_key
  • group by s.time_key, s.item_key,
    s.branch_key,s.location_key

28
Example Cont.
  • The cube created is the base cuboid of the
    sales_star datacube
  • it contains all of the dimensions
  • granularity of each is at the join key level
  • by changing the group by clauses
  • E.g.,
  • group by t.month sum up the measures of each
    group by month
  • removing group by s.branch_key generate a
    higher-level cuboid
  • recoving all group bys total sum of dollars sold
    and total count of units_sold
  • zero-dimensional cuboid is apex cuboid

29
Time by month
  • Select t.year,t.month,s.item_key,s.branch_key,s.lo
    cation_key,sum(s.number_of_units_solds.price),sum
    (s.number_of_units_sold)
  • from time t, item i,branch b,location l,sales s,
  • where s.time_keyt.time_key and
  • s.item_keyi.item_key and
  • s.branch_keyb.branch_key and
  • s.location_keyl.location_key
  • group by t.year, t.month, s.item_key,
    s.branch_key,s.location_key

30
A three dimensional cuboid
  • Select s.time_key,s.item_key, s.location_key,
  • sum(s.number_of_units_solds.price),
  • sum(s.number_of_units_sold)
  • from time t, item i,branch b,location l,sales s,
  • where s.time_keyt.time_key and
  • s.item_keyi.item_key and
  • s.branch_keyb.branch_key and
  • s.location_keyl.location_key
  • group by s.time_key, s.item_key,s.location_key

31
Concept hierarchies
  • Defines a sequence of mappings from a set of
    low-level concepts to high-level more general
    concepts
  • E.g., dimension location is described by
  • number,street,city,province_or_state,zipcode and
    country
  • are related by a total order, forming a concept
    hierarchy
  • streetltcityltprovince_or_stateltcountry
  • The attributes of a dimension may be organized in
    a partial order, forming a lattice
  • day,week,month,quarter, year
  • dayltmonthltquarter,weekltyear

32
A Concept Hierarchy Dimension (location)
all
all
Europe
North_America
...
region
Mexico
Canada
Spain
Germany
...
...
country
Vancouver
...
...
Toronto
Frankfurt
city
M. Wind
L. Chan
...
office
33
Partially ordered co
  • The attributes of a dimension may be organized in
    a partial order, forming a lattice
  • day,week,month,quarter, year
  • dayltmonthltquarter,weekltyear
  • predefined in the data mining system
  • time
  • fiscal year starting on April 1
  • academic year starting on September 1

34
Multidimensional Data
  • Sales volume as a function of product, month, and
    region

Dimensions Product, Location, Time Hierarchical
summarization paths
Region
Industry Region Year Category
Country Quarter Product City Month
Week Office Day
Product
A partially ordered hierarchy
Month
35
Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
location
item
time
1-D cuboids
item,time
item,location
time, location
2-D cuboids
3-D(base) cuboid
item, time, location
36
Set grouping hierarchy
  • Set-grouping hierarchy
  • discretizing or grouping values for a given
    dimension or attribute
  • Ex price
  • There may be more than one concept hierarchy for
    a given attribute or dimension based on
    different user viewpoints
  • price by defining ranges for
  • inexpensive, moderately_priced,expensive

37
How defined?
  • provided by manually by
  • system users
  • domain experts
  • knowledge engineers or
  • automatically generated based on statistical
    analysis of the data distribution

38
Typical OLAP Operations
  • In the multidimensional model data are organized
    into multiple dimensions
  • each dimension contains multiple levels of
    abstraction defined by concept hierarchies
  • This organization provides users with the
    flexibility to view data from different
    perspectives

39
Example
  • Refer to figure 2.10 in Hans book
  • data cube for AllElls sales
  • dimensions location,time,item
  • location -- city
  • time -- quarters
  • item -- item types
  • measure displayed is dollars-sold

40
Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
location
item
time
1-D cuboids
item,time
item,location
time, location
2-D cuboids
3-D(base) cuboid
item, time, location
41
Roll-up (drill-up)
  • Climbing up a concept hierarchy for a dimension
    or
  • by dimension reduction
  • Exroll-up operation aggregates data by ascending
    the location hierarchy
  • from the level of city
  • to the level of country
  • rather than grouping the data by city,the cubes
  • groups the data by country

42
By a drill up opperation examine sales By country
rather than city level
roll up
43
  • when performed by dimension reduction
  • one or more dimensions are removed from the cube
  • Ex a sales cube with location and time
  • roll-up may remove the time dimension
  • aggregation of total sales by location
  • rather than by location and by time

Two dimensional cuboid
One dim. cuboid
44
Drill-down (roll-down)
  • reverse of roll-up
  • navigates from less detailed data to more
    detailed data
  • from higher level summary to lower level summary
    or detailed data, or
  • stepping down a concept hierarchy for a dimension
  • introducing new dimensions
  • Ex drill-down for time
  • dayltmonthltquarterltyear
  • form the level of quarter
  • to the more detailed level of month
  • Adding a new dimension to the data

45
Drill down
46
Slice and dice
  • Slice a selection on one dimension of the cube
  • resulting in subcube
  • Ex sale data are selected for dimension time
    using time Q1
  • dice defines a subcube by performing a selection
    on two or more dimensions
  • Ex a dice opp. Based on
  • locationtoronto or vencover and
  • time Q1 or Q2 and
  • item home entertainment or computer

47
slice
dice
48
Pivot (rotate)
  • Visualization opp. Rotates the data axes in view
    to provide an alternative presentation of data
  • Exitem and location axes in a 2-D slice are
    rotated
  • or transforming a 3-D cube into a series of 2-D
    planes

49
Other OLAP 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)
  • ranking the top N or bottom N items in lists
  • moving averages
  • growth rates
  • interests

50
A Star-Net Query Model
  • Radial lines from a central point
  • each line represents a concept hierarchy for a
    dimension
  • each abstraction level is called a footprint
  • granularities available for use by OLAP
  • Exfigure 2.11
  • four radial lines,for concept hierarchies
  • location,customer,item,time
  • time line has 4 footprints
  • day,month,quarter,year

51
A Star-Net Query Model
Customer Orders
Shipping Method
Customer

CONTRACTS
AIR-EXPRESS
ORDER
TRUCK
PRODUCT LINE
Product
Time
DAILY
QTRLY
ANNUALY
PRODUCT ITEM
PRODUCT GROUP
CITY
SALES PERSON
COUNTRY
DISTRICT
REGION
DIVISION
Each circle is called a footprint
Location
Organization
Promotion
52
Chapter 2 Data Warehousing and OLAP Technology
for Data Mining
  • What is a data warehouse?
  • A multi-dimensional data model
  • Data warehouse architecture
  • Data warehouse implementation
  • Further development of data cube technology
  • From data warehousing to data mining

53
Design of a 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

54
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

55
Multi-Tiered Architecture
Monitor Integrator
OLAP Server
Metadata
Analysis Query Reports Data mining
Serve
Data Warehouse
Data Marts
Data Sources
OLAP Engine
Front-End Tools
Data Storage
56
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

57
Data Warehouse Development A Recommended Approach
Multi-Tier Data Warehouse
Distributed Data Marts
Enterprise Data Warehouse
Data Mart
Data Mart
Model refinement
Model refinement
Define a high-level corporate data model
58
Storage of the cube
  • Cuboids are referred as aggregations
  • One factor affecting storage requirements
  • Sparsity the amount of empty cells in a cube
  • The base cuboid is likely to contain many empty
    cells
  • it is a spares cube or array
  • the 0 or lower dimensional cuboids are less
    spares than the higher dimensional ones
  • it is not likely that they contain empty cells
  • Moving along higher levels for the dimension
    hierarchy
  • the cuboids becomes less spares or more dense

59
Two dimensional sparse cuboid
One dimensional Densed cuboid
60
OLAP Server Architectures Relational OLAP
(ROLAP)
  • Use relational or extended-relational DBMS to
    store and manage warehouse data and OLAP middle
    ware to support missing pieces
  • query response is generally slower
  • low storage requirement
  • Include optimization of DBMS backend,
    implementation of aggregation navigation logic,
    and additional tools and services
  • greater scalability
  • appropriate for large data sets that are
    infrequently queried
  • historical data from less recent previous years

61
Multidimensional OLAP (MOLAP)
  • Array-based multidimensional storage engine
    (sparse matrix techniques)
  • fast indexing to pre-computed summarized data
  • a two-level storage representation
  • dense subcubes are stored as array structures
  • spars subcubes are stored by compression
    techniques
  • appropriate for cubes with frequent use and rapid
    query response

62
Hybrid OLAP (HOLAP)
  • combines ROLAP and MOLAP benefiting from
  • greater scalability of ROLAP
  • faster computation of MOLAP
  • Large volumes of data base cuboid is stored in a
    relational database
  • aggregations are stored as arrays
  • appropriate for for cubes that requre
  • rapid query response for summaries based on a
    large amount of base data

63
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
  • Example A data cube containing
  • item,city,year as dimensions and
  • sales_in_ as measure
  • typical queries are
  • compute sum of sales, grouping by item and city
  • compute sum of sales, grouping by item
  • compute sum of sales, grouping by city
  • what is the total number of cuboids or group by s
    for this data cube
  • 238

64
Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
country
product
date
1-D cuboids
product,date
product,country
date, country
2-D cuboids
3-D(base) cuboid
product, date, country
65
Efficient Data Cube Computation
  • How many cuboids in an n-dimensional cube with 1
    levels?
  • 2n cuboids including the base cuboid
  • in OLAP compute all or at least some of the
    cuboids in advance
  • fast response time
  • avoids some redundant computation

66
Number of cuboids
  • if the cube has many dimensions with multiple
    level hierarchies
  • T is total number of cuboids
  • Li is the number of levels associated with
    dimension i
  • excluding the top level all
  • as generalizing to all is equivalent to the
    removal of a dimension

67
Materialization of data cube
  • There are three choices for data cube
    materialization given a base cuboid
  • Materialize every (cuboid) (full
    materialization),
  • huge amounts of memory space
  • none (no materialization), or
  • slow processing of queries
  • some (partial materialization)
  • trade-off between storage space and response time
  • Selection of which cuboids to materialize
  • Based on size, sharing, access frequency, etc.
  • A heuristic approach for cuboid selection
  • materialize the set of cuboids on which other
    popularly referenced cuboids are based

68
Processing Cubes
  • Complete load of the cube
  • all dimension and fact table data is read and
  • all specified aggregations cuboids are calculated
  • process a cube when
  • its structure is new or
  • its dimensions or measures have been edited
  • Incrementally updating a cube
  • new data is added but existing data not changed
    and cube structure si the same
  • Refreshing
  • data cleared and reloaded
  • its aggregations recalculated
  • faster then processingno design of aggregation
    tables

69
Calculated Members
  • Dimension member or measure whose value is
    computed at run time using an expression
  • Only the definitions are stored but values exists
    only in memory upon a query
  • do not increase in cube size
  • Ex if sales and cost are included in the base
    fact table
  • a profit measure can be a calculated member
  • profit sales cost
  • Average_sales sales/_items_sold

70
Virtual cubes
  • Combination of multiple cubes in one logical cube
  • can be based on a single cube to expose only
    selected subsets of measures and dimensions
  • Require no physical space
  • store only the dimensions information not actual
    data
  • provide a valuable security functiton
  • limiting the access of some users

71
Member Properties
  • Attribute of a dimension member
  • provides additional information about the member
  • a column in the same dimension table as the
    associated members
  • used in queries
  • provide users more options when analysing cube
    data

72
Example time table
  • A typical time table
  • (time_id,day,month,quarter,year,business
    day,leap,day of the week)
  • dimension levels dayltmonthltquarterltyear
  • member properties for day
  • weekend or business day0 or 1
  • day of the week1,2,3,...,7
  • a member property for year is
  • whether it is leap year or not0 or 1

73
Virtual Dimensions
  • Logical dimension based on a member property of a
    level in a physical dimension
  • enables users to analyze cube data based on the
    member properties of dimension levels
  • add a virtual dimension to a cube only if
  • the dimension that supplies its member property
    is also included in the cube
  • adding a virtual dimension does not increase cube
    size
  • not affect cube processing time
  • calculated in memory when needed
  • query processing time is slower

74
Example
  • The business day column was a member property for
    day level of the time dimension
  • the user may want to investigate sales by
  • type of day (business or weekend)
  • makes business day member property as a virtual
    dimension of the sale cube

75
Parent Child Dimensions
  • Based on two dimension table columns that
    together define the lieage relationships among
    the members of the dimension
  • Member key columnidentifies each member
  • Parent key column identifies the parent of each
    member

76
Example A HR dimension
Poal West
James Smith
Jill Kelly
Amy Joens
John Grande
Jo Brown
77
A Concept Hierarchy Dimension (location)
all
all
Europe
North_America
...
region
Mexico
Canada
Spain
Germany
...
...
country
Vancouver
...
...
Toronto
Frankfurt
city
M. Wind
L. Chan
...
office
78
Example Parent Child Dimension
  • Emp._Name Emp_Number Man_Emp_Num
  • James Smith 1 3
  • Amy Jones 2 3
  • Paul West 3 3
  • Jill Kelly 4 3
  • John Grande 5 1
  • Jo Brown 6 1
  • Emp_Num identifies each member
  • Man_Emp_Nun identifies the parent of each member

79
Exercise
  • How do you represent the same organizational
    chart by treditional concept hyerarchies?

80
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.
  • Differences among the three tasks

81
From On-Line Analytical Processing 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.
  • Architecture of OLAM

82
An OLAM Architecture
Layer4 User Interface
Mining query
Mining result
User GUI API
OLAM Engine
OLAP Engine
Layer3 OLAP/OLAM
Data Cube API
Layer2 MDDB
MDDB
Meta Data
Database API
FilteringIntegration
Filtering
Layer1 Data Repository
Data Warehouse
Data cleaning
Databases
Data integration
83
Summary
  • Data warehouse
  • A subject-oriented, integrated, time-variant, and
    nonvolatile collection of data in support of
    managements decision-making process
  • A multi-dimensional model of a data warehouse
  • Star schema, snowflake schema, fact
    constellations
  • A data cube consists of dimensions measures
  • OLAP operations drilling, rolling, slicing,
    dicing and pivoting
  • OLAP servers ROLAP, MOLAP, HOLAP
  • Efficient computation of data cubes
  • Partial vs. full vs. no materialization
  • Multiway array aggregation
  • Bitmap index and join index implementations
  • Further development of data cube technology
  • Discovery-drive and multi-feature cubes
  • From OLAP to OLAM (on-line analytical mining)

84
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. In Proc. 1996 Int. Conf. Very Large
    Data Bases, 506-521, Bombay, India, Sept. 1996.
  • D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek.
    Efficient view maintenance in data warehouses.
    In Proc. 1997 ACM-SIGMOD Int. Conf. Management of
    Data, 417-427, Tucson, Arizona, May 1997.
  • R. Agrawal, J. Gehrke, D. Gunopulos, and P.
    Raghavan. Automatic subspace clustering of high
    dimensional data for data mining applications. In
    Proc. 1998 ACM-SIGMOD Int. Conf. Management of
    Data, 94-105, Seattle, Washington, June 1998.
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    multidimensional databases. In Proc. 1997 Int.
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    http//www.olapcouncil.org/research/apily.htm,
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References (II)
  • V. Harinarayan, A. Rajaraman, and J. D. Ullman.
    Implementing data cubes efficiently. In Proc.
    1996 ACM-SIGMOD Int. Conf. Management of Data,
    pages 205-216, Montreal, Canada, June 1996.
  • Microsoft. OLEDB for OLAP programmer's reference
    version 1.0. In http//www.microsoft.com/data/oled
    b/olap, 1998.
  • K. Ross and D. Srivastava. Fast computation of
    sparse datacubes. In Proc. 1997 Int. Conf. Very
    Large Data Bases, 116-125, Athens, Greece, Aug.
    1997.
  • K. A. Ross, D. Srivastava, and D. Chatziantoniou.
    Complex aggregation at multiple granularities.
    In Proc. Int. Conf. of Extending Database
    Technology (EDBT'98), 263-277, Valencia, Spain,
    March 1998.
  • S. Sarawagi, R. Agrawal, and N. Megiddo.
    Discovery-driven exploration of OLAP data cubes.
    In Proc. Int. Conf. of Extending Database
    Technology (EDBT'98), pages 168-182, Valencia,
    Spain, March 1998.
  • E. Thomsen. OLAP Solutions Building
    Multidimensional Information Systems. John Wiley
    Sons, 1997.
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    array-based algorithm for simultaneous
    multidimensional aggregates. In Proc. 1997
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