1. Data Warehousing and OLAP
CENG 514
May 18, 2018

2. Data Analysis Needs
Operational databases served as the source of information to facilitate the decision making process
Decision support systems (DSS) were developed around this data
Information requirements have grown quite complex over time and it is difficult to extract all the needed information from a database
May 18, 2018

3. Decision Support Systems
Methodology (or series of methodologies) designed to extract information from data and to use such information as a basis for decision making
Decision support system (DSS):
Arrangement of computerized tools used to assist managerial decision making within a business
Used at all levels within an organization
Often tailored to focus on specific business areas
Provides ad hoc query tools to retrieve data and to display data in different formats
May 18, 2018

4. Decision Support Systems
Combines historical operational data with business models that reflect the business activities
Compare the relative rates of productivity growth by company division over some specified period of time
Define the relationship between advertising types and sales levels
Define relative market shares by selected product lines
May 18, 2018

5. What is Data Warehouse?
“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
May 18, 2018

6. 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.
May 18, 2018

7. 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.
May 18, 2018

8. 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”.
May 18, 2018

9. Data Warehouse—Non-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.
May 18, 2018

10. 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
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11. OLTP vs. OLAP
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12. Data Model: 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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13. 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
time,item,supplier
item,location,supplier
4-D(base) cuboid
May 18, 2018
time, item, location, supplier

14. 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
May 18, 2018

15. Star Schemas
Data modeling technique used to map multidimensional decision support data into a relational database
Creates the near equivalent of a multidimensional database schema from the existing relational database
The schema was developed because existing relational modeling techniques, ER and normalization did not yield a database structure that served advanced data analysis requirements well
Yield an easily implemented model for multidimensional data analysis, while still preserving the relational structures on which the operational database is built
Has four components: facts, dimensions, attributes, and attribute hierarchies
May 18, 2018

16. Star Schemas
Facts: numeric values that represent a specific business aspect or activity (sales figures).
The fact table contains facts that are linked through their dimensions
Metrics are facts computed or derived at run time
Dimensions: qualifying characteristics that provide additional perspectives to a fact (sales have product, location and time dimensions)
Dimensions are stored in a dimension table
The Data Warehouse

17. Star Schema Representation
Facts and dimensions are normally represented by physical tables in the data warehouse database
The fact table is related to each dimension table in a many to one relationship
Many fact rows are related to each dimension row – each product appears many times in the sales fact table
Fact and dimension tables are related by foreign keys and are subject to the familiar PK/FK constraints
Because the fact table is related to many dimension tables, the PK of the fact table is a composite PK
May 18, 2018

18. Example of Star Schema item branch time Sales Fact Table time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_type
item
Sales Fact Table
time_key
item_key
branch_key
branch_key
branch_name
branch_type
branch
location_key
street
city
state_or_province
country
location
location_key
units_sold
dollars_sold
avg_sales
Measures
May 18, 2018

19. Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_key
item
supplier_key
supplier_type
supplier
Sales Fact Table
time_key
item_key
branch_key
location_key
street
city_key
location
branch_key
branch_name
branch_type
branch
location_key
units_sold
city_key
city
state_or_province
country
dollars_sold
avg_sales
Measures
May 18, 2018

20. Example of Fact Constellation
time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_type
item
Shipping Fact Table
Sales Fact Table
time_key
item_key
time_key
shipper_key
item_key
from_location
branch_key
branch_key
branch_name
branch_type
branch
location_key
to_location
location_key
street
city
province_or_state
country
location
dollars_cost
units_sold
units_shipped
dollars_sold
avg_sales
shipper_key
shipper_name
location_key
shipper_type
shipper
Measures
May 18, 2018

21. 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().
May 18, 2018

22. A Concept Hierarchy: Dimension (location)
all
all
Europe
...
North_America
region
Germany
...
Spain
Canada
...
Mexico
country
Vancouver
...
city
Frankfurt
...
Toronto
L. Chan
...
M. Wind
office
May 18, 2018

23. 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
Month
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24. 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
May 18, 2018

25. 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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26. May 18, 2018

27. 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
May 18, 2018

28. Multi-Tiered Architecture
Operational
DBs
other
sources
Monitor
&
Integrator
OLAP Server
Metadata
Extract
Transform
Load
Refresh
Analysis
Query
Reports
Data mining
Serve
Data
Warehouse
Data Marts
Data Sources
Data Storage
OLAP Engine
Front-End Tools
May 18, 2018

29. 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
May 18, 2018

30. 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
Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services
greater scalability
Multidimensional OLAP (MOLAP)
Array-based multidimensional storage engine (sparse matrix techniques)
fast indexing to pre-computed summarized data
Hybrid OLAP (HOLAP)
User flexibility, e.g., low level: relational, high-level: array
Specialized SQL servers
specialized support for SQL queries over star/snowflake schemas
May 18, 2018

31. Relational OLAP Builds on existing relational technologies
Adds the following extensions to RDBMS
Multidimensional data schema support within the RDBMS
Star schema to enable RDMS (normalized data) to support multidimensional data representations (nonnormalized, aggregated and duplicated)
Data access language and query performance are optimized for multidimensional data
ROLAP extends SQL so that it can differentiate between access requirements for data warehouse data and operational data
Support for VLDBs
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32. Typical ROLAP Client/Server Architecture
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33. Multidimensional OLAP
MOLAP extends OLAP functionality to multidimensional DBMSs (MDBMSs)
An MDBMS stores data in matrix-like n-dimensional arrays
MDBMS end users visualize the stored data as a three-dimensional cube known as a data cube
They data cubes can grow to n-dimensions becoming hypercubes
Data cubes are created by extracting data from the operational databases or the data warehouse
They are pre-created and static and queried based on their dimensions e.g., product, location and time for a cube for sales
To speed data access they are held in memory – cube cache
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34. MOLAP Client/Server Architecture
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35. MDBMS
Because a cube is pre-defined, the addition of a new dimension requires that the entire data cube be recreated – a time consuming process
If this needs to be done often, the MDBMS loses some of its speed advantage over the RDBMS
MDBMS is best suited for small and medium data sets
Scalability is limited due to the restrictions on the size of the data cube to avoid lengthy data access times caused by having less memory available for the OS and application programs
Employ proprietary data storage techniques that require proprietary data access methods using a multidimensional query language
Most handle sparsity of the data cubes effectively to reduce processing overhead and resource requirements
May 18, 2018

36. Relational vs. Multidimensional OLAP
May 18, 2018

37. 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.
May 18, 2018

38. Materialized View
“ Materialized views contains aggregate data (cuboids) derived from a fact table in order to minimize the query response time “
There are 3 kinds of materialization
(Given a base cuboid )
1. No Materialization
Precompute only the base cuboid
“ Slow response time ”
2. Full Materialization
Precompute all of the cuboids
“ Large storage space “
3. Partial Materialization
Selectively compute a subset of the cuboids
“ Mix of the above “
May 18, 2018

39. Cube Computation: ROLAP-Based Method
ROLAP-based cubing algorithms
Sorting, hashing, and grouping operations are applied to the dimension attributes in order to reorder and cluster related tuples
Grouping is performed on some sub-aggregates as a “partial grouping step”
Aggregates may be computed from previously computed aggregates, rather than from the base fact table
May 18, 2018

40. Multi-way Array Aggregation for Cube Computation
Partition arrays into chunks (a small subcube which fits in memory).
Compressed sparse array addressing: (chunk_id, offset)
Compute aggregates in “multiway” by visiting cube cells in the order which minimizes the # of times to visit each cell, and reduces memory access and storage cost. A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1
c 0 b3 b2 b1 b0 a2 a3 C 44 28 56 40 24 52 36 20 60
What is the best traversing order to do multi-way aggregation?
May 18, 2018

41. 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
Base table
Index on Region
Index on Type
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42. Bitmap Indexing Another Example Dimensions Item city Where:
H=Home entertainment, C=Computer
P=Phone, S=Security
V=Vancouver, T=Toronto
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43. 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 — a rather costly operation
In data warehouses, join index relates the values of the dimensions of a star 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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44. Join Indexing
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45. Efficient Processing of 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 to which materialized cuboid(s) the relevant operations should be applied.
Exploring indexing structures and compressed vs. dense array structures in MOLAP
May 18, 2018

46. Consider a data cube for “Best Electronics” of the form
“sales [time, item, location]:sum(sales_in_dollars)
Dimension hierarchies used are :
“ day<month<quarter<year ” for time
“ item_name<brand<type” for item
“ street<city<province_or_state<country “ for location
Query :{ brand,province_or_state} with year = 2000
Materialized cuboids available are
Cuboid 1: { item_name,city,year}
Cuboid 2: {brand,country,year}
Cuboid 3: {brand,province_or_state,year}
Cuboid 4: {item_name,province_or_state} where year=2000
May 18, 2018

47. Here country is more general concept than province_or_state
“ Which of the above four cuboids should be selected to process the query ? “
Cuboid 2
It cannot be used
Since finer granularity data cannot be generated from coarser granularity data
Here country is more general concept than province_or_state
Cuboid 1,3,4
Can be used
They have the same set or a superset of the dimensions in the query
The selection clause in the query can imply the selection in the cuboid
The abstraction levels for the item and location dimensions are at a finer level than brand and province_or_state respectively
May 18, 2018

48. How would the cost of each cuboid compare if used to process the query
Will cost more
Since both item_name and city are at a lower level than brand and province_or_state specified in the query
Cuboid 3 :
Will cost least
If there are not many year values associated with items in the cube but there are several item_names for each brand
Cuboid 3 will be smaller than cuboid 4
Cuboid 4 :
Will cost least
If efficient indices are available
Hence some cost based estimation is required in order to decide which set of cuboids must be selected for query processing
May 18, 2018

49. Metadata Repository
Meta data is the data defining warehouse objects. It has the following kinds
Description of the structure of the 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
May 18, 2018

50. 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
May 18, 2018

51. 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.
May 18, 2018

52. An OLAM Architecture Mining query Mining result OLAM Engine OLAP
Layer4
User Interface
User GUI API
OLAM
Engine
OLAP
Engine
Layer3
OLAP/OLAM
Data Cube API
Layer2
MDDB
MDDB
Meta Data
Database API
Filtering&Integration
Filtering
Layer1
Data Repository
Data cleaning
Data
Warehouse
Databases
Data integration
May 18, 2018

53. DBMS, OLAP, and Data Mining
Task
Extraction of detailed and summary data
Summaries, trends and forecasts
Knowledge discovery of hidden patterns and insights
Type of result
Information
Analysis
Insight and Prediction
Method
Deduction (Ask the question, verify with data)
Multidimensional data modeling, Aggregation, Statistics
Induction (Build the model, apply it to new data, get the result)
Example question
Who purchased mutual funds in the last 3 years?
What is the average income of mutual fund buyers by region by year?
Who will buy a mutual fund in the next 6 months and why?
May 18, 2018

54. Example of DBMS, OLAP and Data Mining: Weather Data
Day
outlook
temperature
humidity
windy
play 1
sunny 85
false no 2 80 90
true 3
overcast 83 86
yes 4
rainy 70 96 5 68 6 65 7 64 8 72 95 9 69 10 75 11 12 13 81 14 71 91

55. Example of DBMS, OLAP and Data Mining: Weather Data
By querying a DBMS containing the above table we may answer questions like:
What was the temperature in the sunny days? {85, 80, 72, 69, 75}
Which days the humidity was less than 75? {6, 7, 9, 11}
Which days the temperature was greater than 70? {1, 2, 3, 8, 10, 11, 12, 13, 14}
Which days the temperature was greater than 70 and the humidity was less than 75? The intersection of the above two: {11}
May 18, 2018

56. Example of DBMS, OLAP and Data Mining: Weather Data
Using OLAP we can create a Multidimensional Model of our data (Data Cube).
For example using the dimensions: time, outlook and play we can create the following model.
9 / 5
sunny
rainy
overcast
Week 1
0 / 2
2 / 1
2 / 0
Week 2
1 / 1
May 18, 2018

57. Example of DBMS, OLAP and Data Mining: Weather Data
Using the ID3 algorithm we can produce the following decision tree:
outlook = sunny
humidity = high: no
humidity = normal: yes
outlook = overcast: yes
outlook = rainy
windy = true: no
windy = false: yes
May 18, 2018

58. Summary Data warehouse 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)
May 18, 2018