PPT ON DATA WAREHOUSE

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Data Warehouse  Presentation Transcript

1.Data Warehouse: Concepts and Techniques

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 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

4.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.

5.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.  

6.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”.

7.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.

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
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

12.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
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.

14.Cube: A Lattice of Cuboids

15.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

16.Example of Star Schema

17.Example of Snowflake Schema

18.Example of Fact Constellation

19.A Data Mining Query Language, DMQL: Language Primitives

20.Defining a Star Schema in DMQL

21.Defining a Snowflake Schema in DMQL

22.Defining a Fact Constellation in DMQL
define cube sales [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week, month, quarter, year)
define dimension item as (item_key, item_name, brand, type, supplier_type)
define dimension branch as (branch_key, branch_name, branch_type)
define dimension location as (location_key, street, city, province_or_state, country)
define cube shipping [time, item, shipper, from_location, to_location]:
dollar_cost = sum(cost_in_dollars), unit_shipped = count(*)
define dimension time as time in cube sales
define dimension item as item in cube sales
define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type)
define dimension from_location as location in cube sales
define dimension to_location as location in cube sales

23.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().

24.A Concept Hierarchy: Dimension (location)

25.View of Warehouses and Hierarchies

26.Multidimensional Data
Sales volume as a function of product, month, and region

27.A Sample Data Cube

28.Cuboids Corresponding to the Cube

29.Browsing a Data Cube

30.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)

31.A Star-Net Query Model

32.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

33.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

34.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

35.Multi-Tiered Architecture

36.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

37.Data Warehouse Development: A Recommended Approach

38.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

39.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

40.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.

41.Cube Operation
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)
()

42.Cube Computation: ROLAP-Based Method
Efficient cube computation methods
ROLAP-based cubing algorithms (Agarwal et al’96)
Array-based cubing algorithm (Zhao et al’97)
Bottom-up computation method (Bayer & Ramarkrishnan’99)
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 subaggregates as a “partial grouping step”
Aggregates may be computed from previously computed aggregates, rather than from the base fact table

43.Cube Computation: ROLAP-Based Method (2)
This is not in the textbook but in a research paper
Hash/sort based methods (Agarwal et. al. VLDB’96)
Smallest-parent: computing a cuboid  from the smallest cubod previously computed cuboid.
Cache-results:  caching results of a cuboid from which other cuboids are computed to reduce disk I/Os
Amortize-scans: computing as many as possible cuboids at the same time to amortize disk reads
Share-sorts:  sharing sorting costs cross multiple cuboids when sort-based method is used
Share-partitions: sharing the partitioning cost cross multiple cuboids when hash-based algorithms are used

44.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.

45.Multi-way Array Aggregation for Cube Computation

46.Multi-way Array Aggregation for Cube Computation

47.Method: the planes should be sorted and computed according to their size in ascending order.
See the details of Example 2.12 (pp. 75-78)
Idea: keep the smallest plane in the main memory, fetch and compute only one chunk at a time for the largest plane
Limitation of the method: computing well only for a small number of dimensions
If there are a large number of dimensions, “bottom-up computation” and iceberg cube computation methods can be explored

48.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

49.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 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

50.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 to which materialized cuboid(s) the relevant operations should be applied.
Exploring indexing structures and compressed vs. dense array structures in MOLAP

51.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

52.Data Warehouse Back-End Tools and Utilities
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

53.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

54.Discovery-Driven Exploration of Data Cubes
Hypothesis-driven: exploration by user, huge search space
Discovery-driven (Sarawagi et al.’98)
pre-compute measures indicating exceptions, guide user in the data analysis, at all levels of aggregation
Exception: significantly different from the value anticipated, based on a statistical model
Visual cues such as background color are used to reflect the degree of exception of each cell
Computation of exception indicator (modeling fitting and computing SelfExp, InExp, and PathExp values) can be overlapped with cube construction

55.Examples: Discovery-Driven Data Cubes

56.Complex Aggregation at Multiple Granularities: Multi-Feature Cubes
Multi-feature cubes (Ross, et al. 1998): Compute complex queries involving multiple dependent aggregates at multiple granularities
Ex. Grouping by all subsets of {item, region, month}, find the maximum price in 1997 for each group, and the total sales among all maximum price tuples
select item, region, month, max(price), sum(R.sales)
from purchases
where year = 1997
cube by item, region, month: R
such that R.price = max(price)
Continuing the last example, among the max price tuples, find the  min and max shelf life, and find the fraction of the total sales due to tuple that have min shelf life within the set of all max price tuples

57.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

58.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

59.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

60.An OLAM Architecture

61.Summary
Data warehouse
A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s 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)