Data Warehouse and OLAP
Content
Ch.2: Data Warehouse 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
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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. – W. H. Inmon — “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”
• Data warehousing:
– The process of constructing and using data warehouses
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Data Warehouse — Key Features (1)
• Subject-Oriented:
– Organized around major subjects:
• E.g., 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 useless data in the decision support process.
• Integrated:
– Constructed from multiple, heterogeneous data sources:
• RDBs, flat files, on-line transaction records, …
– Applying data cleaning and data integration techniques.
• 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 — Key Features (2)
• Time Variant:
– The time horizon for the data warehouse is significantly longer than that of operational systems.
• Current value data ⇔ historical perspective info. (past 5-10 yr).
– Every key structure in the data warehouse:
• Contains an element of time [explicitly or implicitly], • But the key of tuples may or may not contain “time element”.
• Non-Volatile:
– A physically separate store of data transformed (copied) from the operational environment into one semantic data store.
• Operational data update does not occur in data warehouse. • Require no transaction processing, recovery, and concurrency control mechanisms. [⇐ on-line database operations]
– Requires only two operations in data accessing:
• initial loading of data and access of data.
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Compared w. Heterogeneous DBMS
• Traditional heterogeneous DB integration:
– Build wrappers/mediators on top of heterogeneous DB.
• E.g., IBM Data Joiner and Informix DataBlade.
– Query-driven approach: [costly]
• 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.
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Compared w. 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 ⇔ OLAP):
[ref. Table 2.1 p.43]
– User and system orientation: customer (query) ⇔ market. – Data contents: current, detailed ⇔ historical, consolidated. – Database design: ER + application ⇔ star + subject. – View: current, local ⇔ evolutionary, integrated. – Access patterns: update ⇔ read-only but complex queries.
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OLTP vs. OLAP
OLTP users function DB design data usage access unit of work # records accessed #users DB size metric clerk, IT professional day to day operations application-oriented current, up-to-date detailed, flat relational isolated repetitive
[ref. Table 2.1 p.43]
OLAP knowledge worker decision support subject-oriented historical, summarized, multidimensional integrated, consolidated ad-hoc lots of scans complex query millions hundreds 100GB-TB query throughput, response
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read/write index/hash on prim. key short, simple transaction tens thousands 100MB-GB transaction throughput
Why Separate Data Warehouse?
• High performance for both systems: [asynchronous]
– DBMS ⇒ tuned for OLTP:
• access methods, indexing, concurrency control, recovery, etc.
– Warehouse ⇒ tuned for OLAP:
• complex OLAP queries, multidimensional view, consolidation.
• Different functions and different data:
– missing data: Decision support (DS) 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 & formats which have to be reconciled.
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From Tables and Spreadsheets to Data Cubes
• Data warehouses utilize an n-dimensional data model to view 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: time, item, branch, location, supplier, …
– Dimension tables, each for a dimension.
• E.g., item (item_name, brand, type). • E.g., time (day, week, month, quarter, year).
– Fact table contains numerical measures (say, dollars_sold, units_sold, amount_budgeted, …) 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. [group by]
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Cube: A Lattice of Cuboids
C
4 0
all time item location supplier
0-D (apex) cuboid 1-D cuboids
C14
time,item
4 C2
time,location time,supplier
item,location
location,supplier
item,supplier
2-D cuboids
C34
4 C4
time,item,location
time,location,supplier time,item,supplier item,location,supplier
3-D cuboids 4-D (base) cuboid
time, item, location, supplier
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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, or Galaxy schema : 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
time_key day day_of_the_week month quarter year
item
Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales
item_key item_name brand type supplier_type
branch
branch_key branch_name branch_type
location
location_key street city province_or_street country
Measures
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Example of Snowflake Schema
time
time_key day day_of_the_week month quarter year
item
Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales location
location_key street city_key item_key item_name brand type supplier_key
supplier
supplier_key supplier_type
branch
branch_key branch_name branch_type
city
city_key city province_or_street country
Measures
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Example of Fact Constellation
time
time_key day day_of_the_week month quarter year
item
Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales
item_key item_name brand type supplier_type
Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped
branch
branch_key branch_name branch_type
location
location_key street city province_or_street country
shipper
shipper_key shipper_name location_key shipper_type
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Measures
A Data Mining Query Language, DMQL: Language Primitives
• Cube Definition (Fact Table):
– define cube <cube_name> [<dimension_list>]: <measure_list>
• Dimension Definition (Dimension Table):
– define dimension <dimension_name> as (<attribute_or_subdimension_list>)
• Special Case (Shared Dimension Tables):
– First time as “cube definition” – define dimension <dimension_name> as <dimension_name_first_time> in cube <cube_name_first_time>
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Defining a Star Schema in DMQL
• Procedure:
– define cube sales_star [time, item, branch, location]: units_sold = count(*), dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars) – 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)
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Defining a Snowflake Schema in DMQL
• Procedure:
– define cube sales_snowflake [time, item, branch, location]: units_sold = count(*), dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars) – define dimension time as (time_key, day, day_of_week, month, quarter, year) – define dimension item as (item_key, item_name, brand, type, supplier(supplier_key, supplier_type)) – define dimension branch as (branch_key, branch_name, branch_type) – define dimension location as (location_key, street, city(city_key, province_or_state, country))
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Defining a Fact Constellation in DMQL
• define cube sales [time, item, branch, location]: units_sold = count(*), dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars)
– – – – define dimension define dimension define dimension define dimension country) time as (time_key, day, day_of_week, month, quarter, year) item as (item_key, item_name, brand, type, supplier_type) branch as (branch_key, branch_name, branch_type) location as (location_key, street, city, province_or_state,
• 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
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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(). ⇐ aggregate
• 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(). ⇐ derived
• Holistic: if there is no constant bound on the storage size needed to describe a sub-aggregate.
– E.g., median(), mode(), rank(). ⇐ statistical
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A Concept Hierarchy: Dimension (location)
Higher: more general concepts
all region country city office Germany Europe ...
all ... North_America Canada ... Mexico
Spain
Frankfurt
...
Vancouver ... L. Chan ...
Toronto
M. Wind
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Lower: more specific concepts
View of Warehouses and Hierarchies
Specification of hierarchies
• Schema hierarchy
– day < {month < quarter; week} < year
• Set_grouping hierarchy
– {1..10} < inexpensive Year Quarter Month Week Day
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Multidimensional Data
• Sales volume as a function of product, month, and region. Dimensions: Product, Location, Time
gio n
Industry Category Region Country City Office Year Quarter Month Week Day
Product
Re
Product
Month
Hierarchical summarization paths
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A Sample Data Cube
Date
od uc t
TV PC VCR sum 1Qtr 2Qtr 3Qtr 4Qtr sum Total annual sales of TV in U.S.A.
U.S.A
Country
Canada
Mexico sum
Pr
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Cuboids Corresponding to the Cube
C C C
3 0
all 0-D(apex) cuboid
product
3 1
date country 1-D cuboids
3 2
product,date product,country date, country
2-D cuboids
product, date, country
3 C3
3-D(base) cuboid
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Browsing a Data Cube
• Visualization • OLAP capabilities • Interactive manipulation
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Typical OLAP Operations
ref. Fig. 2.10 p.59
• 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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A Star-Net Query Model
Each circle (abstract level) is called a footprint.
Shipping Method Customer Orders CONTRACTS AIR-EXPRESS TRUCK Time ANNUALY QTRLY DAILY CITY COUNTRY DISTRICT REGION Location Promotion DIVISION Organization
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Customer
ORDER PRODUCT LINE Product
PRODUCT ITEM PRODUCT GROUP SALES PERSON
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.
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Data Warehouse Design Process
• Top-down, bottom-up approaches or hybrid:
– 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, … – The grain (atomic level of data) of the business process. – The dimensions for each fact table record. – The measure that will populate each fact table record.
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Multi-Tiered Architecture
Metadata other
Monitor & Integrator
OLAP Server
sources
Operational Extract Transform Load Refresh Data Warehouse Serve
Analysis Query Reports Data mining
DBs
Data Marts
Data Sources
Data Storage
1
OLAP Engine Front-End Tools
2 3
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1
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. dep. (directly from warehouse) data mart.
• Virtual warehouse:
– A set of views over operational databases. – Only some summary views are materialized.
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Data Warehouse Development: A Recommended Approach
Multi-Tier Data Warehouse Distributed Data Marts
Data Mart
Data Mart
Enterprise Data Warehouse
Model refinement
Model refinement
Define a high-level corporate data model
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2
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. [Informix’s Red-brick]
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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
() (item)
• Need to compute these Group-By’s:
(city)
(year)
2
– (date, product, customer), (city, item) (city, year) (date,product), (date, n customer), (product, customer), (date), (product), (city, item, year) (customer), ().
(item, year)
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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 Li levels?
T = ∏ i =1 ( Li + 1) ⇒ 2n , if Li = 1.
n
• 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.
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Cube Computation: ROLAP-Based Method (1)
• 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 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.
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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 cuboid previously computed. – 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.
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Multi-way Array Aggregation for Cube Computation
• Partition arrays into chunks (a small sub-cube fit 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 re-visit each cell, thereby reducing memory access and storage cost.
C
c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0
b3
B 13
9 5 1 a0
14
15
16
B
b2 b1 b0
2 a1
3 a2
4 a3
60 44 28 56 40 24 52 36 20 ordering
What is the best traversing order to do multi-way aggregation?
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A
Multi-way Array Aggregation for Cube Computation
AC
BC
C
c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 B 13 9 5 1 a0 2 a1 3 a2 4 a3 14 15 16 28 24 20 44 40 36 60 56 52
b3
B b2
b1 b0
A
b0c0 of BC AB
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Multi-way Array Aggregation for Cube Computation
a0b0c0 ⇒ a0b0, b0c0, a0c0. AC
BC
C
c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 B 13 9 5 1 a0 2 a1 3 a2 4 a3 14 15 16 28 24 20 44 40 36 60 56 52
b3
B b2
b1 b0
A
AB
A=40 B=400 C=4000 ⇒ AB=16000 ⇒ AC=160000 ⇒ BC=1600000
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Multi-Way Array Aggregation for Cube Computation (cont’d)
• 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.
Store only partitions with aggregate value > threshold
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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
Base table
Cust C1 C2 C3 C4 C5 Region Asia Europe Asia America Europe
Index on Region
Index on Type
Type RecIDAsia Europe America RecID Retail Dealer Retail 1 1 0 1 1 0 0 Dealer 2 2 0 1 0 1 0 Dealer 3 3 0 1 1 0 0 Retail 4 1 0 4 0 0 1 5 0 1 0 1 0 Dealer 5
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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 — a rather costly operation fact table
• 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 to which materialized cuboids the relevant operations should be applied. • Exploring indexing structures and compressed vs. dense array structures in MOLAP.
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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 definitions, 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 (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 & definitions, ownership of data, charging policies.
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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 indices and partitions.
• Refresh
– propagate the updates from the data sources to the warehouse.
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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.
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Examples: Discovery-Driven Data Cubes
more PathExp Drill down high InExp high SelfExp
high InExp Drill down
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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 Dependent! where year = 1997 grouping variable of group gi 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 tuples that have min shelf life within the set of all max price tuples.
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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. OLAP focuses on interactive data aggregation tools; data mining emphasizes more automation, deeper analysis.
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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 (OLAP Mining):
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An OLAM Architecture (Layering)
Mining query
User GUI API
Mining result
Layer4 User Interface Layer3 OLAP/OLAM
OLAM Engine
Data Cube API
OLAP Engine
Layer2
MDDB Meta Data
MDDB
Filtering&Integration Databases
Database API
Data cleaning
Filtering Data Warehouse
Layer1 Data Repository
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Data integration
Summary
• Data warehouse:
– A subject-oriented, integrated, time-variant, & 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).
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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 VLDB, 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, 417-427, 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, 94-105, Seattle, Washington, June 1998. R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. In Proc. 1997 Int. Conf. Data Engineering, 232-243, Birmingham, England, April ‘97. K. Beyer and R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs. In Proc. 1999 ACM-SIGMOD, 359-370, Philadelphia, PA, June 1999. S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997. OLAP council. MDAPI specification version 2.0. In http://www.olapcouncil.org/research/apily.htm, 1998. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997.
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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/oledb/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. Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for simultaneous multidimensional aggregates. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 159-170, Tucson, Arizona, May 1997.
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• • •
•
• •
• 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
1
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. – W. H. Inmon — “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”
• Data warehousing:
– The process of constructing and using data warehouses
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Data Warehouse — Key Features (1)
• Subject-Oriented:
– Organized around major subjects:
• E.g., 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 useless data in the decision support process.
• Integrated:
– Constructed from multiple, heterogeneous data sources:
• RDBs, flat files, on-line transaction records, …
– Applying data cleaning and data integration techniques.
• 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 — Key Features (2)
• Time Variant:
– The time horizon for the data warehouse is significantly longer than that of operational systems.
• Current value data ⇔ historical perspective info. (past 5-10 yr).
– Every key structure in the data warehouse:
• Contains an element of time [explicitly or implicitly], • But the key of tuples may or may not contain “time element”.
• Non-Volatile:
– A physically separate store of data transformed (copied) from the operational environment into one semantic data store.
• Operational data update does not occur in data warehouse. • Require no transaction processing, recovery, and concurrency control mechanisms. [⇐ on-line database operations]
– Requires only two operations in data accessing:
• initial loading of data and access of data.
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Compared w. Heterogeneous DBMS
• Traditional heterogeneous DB integration:
– Build wrappers/mediators on top of heterogeneous DB.
• E.g., IBM Data Joiner and Informix DataBlade.
– Query-driven approach: [costly]
• 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.
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Compared w. 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 ⇔ OLAP):
[ref. Table 2.1 p.43]
– User and system orientation: customer (query) ⇔ market. – Data contents: current, detailed ⇔ historical, consolidated. – Database design: ER + application ⇔ star + subject. – View: current, local ⇔ evolutionary, integrated. – Access patterns: update ⇔ read-only but complex queries.
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OLTP vs. OLAP
OLTP users function DB design data usage access unit of work # records accessed #users DB size metric clerk, IT professional day to day operations application-oriented current, up-to-date detailed, flat relational isolated repetitive
[ref. Table 2.1 p.43]
OLAP knowledge worker decision support subject-oriented historical, summarized, multidimensional integrated, consolidated ad-hoc lots of scans complex query millions hundreds 100GB-TB query throughput, response
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read/write index/hash on prim. key short, simple transaction tens thousands 100MB-GB transaction throughput
Why Separate Data Warehouse?
• High performance for both systems: [asynchronous]
– DBMS ⇒ tuned for OLTP:
• access methods, indexing, concurrency control, recovery, etc.
– Warehouse ⇒ tuned for OLAP:
• complex OLAP queries, multidimensional view, consolidation.
• Different functions and different data:
– missing data: Decision support (DS) 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 & formats which have to be reconciled.
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From Tables and Spreadsheets to Data Cubes
• Data warehouses utilize an n-dimensional data model to view 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: time, item, branch, location, supplier, …
– Dimension tables, each for a dimension.
• E.g., item (item_name, brand, type). • E.g., time (day, week, month, quarter, year).
– Fact table contains numerical measures (say, dollars_sold, units_sold, amount_budgeted, …) 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. [group by]
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Cube: A Lattice of Cuboids
C
4 0
all time item location supplier
0-D (apex) cuboid 1-D cuboids
C14
time,item
4 C2
time,location time,supplier
item,location
location,supplier
item,supplier
2-D cuboids
C34
4 C4
time,item,location
time,location,supplier time,item,supplier item,location,supplier
3-D cuboids 4-D (base) cuboid
time, item, location, supplier
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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, or Galaxy schema : 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
time_key day day_of_the_week month quarter year
item
Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales
item_key item_name brand type supplier_type
branch
branch_key branch_name branch_type
location
location_key street city province_or_street country
Measures
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Example of Snowflake Schema
time
time_key day day_of_the_week month quarter year
item
Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales location
location_key street city_key item_key item_name brand type supplier_key
supplier
supplier_key supplier_type
branch
branch_key branch_name branch_type
city
city_key city province_or_street country
Measures
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Example of Fact Constellation
time
time_key day day_of_the_week month quarter year
item
Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales
item_key item_name brand type supplier_type
Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped
branch
branch_key branch_name branch_type
location
location_key street city province_or_street country
shipper
shipper_key shipper_name location_key shipper_type
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Measures
A Data Mining Query Language, DMQL: Language Primitives
• Cube Definition (Fact Table):
– define cube <cube_name> [<dimension_list>]: <measure_list>
• Dimension Definition (Dimension Table):
– define dimension <dimension_name> as (<attribute_or_subdimension_list>)
• Special Case (Shared Dimension Tables):
– First time as “cube definition” – define dimension <dimension_name> as <dimension_name_first_time> in cube <cube_name_first_time>
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Defining a Star Schema in DMQL
• Procedure:
– define cube sales_star [time, item, branch, location]: units_sold = count(*), dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars) – 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)
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Defining a Snowflake Schema in DMQL
• Procedure:
– define cube sales_snowflake [time, item, branch, location]: units_sold = count(*), dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars) – define dimension time as (time_key, day, day_of_week, month, quarter, year) – define dimension item as (item_key, item_name, brand, type, supplier(supplier_key, supplier_type)) – define dimension branch as (branch_key, branch_name, branch_type) – define dimension location as (location_key, street, city(city_key, province_or_state, country))
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Defining a Fact Constellation in DMQL
• define cube sales [time, item, branch, location]: units_sold = count(*), dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars)
– – – – define dimension define dimension define dimension define dimension country) time as (time_key, day, day_of_week, month, quarter, year) item as (item_key, item_name, brand, type, supplier_type) branch as (branch_key, branch_name, branch_type) location as (location_key, street, city, province_or_state,
• 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
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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(). ⇐ aggregate
• 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(). ⇐ derived
• Holistic: if there is no constant bound on the storage size needed to describe a sub-aggregate.
– E.g., median(), mode(), rank(). ⇐ statistical
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A Concept Hierarchy: Dimension (location)
Higher: more general concepts
all region country city office Germany Europe ...
all ... North_America Canada ... Mexico
Spain
Frankfurt
...
Vancouver ... L. Chan ...
Toronto
M. Wind
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Lower: more specific concepts
View of Warehouses and Hierarchies
Specification of hierarchies
• Schema hierarchy
– day < {month < quarter; week} < year
• Set_grouping hierarchy
– {1..10} < inexpensive Year Quarter Month Week Day
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Multidimensional Data
• Sales volume as a function of product, month, and region. Dimensions: Product, Location, Time
gio n
Industry Category Region Country City Office Year Quarter Month Week Day
Product
Re
Product
Month
Hierarchical summarization paths
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A Sample Data Cube
Date
od uc t
TV PC VCR sum 1Qtr 2Qtr 3Qtr 4Qtr sum Total annual sales of TV in U.S.A.
U.S.A
Country
Canada
Mexico sum
Pr
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Cuboids Corresponding to the Cube
C C C
3 0
all 0-D(apex) cuboid
product
3 1
date country 1-D cuboids
3 2
product,date product,country date, country
2-D cuboids
product, date, country
3 C3
3-D(base) cuboid
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Browsing a Data Cube
• Visualization • OLAP capabilities • Interactive manipulation
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Typical OLAP Operations
ref. Fig. 2.10 p.59
• 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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A Star-Net Query Model
Each circle (abstract level) is called a footprint.
Shipping Method Customer Orders CONTRACTS AIR-EXPRESS TRUCK Time ANNUALY QTRLY DAILY CITY COUNTRY DISTRICT REGION Location Promotion DIVISION Organization
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Customer
ORDER PRODUCT LINE Product
PRODUCT ITEM PRODUCT GROUP SALES PERSON
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.
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Data Warehouse Design Process
• Top-down, bottom-up approaches or hybrid:
– 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, … – The grain (atomic level of data) of the business process. – The dimensions for each fact table record. – The measure that will populate each fact table record.
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Multi-Tiered Architecture
Metadata other
Monitor & Integrator
OLAP Server
sources
Operational Extract Transform Load Refresh Data Warehouse Serve
Analysis Query Reports Data mining
DBs
Data Marts
Data Sources
Data Storage
1
OLAP Engine Front-End Tools
2 3
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1
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. dep. (directly from warehouse) data mart.
• Virtual warehouse:
– A set of views over operational databases. – Only some summary views are materialized.
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Data Warehouse Development: A Recommended Approach
Multi-Tier Data Warehouse Distributed Data Marts
Data Mart
Data Mart
Enterprise Data Warehouse
Model refinement
Model refinement
Define a high-level corporate data model
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2
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. [Informix’s Red-brick]
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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
() (item)
• Need to compute these Group-By’s:
(city)
(year)
2
– (date, product, customer), (city, item) (city, year) (date,product), (date, n customer), (product, customer), (date), (product), (city, item, year) (customer), ().
(item, year)
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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 Li levels?
T = ∏ i =1 ( Li + 1) ⇒ 2n , if Li = 1.
n
• 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.
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Cube Computation: ROLAP-Based Method (1)
• 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 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.
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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 cuboid previously computed. – 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.
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Multi-way Array Aggregation for Cube Computation
• Partition arrays into chunks (a small sub-cube fit 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 re-visit each cell, thereby reducing memory access and storage cost.
C
c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0
b3
B 13
9 5 1 a0
14
15
16
B
b2 b1 b0
2 a1
3 a2
4 a3
60 44 28 56 40 24 52 36 20 ordering
What is the best traversing order to do multi-way aggregation?
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A
Multi-way Array Aggregation for Cube Computation
AC
BC
C
c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 B 13 9 5 1 a0 2 a1 3 a2 4 a3 14 15 16 28 24 20 44 40 36 60 56 52
b3
B b2
b1 b0
A
b0c0 of BC AB
39
Multi-way Array Aggregation for Cube Computation
a0b0c0 ⇒ a0b0, b0c0, a0c0. AC
BC
C
c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 B 13 9 5 1 a0 2 a1 3 a2 4 a3 14 15 16 28 24 20 44 40 36 60 56 52
b3
B b2
b1 b0
A
AB
A=40 B=400 C=4000 ⇒ AB=16000 ⇒ AC=160000 ⇒ BC=1600000
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Multi-Way Array Aggregation for Cube Computation (cont’d)
• 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.
Store only partitions with aggregate value > threshold
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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
Base table
Cust C1 C2 C3 C4 C5 Region Asia Europe Asia America Europe
Index on Region
Index on Type
Type RecIDAsia Europe America RecID Retail Dealer Retail 1 1 0 1 1 0 0 Dealer 2 2 0 1 0 1 0 Dealer 3 3 0 1 1 0 0 Retail 4 1 0 4 0 0 1 5 0 1 0 1 0 Dealer 5
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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 — a rather costly operation fact table
• 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 to which materialized cuboids the relevant operations should be applied. • Exploring indexing structures and compressed vs. dense array structures in MOLAP.
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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 definitions, 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 (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 & definitions, ownership of data, charging policies.
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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 indices and partitions.
• Refresh
– propagate the updates from the data sources to the warehouse.
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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.
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Examples: Discovery-Driven Data Cubes
more PathExp Drill down high InExp high SelfExp
high InExp Drill down
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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 Dependent! where year = 1997 grouping variable of group gi 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 tuples that have min shelf life within the set of all max price tuples.
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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. OLAP focuses on interactive data aggregation tools; data mining emphasizes more automation, deeper analysis.
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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 (OLAP Mining):
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An OLAM Architecture (Layering)
Mining query
User GUI API
Mining result
Layer4 User Interface Layer3 OLAP/OLAM
OLAM Engine
Data Cube API
OLAP Engine
Layer2
MDDB Meta Data
MDDB
Filtering&Integration Databases
Database API
Data cleaning
Filtering Data Warehouse
Layer1 Data Repository
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Data integration
Summary
• Data warehouse:
– A subject-oriented, integrated, time-variant, & 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).
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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 VLDB, 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, 417-427, 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, 94-105, Seattle, Washington, June 1998. R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. In Proc. 1997 Int. Conf. Data Engineering, 232-243, Birmingham, England, April ‘97. K. Beyer and R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs. In Proc. 1999 ACM-SIGMOD, 359-370, Philadelphia, PA, June 1999. S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997. OLAP council. MDAPI specification version 2.0. In http://www.olapcouncil.org/research/apily.htm, 1998. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997.
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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/oledb/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. Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for simultaneous multidimensional aggregates. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 159-170, Tucson, Arizona, May 1997.
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