• Data Warehouse: Basic Concepts
  • Data Warehouse Modeling: Data Cube and OLAP
  • Data Warehouse Design and Usage
  • Data Warehouse Implementation
  • Data Generalization by Attribute-Oriented Induction
  • Summary

What is a Data Warehouse?

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

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

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.

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”

Data Warehouse—Nonvolatile

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


usersclerk, IT professional
knowledge worker


DB design

day to day operations


decision support


datasurrent, up-to-date detailed, flat relational isolated
historical, summarized, multidimensional integrated, sonsolidated




read/write index/hash on prim key


lots of scans

unit of work

#records accessed

short, simple transaction


complex query



DB size




transaction throughput



query throughput, response

Why a Separate Data Warehouse?

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

Data Warehouse: A Multi-Tiered ArchitectureUntitled


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

Extraction, Transformation, and Loading (ETL)

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

Metadata Repository

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

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.

Cube: A Lattice of Cuboids

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

Example of Star Schema

Example of Snowflake Schema

Example of Fact Constellation

A Concept Hierarchy: Dimension (location)

Data Cube Measures: Three Categories

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

View of Warehouses and Hierarchies

Multidimensional Data

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

A Sample Data Cube

Cuboids Corresponding to the Cube

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)

Typical OLAP Operations

A Star-Net Query Model

Browsing a Data Cube

Design of Data Warehouse: A Business Analysis Framework

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

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

Data Warehouse Development: A Recommended Approach

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

From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM)

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

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?

\[ T = \prod_{i=1}^{n}(L_{i}+1) \]

  • 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.

The “Compute Cube” Operator

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

Indexing OLAP Data: Bitmap Index

  • Index on a particular column
  • Each value in the column has a bit vector: bit-op is fast
  • The length of the bit vector: # of records in the base table
  • The i-th bit is set if the i-th row of the base table has the value for the indexed column
  • not suitable for high cardinality domains
    • A recent bit compression technique, Word-Aligned Hybrid (WAH), makes it work for high cardinality domain as well [Wu, et al. TODS’06]

Indexing OLAP Data: Join Indices

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

Efficient Processing OLAP Queries

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

OLAP Server Architectures

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

Attribute-Oriented Induction

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

Attribute-Oriented Induction: An Example

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

Class Characterization: An Example

Basic Principles of Attribute-Oriented Induction

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

Attribute-Oriented Induction: Basic Algorithm

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

Presentation of Generalized Results

  • Generalized relation:
    • Relations where some or all attributes are generalized, with counts or other aggregation values accumulated.
  • Cross tabulation:
    • Mapping results into cross tabulation form (similar to contingency tables).
    • Visualization techniques:
    • Pie charts, bar charts, curves, cubes, and other visual forms.
  • Quantitative characteristic rules:
    • Mapping generalized result into characteristic rules with quantitative information associated with it, e.g.,

grad(x) Λ male(x) ⇒ birth_region(x) = “Canadd[t:53%] ∨ birth_region(x) = “foreign[t:47%]

Mining Class Comparisons

  • Comparison: Comparing two or more classes
  • Method:
    • Partition the set of relevant data into the target class and the contrasting class(es)
    • Generalize both classes to the same high level concepts
    • Compare tuples with the same high level descriptions
    • Present for every tuple its description and two measures
      • support - distribution within single class
      • comparison - distribution between classes
    • Highlight the tuples with strong discriminant features
  • Relevance Analysis:
    • Find attributes (features) which best distinguish different classes

Concept Description vs. Cube-Based OLAP

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


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


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

References (cont')

  • C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design: Relational and Dimensional Techniques. John Wiley, 2003
  • W. H. Inmon. Building the Data Warehouse. John Wiley, 1996
  • R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2ed. John Wiley, 2002
  • P. O'Neil and D. Quass. Improved query performance with variant indexes. SIGMOD'97
  • Microsoft. OLEDB for OLAP programmer's reference version 1.0. In, 1998
  • A. Shoshani. OLAP and statistical databases: Similarities and differences. PODS’00.
  • S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. ICDE'94
  • P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987.
  • J. Widom. Research problems in data warehousing. CIKM’95.
  • K. Wu, E. Otoo, and A. Shoshani, Optimal Bitmap Indices with Efficient Compression, ACM Trans. on Database Systems (TODS), 31(1), 2006, pp. 1-38.

  • March 13, 2013

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