Data Mining and Data Warehousing Henryk Maciejewski Data

Data Mining and Data Warehousing

Henryk Maciejewski

Data Warehousing and OLAP

Part II Data Warehousing –Contents

• OLAP Approach to Data Analysis• Database for OLAP = Data Warehouse

– Logical model – Physical models (ROLAP, MOLAP, HOLAP)

• Querying multidimensional data• DW project methodologies

Further Reading

• J. Han, M. Kamber, Data Mining: Concepts and Techniques, Second Edition, Elsevier 2006.

• W. Inmon: Building the data warehouse, Wiley 2005.• F. Silvers: Building and maintaining a data

warehouse, CRC Press 2008.• www.information-management.com

From DBMS to Analytical Systems...

• The 1960s: first IT systems• The 1970s:

• DBMS systems• On-line transactional processing systems (OLTP)

• The 1990s:• On-line analytical processing (OLAP), data warehousing,

data mining – Business Intelligence (BI), DSS

IT Systems Generate Data Deluge• IT Systems in:

• Retail trade – bar codes, credit cards, …• Banking, insurance, telecoms, healthcare, etc. etc.• Science (biology , weather/Earth monitoring, sky surveys,...)

• Data Deluge• WalMart: 20 million transactions per day• Mobil: ca. 100 TB of data (exploration of oil reserves)• Human Genome Project: ~GB of data• NASA Earth Observing System: 50 GB per hour (!)• DISS solar energy plant monitoring: ~ 800 numbers / 5 secs

How to Get Information out of Data

• Efficient technologies available to gather and store data • Simple approaches to data analysis prove inefficient

• Spreadsheet based, SQL query based, ...

• Technologies + tools needed for efficient data analysis / knowledge extraction from data• Hence OLAP, KDD (Knowledge Discovery in Databases), DM emerged

• Information – data in context; data that have meaning, relevance and purpose

Various Approaches to Data Analysis

SQL

Data Warehouse / OLAP

Data Mining

SQL queries to „raw” data

Multidimensional data model: y(w1,w2,...wn)

Database for OLAPIntegrated data(ETL – Extract-Transform-Load)

Discovering relationships in data

E.g., Customer profiles ,…Models to assess credit risk, etc.

Data Analysis Techniques – SQL Queries

Data source

Data source

Data source

Programmer – DB admin generates an SQL program

SQL

„Cross-sectional” question

Drawbacks:Considerable coding effortHeavy load on OLTP serversMultiple versions of the truth…

SQL

SQL

Report

Data Warehouse (W. Inmon 1992)

ETL: Data access Data integration (cleaning, transformation)

Source data

Source data

Source data

Data WarehouseData Mart

Specific structure of database optimized for OLAP (MDDB, „snowflake”, „star schema”, ROLAP, MOLAP, HOLAP)

OLAP / DSS

Why OLAP Technology is BecomingIndispensable

• Getting information of out historical data• Integration of data sources in the enterprise• „Cross-sectional” analyses of enterprise data

→ discovering relationships / patterns in large amounts of data → trend analysis→ data mining

OLAP/Data Warehouse – Key Design Issues

• Data organization• Multidimensional data model (facts seen as a function of dimensions)• Physical data storage that allows for fast (online) analysis of vast data

volumes

• Data integration• Ensure high quality of analytical data• „Taming the data chaos”• Single version of the truth

OLAP vs. OLTP – Different Applications and Data Model

• OLTP – operational data– automation of day-to-day operations of organization:

→ phone-call billing, orders / invoices processing, banking / credit card transactions, etc., etc.

• OLAP– analytical data– getting information for decision support

→ Who are our best customers (characteristics)?→ Churn analysis→ How does increase in sales correlate with quality of service?

OLAP vs. OLTP – SummaryProblem OLTP OLAP

Main applications Automation of operations of organization:- entering data on routine day-to-day

transactions- fixed structure reports / summaries

created on regular basis (daily, monthly, etc.)

Decision support - multidimensional statistical

analyses, forecasting, ad hoc queries,

- advanced reporting

Time horizon for data retention

Usually short term (90 days, 1 year) Long term data retention, to support historic data analyses, comparative reports, trend analysis over time

Data updates ‘On the fly’, during individual transaction Static data, updated on regular basis (e.g., monthly), data collected over time (time-stamped)

Data access Frequent access to small portions of data (a few or tens of records)

Simple, well structured queries

Rare access involving large amounts of data

Complex queries, ad-hoc

Schedule

• OLAP Approach to data analysis– OLAP vs OLTP– OLAP – data integration

• Database for OLAP = Data Warehouse– Logical data model – multidimensionality– Physical data models (ROLAP, MOLAP, HOLAP)

„Data chaos” – Why it is Hard to Run Analytics Based on OLTP

• Main obstacles for building successful OLAP ‘on top’ of transactional data:– Data awareness– Data understanding– Data variability– Data redundancy (and hence consistency)

• „Data islands” in disparate transactional systems

Data Chaos – ExampleFaculty of EE

Courses DB

Teachers DB

Notes DB

Recruitment DB

Faculty of Architecture

Tutors DB

Exam results DB

Courses DB

Problems / difficulties:

→ how to find data

→ how to extract data

→ understand the meaning

→ clean the data

Data warehouse

17

Business Intelligence Based on OLTP?

• How to get to the data in the DB?

• How to locate the right table / column ?

• How to understand the meaning of the data ?

• How to clean the data ?

Dedicated System for BI (OLAP)• ETL (Extract Transform

Load)– Connect to source DB– Integrate / clean– Transform to the

multidimensional model

• Multidimensional model of data (facts vs. dimensions)

Example: Multidimensional Model

Cubes:Over-hoursAvailabilityFuel consumption

Example: ETL Process ETL for the cube Availability

Data Warehouse – DefinitionDate Warehouse – subject-oriented, integrated, time-varying, non-volatile

collection of data that is used primarily in organizational decision making.

Subject oriented – data is organized around subjects of interest to data analyst (e.g., customer, product, supplier); transactional systems are process-oriented(e.g., order processing).

Integrated – data warehouse integrates data from several data sources; data characteristics (attributes) must be coded in a consistent way (e.g., consistent coding of SEX (‘male’-’female’, ‘m’-’f’, 0-1)).

Non-volatile – data loaded into data warehouse is a ‘snapshot’ of operational data at a specific point in time; once loaded, data in warehouse cannot be changed.

Time-varying – data elements in warehouse are time-stamped to facilitate analysis of changes / trends over time.

Summary of This Part

• Concept of OLTP and OLAP– Different use, different requirements for

• Data organization (data model) • Database design

• Need for data integration – Overcoming „data chaos”– Ensuring high quality of analytical data in warehouse

23

Example: OLAP for Student Notes



Example: IBM Tivoli Monitoring Data Warehouse

• Monitoring agents –keep 24 h detaileddata

• Data Warehouse –aggregated, time-stamped data drawnfrom agents

Agent Default attribute group

Monitoring Agent for Windows OS

Network_InterfaceNT_ProcessorNT_Logical_DiskNT_MemoryNT_Physical_DiskNT_ServerNT_System

Monitoring Agent for UNIX

Disk System

Monitoring Agent for Linux

Linux_CPULinux_CPU_AveragesLinux_CPU_ConfigLinux_DiskLinux_Disk_IOLinux_Disk_Usage_TrendsLinux_IO_ExtLinux_NetworkLinux_NFS_Statistics

Monitoring Agent for DB2

KUDDBASEGROUP00 KUDDBASEGROUP01 KUDBUFFERPOOL00 KUDINFO00 KUDTABSPACE

Example: IBM Tivoli Monitoring Data Warehouse

Schedule

• Multidimensional Model of OLAP Data• Why OLAP Doesn’t Like Normalized DB

• Relational OLAP (ROLAP)• Multidimensional OLAP (MOLAP)• Hybrid OLAP (HOLAP)

OLAP: Multidimensional Model of Data

• OLAP = multidimensional analysis of data• Multidimensional model of data:

– Measure as a value in multidimensional space of dimensions– Numeric measures – objects of analysis, also referred to as facts– Dimensions – variables on which the measure depends / that uniquely

determine the measure• E.g., measure: sales [$]

dimensions: product, shop, date

OLAP: Multidimensional Model of Data

• Dimension hierarchies, e.g.,– Geographical hierarchy: shop – city – region – country – Time hierarchy: day of week – week – month – year – Product hierarchy: item – type – group

Example – Model Built in Lab• Multidimensional model for analysis of students’ notes:

– Measure: Student’s grade (note)– Dimensions:

• Characteristics of students• Characteristics of teachers• Characteristics of courses (group of courses, type of courses, etc.)• Time hierarchy: calendar semester – year• Workload of students / teachers, etc.

– Various statistics will be of interest, e.g., average grade, number of grades, std deviation, distribution,...

Useful Concepts

– Aggregation: e.g., computing total sales by year based on more detailed data

– Drill-down: create more detailed view (i.e., decrease level of aggregation)

– Rollup: increase level of aggregation– Slice-and-dice: reduce dimensionality of data: fix values of

some dimensions and observe how data depends on the remaining dimensions

Schedule



Normalized DB (a Reminder)

• Database design for OLTP uses Entity Relationship diagrams and normalization techniques

• Normalized DB:– No data redundancy– Many tables with many-to-one relationships– Optimized for easy / fast updates of data– Efficient for constantly changing data– Efficient for OLTP

Normalized DB - ExampleContact IDCustomer IDContact nameContact typeShipment ID

StatusOrder IDOrder item IDCustomer ID

Customer IDCustomer nameAddressCity...

Shipment

Customer

Contact

Order IDCustomer IDOrder dateSales rep ID

Sales rep IDSales rep nameDistrict ID

District IDDistrict namemanager

Order IDOrder item IDProduct IDQuantity

Product IDProduct nameProduct type...

Order

Sales rep

District

Order item

Product

Task – answer the following OLAP query:

Which products were sold to a particular group of customers within specified time frame?

Normalized DB – Problems with OLAP Queries

• Many ‘join’ operations on tables low efficiency of SQL queries

• ‘Circular join paths’ – a query can be answered in two different ways different results possible

• Complicated database scheme SQL code difficult to build / maintain

OLAP: Requirements for Database Design

• Simplicity of database scheme• Efficiency of multidimensional queries• Consistency and accuracy of data

• Database schemes to meet these requirements– Relational OLAP (ROLAP)– Multidimensional OLAP (MOLAP)– Hybrid OLAP (HOLAP)

Schedule



Relational OLAP• Warehouse data stored using a relational database server• Multidimensional data model represented by a star-schema

database or snowflake-schema database

• Star schema:– Single fact table– Single table for each dimension– A fact table entry consist of:

• Aggregate value of the measure • Foreign keys to dimension tables (composite key of the fact table)

Relational OLAP• Warehouse data stored using a relational database server• Multidimensional data model represented by a star-schema

database or snowflake-schema database

• Snowflake schema:– Variant of star schema with (some) dimension tables normalized (for

easier maintenance of dimension data)

Example – Star Schema

Sales person IDProduct IDDate IDCustomer ID

Number soldamount

Sales(fact table)

Product ID

Prod codeProd nameProd typeProd category

Customer ID

NameSexAgeJob name

Sales person ID

NameRegion DivisionOffice

Date ID

DateYearMonthDay

Sales person

Date

Product

Customer

Example – Snowflake Schema

Sales person IDProduct IDDate IDCustomer ID

Number soldamount

Sales(fact table)

Product ID

Prod codeProd nameProd typeProd category

Customer ID

NameSexAgeJob ID

Sales person ID

NameRegion DivisionOffice

Date ID

DateYearMonthDay

Sales person

Date

Product

Customer

Job ID

Job nameJob category

…

Job Code

ROLAP – Example of OLAP Query• OLAP query:How many products were sold to a specific group of customers in a giventime frame?

Translates into the following SQL query:select sum(number_sold) as number_soldfrom fact_sales a,

dimension_date b,dimension_customer c

where b.date = ’21jan2001’dand c.sex = ‘F’and a.dateID = b.dateIDand a.customerID = c.customerID;

Schedule



Multidimensional OLAP

• Warehouse data stored in a multidimensional database (MDDB)

• MDDB – Specialized storage facility that directly reflects multidimensional

model of data – MDDB can be viewed as an N-dimensional (hyper)cube in which values

of numerical measure (object of analysis) are stored– Data stored in MDDB is presummarized, i.e., values stored in cross

sections of dimensions have been aggregated at the MDDB build time (thus performance of multidimensional (OLAP) queries is high)

MDDB – Idea

• Sample base table:– Analysis variable (fact):

note

– Classification variables (dimensions): attributes of students, attributes of teachers, semester, year, faculty, etc.

MDDB – Ideaselect sum(note) as SUM, count(*) as N, spec, semester, yearfrom base_tablewhere spec='INF‘ and semester=8 and year=2001group by spec, semester, year

MDDB – Data Aggregation• Each crossing of the cube

contains specified statistics for the analysis variable(s)

• Distributive measures can be stored in cube, such as N, SUM, SUMWGT, UWSUM, NMISS, USS, MIN, MAX

• Algebraic measures can be computed from stored measures, such as AVG=SUM/N

MDDB – Data Aggregation• Problem with holistic measures,

ie. measures for which no algebraic aggregate function exists. E.g., MEDIAN

• In large cube applications approximate values of holistic measures are computed using algebraic measures

Cubes and Subcubes

• OLAP queries related to a subset of dimensions– Result is aggregated at query time from the NWAY cube – E.g., report on sales of all products over subsequent years – sum for all

products and all months needs to be computed at run time– If there are many dimensions with high cardinality, this can be lengthy

• Subcubes are used to speed up performance for queries (related to subsets of dimensions) that users are likely to ask most frequently

Which Subcubes to Store?Idea: find categories which will be usedmost frequently, with smallest cardinality

Starnet (spiral) model: put categories inascending order of cardinality

Draw spiral starting with YEAR (most frequent use anticipated, lowestcardinality) ⇒ lists of categories = subcubes

YEAR SECTOR REGION GRP_SUPP MONTH GRP SHOP SUPPLIER FAMILY DAY ARTICLE

YEAR SECTOR REGION GRP_SUPP MONTH GRP SHOP SUPPLIER FAMILY DAY

...

YEAR SECTOR

YEAR

Example: Building MDDB (SAS)proc mddb data=grades out=grades_mddb

label='MDDB for analysis of grade data';class year sem sex faculty institute exam type id_title;var note /n sum min max;

hierarchy year sem /name=„Time Hierarchy";hierarchy faculty institute /name=„Affiliation Hierarchy";

run;

NOTE: SAS/MDDB(R) Server Software has been initialized.NOTE: N-way complete cells=1455.NOTE: „Time Hierarchy" computed from "NWAY" cells=10.NOTE: „Affiliation Hierarchy" computed from "NWAY" cells=26.NOTE: PROCEDURE MDDB used:

real time 1:26.54cpu time 1:19.82

Example: Building MDDB (SAS)• DATA – specify base table for the MDDB• CLASS statement – specify classification variables (i.e., NWAY

cube dimensions)• VAR statement – specify analysis variables (with statistics to

be stored in MDDB – distributive aggregate functions)• HIERARCHY statements – specify subcubes to include in

MDDB

• Subcubes can be added / removed (ADDHIER, REMOVEHIERstatements)

ROLAP vs. MOLAPMOLAP ROLAP

Very high query performance Very scalable

Easy maintenance Lower query performance

Less scalable (fixed max size of a cube) Design and maintenance more difficult

Problem with dimensions with very high cardinality

Problem with constantly growing database

„Rule of thumb”: use MOLAP as long as possible, then switch to ... HOLAP

Schedule



HOLAP Data Model

Multidimensional data provider (MDP)

MDDB Relational DB Star schema

cache

viewer

Viewer (OLAP applications)sees a logical MDDB(or a proxy or virtual MDDB) whichis presented by the MDP

HOLAP Techniques• „Racking” – individual MDDBs for

different values of one dimension (e.g., separate MDDBs for subsequent years)

• „Stacking” – different subcubes stored in separate MDDBs or tables (e.g., YEAR*COUNTRY*PRODUCT – local MDDB, YEAR*COUNTRY*PRODUCT*MONTH – on remoteserver)

year=2003 2004 2005 2006

Multidimensional data provider (MDP)

When to Use HOLAP?

• Too much data for one MDDB• Access to existing ROLAP solutions• Ensuring scalability with growing data volume• Flexible integration of distributed data sources• Improved performance – distributed processing of queries

• Price: HOLAP metadata must be maintained

DW Architectures – MOLAP

RDB

ERP

Flat files

OLTP Data Sources

DW(ODS)

ETL

Data Layer OLAP ApplicationLayer

PresentationLayer

RDBMS ServerMDDBS ServerMOLAP Engine

MDDBs MDXXML/A

Create/storecubes

DW Architectures – ROLAP

RDB

ERP

Flat files

OLTP Data Sources

DW(ODS)

ETL

Data Layer OLAP ApplicationLayer

PresentationLayer

RDBMS ServerAnalyticalServer

MDXXML/A

ComplexSQL queries

MS SQL Storage Settings• Proactive caching

– MOLAP – best performance; possible data latency (recent data changes not seen)– ROLAP – recent changes in data seen immediately; price – poor performance– Proactive caching: build MOLAP cache to boost performance

• ? How frequently MOLAP cube should be rebuilt• ? Should outdated MOLAP be queried while cube is rebuilt• ? Rebuild cubes on schedule or based on changes in data

• Minimize latency vs maximize performance • Partitions

– Vertical: cubes based on subsets of rows in fact table– Horizontal: cubes based on separate fact tables (e.g. for subsequent years)

MS SQL Server Analysis Services Storage Settings

Standarizing Access to OLAP Data Sources – XML/A

• XML for Analysis (XML/A)• Standard API between OLAP client and OLAP data provider• Design goals:

– Open standards based, not bound to any language or technology– Optimized for the Web: minimize round-trip transactions and stateless

• Client – server communicate using XML, HTTP, SOAP

Standarizing Access to OLAP Data Sources – XML/A

• XML/A Methods:– Discover – retrieve information (metadata) from provider, such as list of available

cubes and their properties– Execute – request a command execution by server (MDX language command – e.g.,

OLAP MDX SELECT)

Multidimensional Expressions Language (MDX)

• Introduced by Microsoft in OLE DB for OLAP• Now considered de facto standard for querying multidimensional data in OLAP

cubes

• Simple form of MDX query expression:

SELECT axis_specs ON COLUMNS,axis_specs ON ROWS

FROM cubeWHERE slicer_specs

MDX – By Examples• Examples based on cube built in

lab

• A tuple– uniquelly identifies a cell in a cube– defined by a combination of attribute

members for different attributes

– if some attribute is not specified – its All (default) member is used

– if measure is not specified, the first (default) measure defined in the cube is used

MDX – Tuples

• [Measures].[Note Count] is a tuple

• To identify a cell, the All member of other attributes was used

MDX – Tuples

• Tuple points to male (M) students in Student Group (Studiengang) A

• Use ( ) to identify a tuple

MDX – Sets of Tuples

• Two tuples (Note Avg and Note Count) form a set

• Use { } to identify a set of tuples

MDX – Cartesian Products

• .Members MDX function lists members of an attribute

• on columns – axis 0on rows – axis 1(up to 128 axes)

More axes Cartesian product

MDX – Cartesian Products

• Now set of tuples is used in Axis 0 (columns) specification

• Each cell is produced as an intesection of its attribute members

MDX – Slicer Axis (WHERE)

• WHERE clause – used to specify set, tuple or member that restrict the members returned for rows and columns





Data Warehouse Project Methodology(-ies)

• SAS Rapid Data Warehouse Methodology• IBM DW / BI Project Methodology• …

• Purpose: – Ensure disciplined, iterative, approach in the management and

implementation of data warehousing projects– Enable successful business and technical implementation of the data

warehouse

DW Project Methodology - Phases• Assessment

– Determine whether there exists a realistic need and opprotunity to develop a successful DW– Project definition stage (team, sponsor, criteria for success, expectations)– Initial assessment of IT infrastructure (is project feasibile?)– Outcome: formal document

• Requirements– Requirements gathering (in-depth interviews with business people) – Reconciliation stage (analyze gap between expectations and IT capabilities)– Outcome: Requirements Definition Document (logical and physical data model; data extraction paths from

source OLTP systems; transformations required; DW update schedule)

• Desing / Implementation / deployment– Implement logical data model– Build ETL processes (validate, clean, integrate)– Load data to DW– Design, implement data analysis interfaces

• Train users• Review

DW Specific Requirements -Remarks

• Analytical needs in company– Types of reports, time schedules (daily / weekly etc.) – Hierarchies of data / hierachies of reports– Identification of data sources

• Updates of data in DW– Data integration rules; handling missing / wrong data– Time schedule for DW updates

• Data latency / performance– Recent changes in OLTP seen immediately in OLAP? – What latency is acceptable?– OLAP query performance

Data Integration • Analyze source OLTP systems

– Determine DBMS systems / data formats– Select most appropriate sources / columns (cleanest)

• Analyze required integration– Ensure the same coding conventions (‘m-w’, ‘male-female, ‘0-1’)– Identify synonyms, homonyms, analogies– Ensure data quality (integrity, accuracy, completeness)

• data value integrity• data structure integrity

– Define exception handling rules / missing data handling / default values– Finally, define data integration rule/algorithm for each variable

Example – Synonyms, Homonyms, Analogies

• Define how to resolve name conficts between data sources / columns:– Homonyms: same name but different meaning, e.g., Type in one source

reffers to model of a car („AURIS”, „CLIO”, etc.), and in another source – to category („picup”, „truck”, „passenger”, etc. )

– Synonyms: different names but the same meaning, e.g., PersonID in one source, EmployeeCode in another

– Analogies: attributes describe the same object, but differently, e.g., PaymentMethod in one source refers to „cash”, „check”, „credit card”, and in another to „VISA”, „MasterCard”, „USD” etc.

Example – Data Integrity Specify legal relationships between data values

Number of values in a relationship

Student can have 0,1 or n diplomas‘Undergraduate’ 0‘Graduate’ 1 or n

Employee Temporary PermanentName + +Date of birth + +Contract final date + --Anniversary date o +(+ required; -- not allowed; o optional)

Summary• Build dedicated database for OLAP – data mart / warehouse

– Data integration– Data quality assurance

• Database organization– Multidimensional model of data– Physical data organization

• Denormalization• Aggregation

• Benefits from user’s perspective– Integrated overall picture of the enterprise– Easy access to historical data– Trustworthy information returned (single version of the truth)– DSS queries with no impact on transactional systems

• DW Methodology to ensure successful implementation

Documents

Data Mining and Data Warehousing Henryk Maciejewski Data