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1
Lecture 09:
OLAP
www.cl.cam.ac.uk/Teaching/current/Databases/
2
2+2
/* Microsoft SQL Server 2005 */
/* By the way, it is just VHVyaW5nIG1hY2hpbmU= :-) */
WITH SubQuery(t, s, a, b) AS
(...
3
Acknowledgments
•  DB2/400: Mastering Data Warehousing Functions. (IBM
Redbook) Chapters 1 & 2 only. http://
www.redbook...
4
uzz Words Buzz Words Buzz Words Buzz W
•  Data Warehouse (DW)
•  Decision Support (DS)
•  Data Marts (DM)
•  Data Mining...
5
OLTP vs. OLAP
•  Database is operational
•  Data is up-to-date
•  Mostly updates
•  Need to support high
levels of updat...
6
Decision Support Systems
Information Sources Data Warehouse
Server
(Tier 1)
OLAP Servers
(Tier 2)
Clients
(Tier 3)
Opera...
7
xOLAP
•  Multi-dimensional OLAP (MOLAP)
–  ‘A k-dimensional matrix based on a non relational storage
structure.’ [Agrawa...
8
Beware of Data Warehouse
Death March
Edward Yourdon, 1997, Death March:
The Complete Software Developer’s Guide to
Survi...
9
Relational data model
•  based on a single structure of data values in a two
dimensional table
CUSTOMER ORDER
Cus_id Cus...
10
Data warehousing
___Multidimensional Data
Sales volume as a function of product, month, and region
Product
Dimensions:
...
11
A Sample Data Cube
Total annual sales
of TV in U.S.A.Date
Country
sum
sum
TV
VCR
PC
1Qtr 2Qtr 3Qtr 4Qtr
U.S.A
Canada
Me...
12
A Concept Hierarchy for Dimension Location
all
Europe North_America
MexicoCanadaSpainGermany
Vancouver
M. WindL. Chan
....
13
Cuboids Corresponding to
the Cube
all
product date country
product,date product,country date, country
product, date, co...
14
Multidimensional Data:
A University Sample Data Cube
Students’ marks as a function of student, department,
Average Mark...
15
Data Warehousing
•  “A data warehouse is a subject-oriented,
integrated, time-variant, and nonvolatile
collection of da...
16
OLAP Operations
•  Roll up (drill-up): summarize data
–  by climbing up hierarchy or by dimension reduction
•  Drill do...
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Oltp vs olap vs molap vs rolap vs xolap vs zolap

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Oltp vs olap vs molap vs rolap vs xolap vs zolap

  1. 1. 1 Lecture 09: OLAP www.cl.cam.ac.uk/Teaching/current/Databases/
  2. 2. 2 2+2 /* Microsoft SQL Server 2005 */ /* By the way, it is just VHVyaW5nIG1hY2hpbmU= :-) */ WITH SubQuery(t, s, a, b) AS (  SELECT 0, 's', CAST ('<' AS VARCHAR(8000)), CAST ('110110' AS VARCHAR(8000))  UNION ALL  SELECT    t + 1,    newS,    CASE mv      WHEN 'l' THEN SubString(curr.a, 1, Len(curr.a) - 1)      WHEN 's' THEN SubString(curr.a, 1, Len(curr.a) - 1) + newZ      WHEN 'r' THEN SubString(curr.a, 1, Len(curr.a) - 1) + newZ + Left(b + '_', 1)      ELSE '?'    END,    CASE mv      WHEN 'l' THEN newZ + b      WHEN 's' THEN b      WHEN 'r' THEN SubString(b, 2, ((Len(b)-1)+Abs(Len(b)-1))/2)      ELSE '?'    END  FROM    SubQuery AS curr,    (      SELECT 's', '<', '1', '<', 'r' UNION ALL      SELECT '1', '1', '1', '1', 'r' UNION ALL /* find 0 */      SELECT '1', '_', 'a', '0', 's' UNION ALL      SELECT '1', '0', '2', '0', 's' UNION ALL      SELECT '2', '0', '2', '0', 'r' UNION ALL /* find 1; left */      SELECT '2', '_', 'a', '_', 's' UNION ALL      SELECT '2', '1', '3', '1', 'l' UNION ALL      SELECT '3', '0', '4', '1', 's' UNION ALL /* 0 -> 1 */      SELECT '4', '1', '4', '1', 'r' UNION ALL /* find 0 or _; left */      SELECT '4', '_', '5', '_', 'l' UNION ALL      SELECT '4', '0', '5', '0', 'l' UNION ALL      SELECT '5', '1', '6', '0', 's' UNION ALL /* 1 -> 0 */      SELECT '6', '1', '6', '1', 'l' UNION ALL /* rewind */      SELECT '6', '0', '6', '0', 'l' UNION ALL      SELECT '6', '<', 's', '<', 's'           /* restart */    ) AS prog(currS, currZ, newS, newZ, mv)  WHERE    curr.s = currS AND    Right(curr.a, 1) = currZ ) SELECT  CharIndex('0', a + b) - 2 FROM    SubQuery WHERE   s = 'a' OPTION (MAXRECURSION 0); /* SELECT t, s, a + '.' + b FROM SubQuery OPTION (MAXRECURSION 0); */ David Srbecky
  3. 3. 3 Acknowledgments •  DB2/400: Mastering Data Warehousing Functions. (IBM Redbook) Chapters 1 & 2 only. http:// www.redbooks.ibm.com/abstracts/sg245184.html •  Data Warehousing and OLAP Hector Garcia-Molina (Stanford University) http://www.cs.uh.edu/~ceick/6340/dw-olap.ppt •  Data Warehousing and OLAP Technology for Data Mining Department of Computing London Metropolitan University http://learning.unl.ac.uk/csp002n/CSP002N_wk2.ppt
  4. 4. 4 uzz Words Buzz Words Buzz Words Buzz W •  Data Warehouse (DW) •  Decision Support (DS) •  Data Marts (DM) •  Data Mining (DM) •  Enterprise Dashboard (ED) •  Multi-Dimensional Modeling (MDM) •  Online Analytic Processing (OLAP) •  Extract, Transform, and Load (ETL) •  MOLAP vs. ROLAP •  Three Letter Acronym (TLR) •  Drill Down, Roll up (DD+RU) •  Data vs. Knowledge (DvK) •  Data Cube vs. Sugar Cube (DCvSC) Don’t be surprised to see this sort of BDB (Blah-Dee-Blah) in the trade press: “The ED lets you transform enterprise data into knowledge with at-a-glance DS/DM and MDM, allowing interactive DD/RU over large DCs.”
  5. 5. 5 OLTP vs. OLAP •  Database is operational •  Data is up-to-date •  Mostly updates •  Need to support high levels of update transactions •  Normal form schemas are important •  Database is for analysis •  Data is historical •  Mostly reads •  Need to efficiently support complex queries, and only bulk loading of data •  Schema optimized for query processing
  6. 6. 6 Decision Support Systems Information Sources Data Warehouse Server (Tier 1) OLAP Servers (Tier 2) Clients (Tier 3) Operational DB’s Semistructured Sources Extract Transform Load Data Marts Data Warehouse e.g., MOLAP e.g., ROLAP serve Analysis Query/Reporting Data Mining serve serve From Enrico Franconi CS 636
  7. 7. 7 xOLAP •  Multi-dimensional OLAP (MOLAP) –  ‘A k-dimensional matrix based on a non relational storage structure.’ [Agrawal et al] •  Relational OLAP (ROLAP) –  ‘A relational back-end wherein operations of the data are translated to relational queries.’ [Agrawal et al] •  Hybrid OLAP (HOLAP) –  Integration of MOLAP with ROLAP. •  Desktop OLAP (DOLAP) –  Simplified versions of MOLAP or ROLAP. •  ZOLAP –  Speak with your chemist (normally only prescribed for death march victims)
  8. 8. 8 Beware of Data Warehouse Death March Edward Yourdon, 1997, Death March: The Complete Software Developer’s Guide to Surviving “Mission Impossible Projects” Death March projects “use a forced march imposed upon relatively innocent victims, the outcome of which is usually a high casualty rate.” Data Warehouses and Decision Support systems are among the most complex and demanding in the IT world. Failure rates are very high….
  9. 9. 9 Relational data model •  based on a single structure of data values in a two dimensional table CUSTOMER ORDER Cus_id Cus_name … 001 Robert … 002 Lyn … … … … Ord_no Ord_date Cus_id … 01 02 Dec 02 002 … 02 03 Dec 02 Lyn … … … … …
  10. 10. 10 Data warehousing ___Multidimensional Data Sales volume as a function of product, month, and region Product Dimensions: Product, Location, Time Month
  11. 11. 11 A Sample Data Cube Total annual sales of TV in U.S.A.Date Country sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum
  12. 12. 12 A Concept Hierarchy for Dimension Location all Europe North_America MexicoCanadaSpainGermany Vancouver M. WindL. Chan ... ...... ... ... ... all region office country TorontoFrankfurtcity
  13. 13. 13 Cuboids Corresponding to the Cube all product date country product,date product,country date, country product, date, country 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D(base) cuboid
  14. 14. 14 Multidimensional Data: A University Sample Data Cube Students’ marks as a function of student, department, Average Mark of Abraham in Year 1. Module Time Avg Avg Abraham Caroline Bridget Art Business Computing Year 1 Year 2 Year 3 Avg Design
  15. 15. 15 Data Warehousing •  “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
  16. 16. 16 OLAP Operations •  Roll up (drill-up): summarize data –  by climbing up hierarchy or by dimension reduction •  Drill down (roll down): reverse of roll-up –  from higher level summary to lower level summary or detailed data, or introducing new dimensions •  Slice and dice: –  project and select •  Pivot (rotate): –  reorient the cube, visualization, 3D to series of 2D planes. •  Other operations –  drill across: involving (across) more than one fact table –  drill through: through the bottom level of the cube to its back- end relational tables (using SQL)

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