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Why Multi-Dimensional Databases?

Why Multi-Dimensional Databases?. No single "best" data structure for all applications within an enterprise Select the most appropriate data structure on a case-by-case basis from a palette of standard database structures Multidimensional Databases for OLAP?. Why Multi-Dimensional Databases?.

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Why Multi-Dimensional Databases?

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  1. Why Multi-Dimensional Databases? • No single "best" data structure for all applications within an enterprise • Select the most appropriate data structure on a case-by-case basis from a palette of standard database structures • Multidimensional Databases for OLAP?

  2. Why Multi-Dimensional Databases? • From econometric research conducted at MIT in the 1960s, the multidimensional database has matured into the database engine of choice for data analysis applications • Inherent ability to integrate and analyze large volumes of enterprise data • Offers a good conceptual fit with the way end-users visualize business data • Most business people already think about their businesses in multidimensional terms • Managers tend to ask questions about product sales in different markets over specific time periods

  3. Multidimensional Database • Spreadsheets – A 2D database? • Functionalities • What about a stack of similar spreadsheets for different times? • Limitations? We can not relate data in different sheets easily

  4. Multidimensional Database An MDDB is a computer software system designed to allow for the efficient and convenient storage and retrieval system of large volumes of data that is • Intimately related & • Stored, viewed and analyzed form different perspectives These perspectives are called Dimensions

  5. A Motivating Example An automobile manufacturer wants to increase sale volumes by examining sales data collected throughout the organization. The evaluation would require viewing historical sales volume figures from multiple dimensions such as • Sales volume by model • Sales volume by color • Sales volume by dealer • Sales volume over time

  6. SALES VOLUMES FOR GLEASON DEALERSHIP • MODELCOLOR SALES VOLUME • ----------------------------------------------------------------------------------------------------------------------------------- • MINI VAN BLUE 6 • MINI VAN RED 5 • MINI VAN WHITE 4 • SPORTS COUPE BLUE 3 • SPORTS COUPE RED 5 • SPORTS COUPE WHITE 5 • SEDAN BLUE 4 • SEDAN RED 3 • SEDAN WHITE 2

  7. Multidimensional Array Structure 6 5 4 M O D 3 5 5 E L 4 3 2 COLOR Measurement Sales Volumes Dimension MiniVan Coupe Sedan Blue Red White Dimension Positions

  8. RDBMS vs. MDD • Multidimensional array structure represents a higher level of organization than the relational table • Perspectives are embedded directly into the structure in the multidimensional model • All possible combinations of perspectives containing a specific attribute (the color BLUE, for example) line up along the dimension position for that attribute. • Perspectives are placed in fields in the relational model - tells us nothing about field contents.

  9. RDBMS vs. MDD • MDD makes data browsing and manipulation intuitive to the end-user • Any data manipulation action possible with a MDD is also possible using relational technology • Substantial cognitive advantages in query formulation • Substantial computational performance advantages in query processing when using MDD

  10. SALES VOLUMES FOR ALL DEALERSHIPS MODELCOLORDEALERSHIPVOLUME ---------------------------------------------------------------------------------------------------------------------------------------------------- • MINI VAN BLUE CLYDE 6 • MINI VAN BLUE GLEASON 6 • MINI VAN BLUE CARR 2 • MINI VAN RED CLYDE 3 • MINI VAN RED GLEASON 5 • MINI VAN RED CARR 5 • MINI VAN WHITE CLYDE 2 • MINI VAN WHITE GLEASON 4 • MINI VAN WHITE CARR 3 • SPORTS COUPE BLUE CLYDE 2 • SPORTS COUPE BLUE GLEASON 3 • SPORTS COUPE BLUE CARR 2 • SPORTS COUPE RED CLYDE 7 • SPORTS COUPE RED GLEASON 5 • SPORTS COUPE RED CARR 2 • SPORTS COUPE WHITE CLYDE 4 • SPORTS COUPE WHITE GLEASON 5 • SPORTS COUPE WHITE CARR 1 • SEDAN BLUE CLYDE 6 • SEDAN BLUE GLEASON 4 • SEDAN BLUE CARR 2 • SEDAN RED CLYDE 1 • SEDAN RED GLEASON 3 • SEDAN RED CARR 4 • SEDAN WHITE CLYDE 2 • SEDAN WHITE GLEASON 2 • SEDAN WHITE CARR 3

  11. M O D E L DEALERSHIP COLOR Mutlidimensional Representation Sales Volumes Mini Van Coupe Carr Sedan Gleason Clyde Blue Red White

  12. M O D E L DEALERSHIP COLOR Viewing Data - An Example Sales Volumes Assume that each dimension has 10 positions, as shown in the cube above

  13. Viewing Data - An Example • How many records would be there in a relational table? • Implications for viewing data from an end-user standpoint? SALES VOLUMES FOR ALL DEALERSHIPS MODEL COLOR DEALERSHIP VOLUME MINI VAN BLUE CLYDE 2 MINI VAN BLUE GLEASON 2 MINI VAN BLUE CARR 2 MINI VAN RED CLYDE 1 MINI VAN WHITE GLEASON 3 • • • RECORD NUMBER.... 998 RECORD NUMBER.... 999 RECORD NUMBER.... 1000

  14. Performance Advantages • Volume figure when car type = SEDAN, color=BLUE, & dealer=GLEASON? • RDBMS – all 1000 records might need to be searched to find the right record • MDB has more ‘knowledge’ about where the data lies • Max. of 30 position searches!! • Average case 15 vs. 500

  15. Performance Advantages • Total Sales across all colors and dealers when model = SEDAN? • RDBMS – all 1000 records must be searched to get the answer • MDB – Sum the contents of one 10x10 ‘slice’

  16. Performance Advantages • Data manipulation that requires a minute in RDBMS may require only a few seconds in MDB • MDBs are an order of magnitude faster than RDBMSs • Performance benefits are more for queries that generate cross-tab views of data • The performance advantages offered by multidimensional technology facilitates the development of interactive decision support applications like OLAP that can be impractical in a relational environment.

  17. RDBMS vs. MDB • Any data manipulation action possible with a multidimensional database is also possible using relational technology • MDBs however offer several advantages like: • Ease of data presentation and navigation • Ease of maintenance • Performance

  18. Ease of Data Presentation & Navigation • Intuitive spreadsheet like data views are natural output of MDBs • Obtaining the same views in a relational environment, requires either a complex SQL or a SQL generator against a RDB to convert the table outputs into a more intuitive format • Top N queries are not possible with SQL at all

  19. Ease of Maintenance • Ease of maintenance because data is stored as it is viewed • No additional overhead is required to translate user queries into requests for data • To provide same intuitiveness, RDBs use indexes and sophisticated joins which require significant maintenance and storage

  20. Performance • Performance of MDBs can be matched by RDBs through database tuning • Not possible to tune the database for all possible adhoc queries • Tuning requires resources of an expensive DB specialist • Aggregate navigators are helping RDBs to catch up with MDBs as far as aggregation queries are concerned

  21. M O D E L DEALERSHIP COLOR COLOR COLOR JANUARY FEBRUARY MARCH Adding Dimension - An Example Sales Volumes Mini Van Mini Van Mini Van Coupe Coupe Coupe Carr Carr Carr Sedan Sedan Sedan Gleason Gleason Gleason Clyde Clyde Clyde Blue Red White Blue Red White Blue Red White

  22. When is MDD (In)appropriate? First, consider situation 1 PERSONNEL EMPLOYEE# LAST NAME EMPLOYEE AGE SMITH 01 21 REGAN 12 19 FOX 31 63 WELD 14 31 KELLY 54 27 LINK 03 56 KRANZ 41 45 LUCUS 33 41 WEISS 23 19

  23. When is MDD (In)appropriate? Now consider situation 2 1. Set up a MDD structure for situation 1, with LAST NAME and Employee# as dimensions, and AGE as the measurement. 2. Set up a MDD structure for situation 2, with MODEL and COLOR as dimensions, and SALES VOLUME as the measurement.

  24. When is MDD (In)appropriate? 21 19 63 L A 6 5 4 31 M S O T D 3 5 5 27 E N L A 4 3 2 M 56 E 45 COLOR 41 19 EMPLOYEE # MDD Structures for the Situations EmployeeAge Smith Regan Sales Volumes Fox Mini Van Weld Coupe Kelly Link Sedan Red White Blue Kranz Lucas Weiss 31 41 23 01 14 54 03 12 33 Note the sparse between the two MDD representations

  25. When is MDD (In)appropriate? • Our sales volume dataset has a great number of meaningful interrelationships • Interrelationships more meaningful than individual data elements themselves. • The greater the number of inherent interrelationships between the elements of a dataset, the more likely it is that a study of those interrelationships will yield business information of value to the company. • Highly interrelated dataset types be placed in a multidimensional data structure for greatest ease of access and analysis

  26. When is MDD (In)appropriate? • No last name is matching with more than one emp # and no emp # is matching with more than one last name • In contrast, there is a sales figure associated with every combination of model and color resulting in a completed filled up 3x3 matrix • Performance suffers (RDB 9 vs. MDB 18)

  27. When is MDD (In)appropriate? • The relative performance advantages of storing multidimensional data in a multidimensional array increase as the size of the dataset increases • The relative performance disadvantages of storing non-multidimensional data in a multidimensional array increase as the size of the dataset increases. • NO inherent value of storing Non-multidimensional data (employee data) in multidimensional arrays

  28. When is MDD Appropriate? • The greater the number of inherent interrelationships between the elements of a dataset, the more likely it is that a study of those interrelationships will yield business information of value to the company. • Most companies have limited time and resources to devote to analyzing data • It therefore becomes critical that these highly interrelated dataset types be placed in a multidimensional data structure for greatest ease of access and analysis.

  29. When is MDD Appropriate? • Examples of applications that are suited for • multidimensional technology: • Financial Analysis and Reporting • Budgeting • Promotion Tracking • Quality Assurance and Quality Control • Product Profitability

  30. MDD Features - Rotation 6 5 4 6 3 4 M C O O D L 3 5 5 5 5 3 E O L R 4 3 2 4 5 2 o ( ROTATE 90 ) COLOR MODEL Sales Volumes Mini Van Blue Coupe Red Sedan White Mini Van Coupe Sedan Blue Red White View #1: ModelxColor View #2: ColorxModel • Also referred to as “data slicing.” • Each rotation yields a different slice or two dimensional table • of data.

  31. Sales Volumes M C C Mini Van Blue Blue O O O D L L Coupe Red Red E O O L R R Mini Van Carr Carr Sedan White White Gleason Gleason Coupe Clyde Clyde Sedan Sedan Coupe Mini Van Blue Red White Carr Gleason Clyde DEALERSHIP COLOR MODEL o o o ( ROTATE 90 ) ( ROTATE 90 ) ( ROTATE 90 ) DEALERSHIP DEALERSHIP MODEL View #3 View #1 View #2 D D E E A A L L Mini Van Carr Carr E E M R R O Coupe Gleason Gleason S S D H H E Blue Blue Mini Van Sedan I I L Red Clyde Clyde Red Coupe White P P White Sedan Coupe Sedan Mini Van White Clyde Gleason Carr Red Blue DEALERSHIP COLOR MODEL o o ( ROTATE 90 ) ( ROTATE 90 ) MODEL COLOR COLOR View #6 View #4 View #5 MDD Features - Rotation

  32. MDD Features - Rotation • All the six views can be obtained by simple rotation • In MDBs rotations are simple as no rearrangement of data is required • Rotation is also referred to as “data slicing” • No. of views • 2D – 2 • 3D – 6 • 4D - ? 24

  33. MDD Features - Ranging • How sales volume of models painted with new metallic blue compared with the sales of normal blue color models? • The user knows that only Sports Coupe and Mini Van models have received the new paint treatment • Also the user knows that only 2 dealers viz, Carr and Clyde have unconstrained supply of these models

  34. Sales Volumes Mini Van M Mini Van O D Coupe Carr E Coupe Clyde L Normal Metal Blue Blue Carr Clyde DEALERSHIP Normal Metal Blue Blue COLOR MDD Features - Ranging • The end user selects the desired positions along each dimension. • Also referred to as "data dicing." • The data is scoped down to a subset grouping

  35. MDD Features - Ranging • The reduced array can now be rotated and used in computations in the same was as the parent array • Referred to as “Data Dicing” as data is scoped down to a subset grouping • Complex SQL query is required in RDB • Performance is better in MDB as less resource consuming searches are required

  36. MDD Features – Roll-Up & Drill-Down • Users want different views of the same data • For eg., Sales Volume by model vs, sales volume by dealership • Many times views are similar • Sales volume by dealership vs. volume by district • Natural relationship between Sales Volumes at the DEALERSHIP level and Sales Volumes at the DISTRICT level • Sales Volumes for all the dealerships in a district sum to the Sales Volumes for that district

  37. MDD Features – Roll-Up & Drill-Down • Multidimensional database technology is specially designed to facilitate the handling of these natural relationships • Define two related aggregates on the same dimension • One aggregation is dealership and the other district • District is at a higher level of aggregation than dealership

  38. MDD Features - Roll-Ups & Drill Downs • The figure presents a definition of a hierarchy within • the organization dimension. • Aggregations perceived as being part of the same dimension. • Moving up and moving down levels in a hierarchy is referred to • as “roll-up” and “drill-down.”

  39. MDD Features - Roll-Ups & Drill Downs

  40. MDD Features:Drill-Down Through a Dimension

  41. Queries • High degree of structure in MDB makes the query language very simple and efficient • Query language is intuitive • Output is immediately useful to end user

  42. Queries: Example • Display sales volume by model for each dealership PRINT TOTAL.(SALES_VOLUME KEEP MODEL DEALERSHIP) Trends emerge and comparisons are easily made DEALERSHIP MODELCLYDE GLEASON CARR MINI VAN 7 5 6 SPORTS COUPE 4 6 8 SEDAN 3 8 12

  43. Queries: Example • Corresponding SQL SELECT MODEL, DEALERSHIP, SUM(SALES_VOLUME) FROM SALES_VOLUME GROUP BY MODEL, DEALERSHIP ORDER BY MODEL, DEALERSHIP MODEL | DEALERSHIP | SUM(SALES_VOLUME) MINI VAN | CLYDE | 7 MINI VAN | GLEASON | 5 MINI VAN | CARR | 6 SPORTS COUPE | CLYDE | 4 SPORTS COUPE | GLEASON | 6 SPORTS COUPE| CARR | 8 SEDAN | CLYDE | 3 SEDAN | GLEASON | 8 SEDAN| CARR | 12

  44. Queries: Example Use report writer in addition to SQL and we get MINI VAN CLYDE 7 GLEASON 5 CARR 6 SPORTS COUPE CLYDE 4 GLEASON 5 CARR 8 SEDAN CLYDE 3 GLEASON 8 CARR 12

  45. MDD Features:Multidimensional Computations • Well equipped to handle demanding mathematical functions. • Can treat arrays like cells in spreadsheets. For example, in a budget analysis situation, one can divide the ACTUAL array by the BUDGET array to compute the VARIANCE array. • Applications based on multidimensional database technology typically have one dimension defined as a "business measurements" dimension. • Integrates computational tools very tightly with the database structure.

  46. Sales Volumes 10 0.1 11 Mini Van Coupe 12 0.33 16 - 10 8 0.2 Sedan 16 0.0 16 Variance Budget Actual BUSINESS MEASUREMENTS MDD Features:Multidimensional Computations

  47. The Time Dimension • TIME as a predefined hierarchy for rolling-up and drilling-down across days, weeks, months, years and special periods, such as fiscal years. • Eliminates the effort required to build sophisticated hierarchies every time a database is set up. • Extra performance advantages

  48. Contrasting Relational Model and MD Model

  49. RDBMS vs. MDDB • DO I still use RDBMS for my DW? • MDDBs store data in hypercube, i.e., multidimensional array • RDBMS store data as tables with row and columns that do not map directly to multidimensional view that user have of data • EDW – RDBMS • Data Marts - MDDB

  50. RDBMS vs. MDDB: Trade-Offs • SIZE • MDDBs limited by size • Mid – 1990s 10GB caused problems • Today – 100GB is OK • Large DWs are still better served by relational front-ends running against high performance and scalable RDBMS • VOLATILITY • Highly volatile data are better handled by RDBMS • MDDBs take long to load and update

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