Lecture 11
- 1. Ahsan AbdullahAhsan Abdullah 11 Data WarehousingData Warehousing Lecture-11Lecture-11 Multidimensional OLAP (MOLAP)Multidimensional OLAP (MOLAP) Virtual University of PakistanVirtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp National University of Computers & Emerging Sciences, Islamabad Email: ahsan@cluxing.com
- 2. Ahsan Abdullah 2 Multidimensional OLAP (MOLAP)Multidimensional OLAP (MOLAP)
- 3. Ahsan Abdullah 3 OLAP ImplementationsOLAP Implementations 1.1. MOLAP:MOLAP: OLAP implemented with a multi-OLAP implemented with a multi- dimensional data structure.dimensional data structure. 2.2. ROLAP:ROLAP: OLAP implemented with a relationalOLAP implemented with a relational database.database. 3.3. HOLAP:HOLAP: OLAP implemented as a hybrid ofOLAP implemented as a hybrid of MOLAP and ROLAP.MOLAP and ROLAP. 4.4. DOLAP:DOLAP: OLAP implemented for desktop decisionOLAP implemented for desktop decision support environments.support environments.
- 4. Ahsan Abdullah 4 MOLAP ImplementationsMOLAP Implementations OLAP has historically been implemented using aOLAP has historically been implemented using a multi_dimensional data structure or “cube”.multi_dimensional data structure or “cube”. Dimensions are key business factors for analysis:Dimensions are key business factors for analysis: GeographiesGeographies (city, district, division, province,...)(city, district, division, province,...) ProductsProducts (item, product category, product department,...)(item, product category, product department,...) DatesDates (day, week, month, quarter, year,...)(day, week, month, quarter, year,...) Very high performance achieved by O(1) timeVery high performance achieved by O(1) time lookup into “cube” data structure to retrievelookup into “cube” data structure to retrieve pre_aggregated results.pre_aggregated results.
- 5. Ahsan Abdullah 5 MOLAP ImplementationsMOLAP Implementations No standard query language for querying MOLAPNo standard query language for querying MOLAP - No SQL !No SQL ! Vendors provide proprietary languages allowingVendors provide proprietary languages allowing business users to create queries that involve pivots,business users to create queries that involve pivots, drilling down, or rolling up.drilling down, or rolling up. - E.g. MDX of MicrosoftE.g. MDX of Microsoft - Languages generally involve extensive visual (click andLanguages generally involve extensive visual (click and drag) support.drag) support. - Application Programming Interface (API)’s also providedApplication Programming Interface (API)’s also provided for probing the cubes.for probing the cubes.
- 6. Ahsan Abdullah 6 Aggregations in MOLAPAggregations in MOLAP Sales volume as a function of (i) product, (ii) time,Sales volume as a function of (i) product, (ii) time, and (iii) geographyand (iii) geography A cube structure created to handle this.A cube structure created to handle this. Dimensions: Product, Geography, Time Industry Category Product Hierarchical summarization pathsHierarchical summarization paths Product G eog Time w1 w2 w3 w4 w5 w6 Milk Bread Eggs Butter Jam Juice N E W S 12 13 45 8 23 10 Province Division District City Zone Year Quarter Month Week Day
- 7. Ahsan Abdullah 7 Cube operationsCube operations Drill down: get more detailsDrill down: get more details e.g., given summarized sales as above, find breakupe.g., given summarized sales as above, find breakup of sales by city within each region, or within Sindhof sales by city within each region, or within Sindh Rollup: summarize dataRollup: summarize data e.g., given sales data, summarize sales for last yeare.g., given sales data, summarize sales for last year by product category and regionby product category and region Slice and dice: select and projectSlice and dice: select and project e.g.: Sales of soft-drinks in Karachi during laste.g.: Sales of soft-drinks in Karachi during last quarterquarter Pivot: change the view of dataPivot: change the view of data
- 8. Ahsan Abdullah 8 Drill-down - 2,000 4,000 6,000 8,000 10,000 12,000 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 OJ RK 8UP PK MJ BU AJ 2001 2002 Querying the cubeQuerying the cube - 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 2001 2002 Juices Soda Drinks - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Juices Soda Drinks 2001 2002 Drill-Down Roll-Up
- 9. Ahsan Abdullah 9 Querying the cube: PivotingQuerying the cube: Pivoting - 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 2001 2002 Juices Soda Drinks - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 Orange juice Mango juice Apple juice Rola- Kola 8-UP Bubbly- UP Pola- Kola 2001 2002
- 10. Ahsan Abdullah 10 MOLAP evaluationMOLAP evaluation Advantages of MOLAP: Instant response (pre-calculated aggregates). Impossible to ask question without an answer. Value added functions (ranking, % change).
- 11. Ahsan Abdullah 11 MOLAP evaluationMOLAP evaluation Drawbacks of MOLAP: Long load time ( pre-calculating the cube may take days!). Very sparse cube (wastage of space) for high cardinality (sometimes in small hundreds). e.g. number of heaters sold in Jacobabad or Sibi.
- 12. Ahsan Abdullah 12 MOLAP Implementation issuesMOLAP Implementation issues Maintenance issue:Maintenance issue: Every data itemEvery data item received must be aggregated intoreceived must be aggregated into everyevery cubecube (assuming “to-date” summaries are(assuming “to-date” summaries are maintained).maintained). Lot of work.Lot of work. Storage issue:Storage issue: As dimensions get lessAs dimensions get less detailed (e.g., year vs. day) cubes get muchdetailed (e.g., year vs. day) cubes get much smaller, but storage consequences forsmaller, but storage consequences for building hundreds of cubes can be significant.building hundreds of cubes can be significant. Lot of space.Lot of space.
- 13. Ahsan Abdullah 13 Partitioned CubesPartitioned Cubes To overcome the space limitation of MOLAP, the cube isTo overcome the space limitation of MOLAP, the cube is partitioned.partitioned. The divide&conquer cube partitioning approach helpsThe divide&conquer cube partitioning approach helps alleviate the scalability limitations of MOLAPalleviate the scalability limitations of MOLAP implementation.implementation. One logical cube of data can be spread across multipleOne logical cube of data can be spread across multiple physical cubes on separate (or same) servers.physical cubes on separate (or same) servers. Ideal cube partitioning is completely invisible to endIdeal cube partitioning is completely invisible to end users.users. PerformancePerformance degradation does occursdegradation does occurs in case of a joinin case of a join across partitioned cubes.across partitioned cubes.
- 14. Ahsan Abdullah 14 Partitioned Cubes: How it looks Like?Partitioned Cubes: How it looks Like? Time Geography Men’s clothing Children clothing Bed linen Sales data cube partitioned at a major cottonSales data cube partitioned at a major cotton products sale outletproducts sale outlet Product
- 15. Ahsan Abdullah 15 Virtual CubesVirtual Cubes Used to query two dissimilar cubes by creating aUsed to query two dissimilar cubes by creating a third “virtual” cube by a join between two cubes.third “virtual” cube by a join between two cubes. Logically similar to a relational view i.e. linking twoLogically similar to a relational view i.e. linking two (or more) cubes along common dimension(s).(or more) cubes along common dimension(s). Biggest advantage is saving in space by eliminatingBiggest advantage is saving in space by eliminating storage of redundant information.storage of redundant information. Example:Example: Joining the store cube and the list priceJoining the store cube and the list price cube along the product dimension, to calculate thecube along the product dimension, to calculate the sale price without redundant storage of the salesale price without redundant storage of the sale price data.price data.