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Dwh lecture 11-molap
- 1. Data Warehousing Lecture-9 Multidimensional OLAP (MOLAP) 1 Mamuna Fatima
- 2. OLAP Implementations 1. MOLAP: OLAP implemented with a multi-dimensional data structure. 2. ROLAP: OLAP implemented with a relational database. 3. HOLAP: OLAP implemented as a hybrid of MOLAP and ROLAP. 2
- 3. MOLAP Implementations OLAP has historically been implemented using a multi_dimensional data structure or “cube”. • Dimensions are key business factors for analysis: – Geographies (city, district, division, province,...) – Products (item, product category, product department,...) – Dates (day, week, month, quarter, year,...) • Very high performance achieved by O(1) time lookup into “cube” data structure to retrieve pre_aggregated results. 3
- 4. MOLAP Implementations • No standard query language for querying MOLAP - No SQL ! • Vendors provide proprietary languages allowing business users to create queries that involve pivots, drilling down, or rolling up. - E.g. MDX of Microsoft - Languages generally involve extensive visual (click and drag) support. - Application Programming Interface (API)’s also provided for probing the cubes. 4
- 5. Aggregations in MOLAP 5 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
- 6. Cube operations • Drill down: get more details – e.g., given summarized sales as above, find breakup of sales by city within each region, or within Sindh • Rollup: summarize data – e.g., given sales data, summarize sales for last year by product category and region • Slice and dice: select and project – e.g.: Sales of soft-drinks in Karachi during last quarter • Pivot: change the view of data 6
- 7. Querying the cube - 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 2001 2002 Juices Soda Drinks Ahsan Abdullah 7 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 - 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
- 8. Querying 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 8
- 9. MOLAP evaluation 9 Advantages of MOLAP: Instant response (pre-calculated aggregates). Impossible to ask question without an answer. Value added functions (ranking, % change).
- 10. MOLAP evaluation 10 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.
- 11. MOLAP Implementation issues Maintenance issue: Every data item received must be aggregated into every cube (assuming “to-date” summaries are maintained). Lot of work. Storage issue: As dimensions get less detailed (e.g., year vs. day) cubes get much smaller, but storage consequences for building hundreds of cubes can be significant. Lot of space. 11
Hinweis der Redaktion
- Molap: it is a multidimensional implementation of olap Rolap: has underlined table structure and sql also runs. Holap: best of both world. The enviornment switch b/w molap and rolap for performance. Dolap: used for sales for automation, cubes are cut into small pieces then used for dss.
- Olap is logically a cube traditionaly olap is implemented with multidimensional data struct. Second feature is dimensions. When we discuss cube, it is made by dimensions like time, product, geography. Time to access O(1) v. v. fast. Means independent of data size, or input size time to access remain same. In molap we store the aggregates (all) in multidimensional array. And access time of array is O(1).
- Molap implementation has no sql. When u store aggregates in a multi dim array, that array is in 1st normal form 2nd or 3rd normal form? It may be in 1st because you merge the tables and generate the cubes. If there is no sql then how you probe the cube. There are 3rd party tools or graphical enviornments to probe the cubes. Apis are also available to probe the cube.
- Cubes have following operations. Slice and dice: you want to explore a particular part of the cube. Pivot: make x axis and y axis interchange. Will be clear soon.
- Now to query the cube. First chart has histogram representing juices and soda. We can drill down because it appears that soda drinks have constant and almost equal sale. But now you drill down and find q3 has highest juice sale as compare to soda. Means until drill we cannot know this fact. So when there is lot of data with lot of dimensions decision maker cannot make the decision on its own. In last chart it appears that few items have continuly low sale an alarming situation.
- Previously time was on x axis, and product on y axis, but now interchanged. In 2002 8-up has highest sale, so this pivoting reveals hidden facts. When we probe a cube it shows the results using gui enviornment.
- - O(1) access time, because aggregate is already in the main memory. The second adv is that all possible questions are answered in the form of aggregates. Third adv is that molap supports value addition, ie. Sort results, find median, convert into pi chart etc.
- Works well when dimensions are few, but otherwise needs to maintin is a tough task. Cube is sparse because all possible combinations cannot have values i.e. heater sale in sibi which is almost impossible due to hot weather.
- We say data is historical but we cannot go v back in history, and if events are rapidly changing we cannot see the past. So required that the fresh data should come and when it comes we need to put it in all possible aggregates which is a lot of work. So updation is difficult thing for molap. Second issue is about space. Here a misconception may occur that if you aggregates on large grain then less storage space is required but it is not right. But even instead of day to year, the defination of year can vary even within the company so a lot of cubes will become and more storage will be required. When we do the aggregation number of cubes increase and big cube grows and it may not even come in the main memory. So will you purchase new main memory but until when you purchase main memory. So a sol is to partition the cube.