OLAP IN DATA MINING
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OLAP (Online Analytical Processing) is used for complex queries over large datasets to discover patterns and trends. OLAP queries are run over a data warehouse and use multidimensional models like MOLAP with pre-computed data cubes or ROLAP with a star schema. Key OLAP concepts include slicing and dicing the data cube and drill-down and roll-up operations. Data mining techniques like association rule mining can be used to discover relationships between items in transaction data beyond what is found with frequent itemset mining alone.
![OLTP vs. OLAP
§ On-Line Transaction Processing (OLTP):
– technology used to perform updates on operational or
transactional systems (e.g., point of sale systems)
§ On-Line Analytical Processing (OLAP):
– technology used to perform complex analysis of the data
in a data warehouse
4
OLAP is a category of software technology that enables
analysts, managers, and executives to gain insight into data
through fast, consistent, interactive access to a wide variety
of possible views of information that has been transformed
from raw data to reflect the dimensionality of the enterprise
as understood by the user.
[source: OLAP Council: www.olapcouncil.org]](https://image.slidesharecdn.com/olapandmining-200324064025/85/OLAP-IN-DATA-MINING-4-320.jpg)
OLAP IN DATA MINING
- 1. C S 5 6 1 - S P R I N G 2 0 1 2 W P I , M O H A M E D E LTA B A K H OLAP & DATA MINING 1
- 3. OLAP • OLAP: Online Analytic Processing • OLAP queries are complex queries that • Touch large amounts of data • Discover patterns and trends in the data • Typically expensive queries that take long time • Also called decision-support queries • In contrast to OLAP: • OLTP: Online Transaction Processing • OLTP queries are simple queries, e.g., over banking or airline systems • OLTP queries touch small amount of data for fast transactions 3
- 4. OLTP vs. OLAP § On-Line Transaction Processing (OLTP): – technology used to perform updates on operational or transactional systems (e.g., point of sale systems) § On-Line Analytical Processing (OLAP): – technology used to perform complex analysis of the data in a data warehouse 4 OLAP is a category of software technology that enables analysts, managers, and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the dimensionality of the enterprise as understood by the user. [source: OLAP Council: www.olapcouncil.org]
- 5. OLAP AND DATA WAREHOUSE 5 Query and Analysis Component Data Integration Component Data Warehouse Operational DBs External Sources Internal Sources OLAP Server Meta data OLAP Reports Client Tools Data Mining
- 6. OLAP AND DATA WAREHOUSE • Typically, OLAP queries are executed over a separate copy of the working data • Over data warehouse • Data warehouse is periodically updated, e.g., overnight • OLAP queries tolerate such out-of-date gaps • Why run OLAP queries over data warehouse?? • Warehouse collects and combines data from multiple sources • Warehouse may organize the data in certain formats to support OLAP queries • OLAP queries are complex and touch large amounts of data • They may lock the database for long periods of time • Negatively affects all other OLTP transactions 6
- 8. EXAMPLE OLAP APPLICATIONS • Market Analysis • Find which items are frequently sold over the summer but not over winter? • Credit Card Companies • Given a new applicant, does (s)he a credit-worthy? • Need to check other similar applicants (age, gender, income, etc…) and observe how they perform, then do prediction for new applicant 8 OLAP queries are also called “decision- support” queries
- 9. MULTI-DIMENSIONAL VIEW • Data is typically viewed as points in multi-dimensional space 9 10 47 30 12Milk 1%fat 3/1 3/2 3/3 3/4 Milk 2%fat Orange juice bread Time Items NY MA CA Location Raw data cubes (raw level without aggregation) Typical OLAP applications have many dimensions
- 11. APPROACHES FOR OLAP • Relational OLAP (ROLAP) • Multi-dimensional OLAP (MOLAP) • Hybrid OLAP (HOLAP) = ROLAP + MOLAP 11
- 12. RELATIONAL OLAP: ROLAP • Data are stored in relational model (tables) • Special schema called Star Schema • One relation is the fact table, all the others are dimension tables 12 Facts Week Product Product Year Region Time Channel Revenue Expenses Units Model Type Color Channel Region Nation District Dealer Time Large table Small tables
- 13. CUBE vs. STAR SCHEMA 13 Facts Week Product Product Year Region Time Channel Revenue Expenses Units Model Type Color Channel Region Nation District Dealer Time Data inside the cube are the fact records Dimension tables describe the dimensions 10 47 30 12Milk 1%fat 3/1 3/2 3/3 3/4 Milk 2%fat Orange juice bread Time Items NY MA CA Location
- 14. ROLAP: EXTENSIONS TO DBMS • Schema design • Specialized scan, indexing and join techniques • Handling of aggregate views (querying and materialization) • Supporting query language extensions beyond SQL • Complex query processing and optimization • Data partitioning and parallelism 14
- 15. SLICING & DICING • Dicing • how each dimension in the cube is divided • Different granularities • When building the data cube • Slicing • Selecting slices of the data cube to answer the OLAP query • When answering a query 15 Dicing Time by day 10 47 30 12Milk 1%fat 3/1 3/2 3/3 3/4 Milk 2%fat Orange juice bread Time Items NY MA CA Location Dicing Location by state
- 16. SLICING & DICING: EXAMPLE 1 16 Dicing Slicing Slicing operation in ROLAP is basically: -- Selection conditions on some attributes (WHERE clause) + -- Group by and aggregation
- 17. SLICING & DICING: EXAMPLE 2 17
- 18. SLICING & DICING: EXAMPLE 3 18
- 19. DRILL-DOWN & ROLL-UP 19 Region Sales variance Africa 105% Asia 57% Europe 122% North America 97% Pacific 85% South America 163% Nation Sales variance China 123% Japan 52% India 87% Singapore 95% Drill-down (Group by Nation) Roll-up (group by Region)
- 20. ROLAP: DRILL-DOWN & ROLL-UP 20 Drill-down Roll-up
- 21. MOLAP • Unlike ROLAP, in MOLAP data are stored in special structures called “Data Cubes” (Array-bases storage) • Data cubes pre-compute and aggregate the data • Possibly several data cubes with different granularities • Data cubes are aggregated materialized views over the data • As long as the data does not change frequently, the overhead of data cubes is manageable 21 Sales 1996 Red blob Blue blob 1997 Every day, every item, every city Every week, every item category, every city
- 22. MOLAP: CUBE OPERATOR 22 Raw-data (fact table) Aggregation over the X axis Aggregation over the Y axis Aggregation over the Z axis Aggregation over the X,Y
- 23. MOLAP & ROLAP • Commercial offerings of both types are available • In general, MOLAP is good for smaller warehouses and is optimized for canned queries • In general, ROLAP is more flexible and leverages relational technology • ROLAP May pay a performance penalty to realize flexibility 23
- 24. OLTP vs. OLAP 24 • Clerk, IT Professional • Day to day operations • Application-oriented (E-R based) • Current, Isolated • Detailed, Flat relational • Structured, Repetitive • Short, Simple transaction • Read/write • Index/hash on prim. Key • Tens • Thousands • 100 MB-GB • Trans. throughput • Knowledge worker • Decision support • Subject-oriented (Star, snowflake) • Historical, Consolidated • Summarized, Multidimensional • Ad hoc • Complex query • Read Mostly • Lots of Scans • Millions • Hundreds • 100GB-TB • Query throughput, response User Function DB Design Data View Usage Unit of work Access Operations # Records accessed #Users Db size Metric OLTP OLAP Source: Datta, GT
- 25. OLAP: SUMMARY • OLAP stands for Online Analytic Processing and used in decision support systems • Usually runs on data warehouse • In contrast to OLTP, OLAP queries are complex, touch large amounts of data, try to discover patterns or trends in the data • OLAP Models • Relational (ROLAP): uses relational star schema • Multidimensional (MOLAP): uses data cubes 25
- 26. Overview on Data Mining Techniques 26
- 27. DATA MINING vs. OLAP 27 • OLAP - Online Analytical Processing – Provides you with a very good view of what is happening, but can not predict what will happen in the future or why it is happening Data Mining is a combination of discovering techniques + prediction techniques
- 28. DATA MINING TECHNIQUES • Clustering • Classification • Association Rules • Frequent Itemsets • Outlier Detection • …. 28
- 29. FREQUENT ITEMSET MINING • Very common problem in Market-Basket applications • Given a set of items I ={milk, bread, jelly, …} • Given a set of transactions where each transaction contains subset of items • t1 = {milk, bread, water} • t2 = {milk, nuts, butter, rice} 29 What are the itemsets frequently sold together ?? % of transactions in which the itemset appears >= α
- 30. EXAMPLE 30 Assume α = 60%, what are the frequent itemsets • {Bread} à 80% • {PeanutButter} à 60% • {Bread, PeanutButter} à 60% called “Support” All frequent itemsets given α = 60%
- 31. HOW TO FIND FREQUENT ITEMSETS • Naïve Approach • Enumerate all possible itemsets and then count each one 31 All possible itemsets of size 1 All possible itemsets of size 2 All possible itemsets of size 3 All possible itemsets of size 4
- 32. CAN WE OPTIMIZE?? 32 Assume α = 60%, what are the frequent itemsets • {Bread} à 80% • {PeanutButter} à 60% • {Bread, PeanutButter} à 60% called “Support” Property For itemset S={X, Y, Z, …} of size n to be frequent, all its subsets of size n-1 must be frequent as well
- 33. APRIORI ALGORITHM • Executes in scans, each scan has two phases • Given a list of candidate itemsets of size n, count their appearance and find frequent ones • From the frequent ones generate candidates of size n+1 (previous property must hold) • Start the algorithm where n =1, then repeat 33 Use the property reduce the number of itemsets to check
- 36. DATA MINING TECHNIQUES • Clustering • Classification • Association Rules • Frequent Itemsets • Outlier Detection • …. 36
- 37. ASSOCIATION RULES MINING • What is the probability when a customer buys bread in a transaction, (s)he also buys milk in the same transaction? 37 Bread ----------------------> milk Implies? Frequent itemsets cannot answer this question….But Association rules can General Form Association rule: x1, x2, …, xn à y1, y2, …ym Meaning: when the L.H.S appears (or occurs), the R.H.S also appears (or occurs) with certain probability Two measures for a given rule: 1- Support(L.H.S U R.H.S) > α 2- Confidence C = Support(L.H.S U R.H.S)/ Support(L.H.S)
- 38. EXAMPLE 38 Rule: Bread à PeanutButter • Support of rule = support(Bread, PeanutButter) = 60% • Confidence of rule = support(Bread, PeanutButter)/support(Bread) = 75% Rule: Bread, Jelly à PeanutButter • Support of rule = support(Bread, Jelly, PeanutButter) = 20% • Confidence of rule = support(Bread, Jelly, PeanutButter) /support(Bread, Jelly) = 100% Usually we search for rules: Support > α Confidence > β