OLAP and Data Mining

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  • 1. Chapter 32 OLAP and Data Mining Transparencies
  • 2. Chapter 32 - Objectives
    • Purpose of online analytical processing (OLAP) and how OLAP differs from data warehousing.
    • Key features of OLAP applications.
    • Potential benefits associated with successful OLAP applications.
    • Rules for OLAP tools and main types of tools including: multi-dimensional OLAP (MOLAP), relational OLAP (ROLAP), and managed query environment (MQE).
  • 3. Chapter 32 - Objectives
    • OLAP extensions to SQL.
    • Concepts associated with data mining.
    • Main data mining operations including predictive modeling, database segmentation, link analysis, and deviation detection.
    • Relationship between data mining and data warehousing.
  • 4. Data Warehousing and End-User Access Tools
    • Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities.
    • Key developments include:
      • Online analytical processing (OLAP).
      • SQL extensions for complex data analysis.
      • Data mining tools.
  • 5. Introducing OLAP
    • The dynamic synthesis, analysis, and consolidation of large volumes of multi-dimensional data, Codd (1993).
    • Describes a technology that uses a multi-dimensional view of aggregate data to provide quick access to strategic information for purposes of advanced analysis.
  • 6. Introducing OLAP
    • Enables users to gain a deeper understanding and knowledge about various aspects of their corporate data through fast, consistent, interactive access to a wide variety of possible views of the data.
    • Allows users to view corporate data in such a way that it is a better model of the true dimensionality of the enterprise.
  • 7. Introducing OLAP
    • Can easily answer ‘who?’ and ‘what?’ questions, however, ability to answer ‘what if?’ and ‘why?’ type questions distinguishes OLAP from general-purpose query tools.
    • Types of analysis ranges from basic navigation and browsing (slicing and dicing) to calculations, to more complex analyses such as time series and complex modeling.
  • 8. OLAP Benchmarks
    • OLAP Council published an analytical processing benchmark referred to as the APB-1 (OLAP Council, 1998).
    • Aim is to measure a server’s overall OLAP performance rather than the performance of individual tasks.
  • 9. OLAP Benchmarks
    • APB-1 assesses most common business operations including:
      • bulk loading of data from internal/external data sources;
      • incremental loading of data from operational systems;
      • aggregation of input/level data along hierarchies;
      • calculation of new data based on business models;
      • time series analysis;
      • queries with a high degree of complexity;
      • drill-down through hierarchies;
      • ad hoc queries;
      • multiple online sessions.
  • 10. OLAP Benchmarks
    • OLAP applications are judged on their ability to provide just-in-time (JIT) information, a core requirement of supporting effective decision-making.
    • Assessing a server’s ability to satisfy this requirement is more than measuring processing performance but includes its abilities to model complex business relationships and to respond to changing business requirements.
  • 11. OLAP Benchmarks
    • APB-1 uses a standard benchmark metric called AQM (Analytical Queries per Minute).
    • AQM represents number of analytical queries processed per minute including data loading and computation time. Thus, AQM incorporates data loading performance, calculation performance, and query performance into a singe metric.
  • 12. OLAP Benchmarks
    • Publication of APB-1 benchmark results must include both the database schema and all code required for executing the benchmark .
    • An essential requirement of all OLAP applications is ability to provide users with JIT information, to make effective decisions about an organization’s strategic directions.
  • 13. OLAP Applications
    • JIT information is computed data that usually reflects complex relationships and is often calculated on the fly.
    • Also, as data relationships may not be known in advance, the data model must be flexible.
  • 14. Examples of OLAP Applications in Various Functional Areas
  • 15. OLAP Applications
    • Although OLAP applications are found in widely divergent functional areas, all have following key features:
      • multi-dimensional views of data;
      • support for complex calculations;
      • time intelligence.
  • 16. OLAP Applications - Multi-Dimensional Views of Data
    • Core requirement of building a ‘realistic’ business model .
    • Provides basis for analytical processing through flexible access to corporate data.
    • The underlying database design that provides the multi-dimensional view of data should treat all dimensions equally.
  • 17. OLAP Applications - Support for Complex Calculations
    • Must provide a range of powerful computational methods such as that required by sales forecasting, which uses trend algorithms such as moving averages and percentage growth.
    • Mechanisms for implementing computational methods should be clear and non-procedural .
  • 18. OLAP Applications – Time Intelligence
    • Key feature of almost any analytical application as performance is almost always judged over time.
    • Time hierarchy is not always used in same manner as other hierarchies.
    • Concepts such as year-to-date and period-over-period comparisons should be easily defined.
  • 19. OLAP Benefits
    • Increased productivity of end-users .
    • Reduced backlog of applications development for IT staff.
    • Retention of organizational control over the integrity of corporate data.
    • Reduced query drag and network traffic on OLTP systems or on the data warehouse.
    • Improved potential revenue and profitability.
  • 20. Representing Multi-Dimensional Data
    • Example of two-dimensional query.
      • What is the total revenue generated by property sales in each city, in each quarter of 1997?’
    • Choice of representation is based on types of queries end-user may ask.
    • Compare representation - three-field relational table versus two-dimensional matrix.
  • 21. Multi-Dimensional Data as Three-Field Table versus Two-Dimensional Matrix
  • 22. Representing Multi-Dimensional Data
    • Example of three-dimensional query.
      • ‘ What is the total revenue generated by property sales for each type of property (Flat or House) in each city, in each quarter of 1997?’
    • Compare representation - four-field relational table versus three-dimensional cube.
  • 23. Multi-Dimensional Data as Four-Field Table versus Three-Dimensional Cube
  • 24. Representing Multi-Dimensional Data
    • Cube represents data as cells in an array.
    • Relational table only represents multi-dimensional data in two dimensions.
  • 25. Multi-Dimensional OLAP Servers
    • Use multi-dimensional structures to store data and relationships between data.
    • Multi-dimensional structures are best visualized as cubes of data, and cubes within cubes of data. Each side of cube is a dimension.
    • A cube can be expanded to include other dimensions.
  • 26. Multi-Dimensional OLAP Servers
    • A cube supports matrix arithmetic.
    • Multi-dimensional query response time depends on how many cells have to be added ‘on the fly’.
    • As number of dimensions increases, number of the cube’s cells increases exponentially.
  • 27. Multi-Dimensional OLAP Servers
    • However, majority of multi-dimensional queries use summarized, high-level data.
    • Solution is to pre-aggregate (consolidate) all logical subtotals and totals along all dimensions.
    • Pre-aggregation is valuable, as typical dimensions are hierarchical in nature.
      • (e.g. Time dimension hierarchy - years, quarters, months, weeks, and days)
  • 28. Multi-Dimensional OLAP Servers
    • Predefined hierarchy allows logical pre-aggregation and, conversely, allows for a logical ‘drill-down’.
    • Supports common analytical operations
      • Consolidation.
      • Drill-down.
      • Slicing and dicing.
  • 29. Multi-Dimensional OLAP Servers
    • Consolidation - aggregation of data such as simple ‘roll-ups’ or complex expressions involving inter-related data.
    • Drill-Down - is reverse of consolidation and involves displaying the detailed data that comprises the consolidated data.
    • Slicing and Dicing - (also called pivoting) refers to the ability to look at the data from different viewpoints.
  • 30. Multi-Dimensional OLAP servers
    • Can store data in a compressed form by dynamically selecting physical storage organizations and compression techniques that maximize space utilization.
    • Dense data (i.e., data that exists for high percentage of cells) can be stored separately from sparse data (i.e., significant percentage of cells are empty).
  • 31. Multi-Dimensional OLAP Servers
    • Ability to omit empty or repetitive cells can greatly reduce the size of the cube and the amount of processing.
    • Allows analysis of exceptionally large amounts of data.
  • 32. Multi-Dimensional OLAP Servers
    • In summary, pre-aggregation, dimensional hierarchy, and sparse data management can significantly reduce the size of the cube and the need to calculate values ‘on-the-fly’.
    • Removes need for multi-table joins and provides quick and direct access to arrays of data, thus significantly speeding up execution of multi-dimensional queries.
  • 33. Codd’s Rules for OLAP Systems
    • In 1993, E.F. Codd formulated twelve rules as the basis for selecting OLAP tools.
      • Multi-dimensional conceptual view
      • Transparency
      • Accessibility
      • Consistent reporting performance
      • Client-server architecture
      • Generic dimensionality
  • 34. Codd’s Rules for OLAP
      • Dynamic sparse matrix handling
      • Multi-user support
      • Unrestricted cross-dimensional operations
      • Intuitive data manipulation
      • Flexible reporting
      • Unlimited dimensions and aggregation levels.
  • 35. Codd’s Rules for OLAP Systems
    • There are proposals to re-define or extend the rules. For example, to also include:
      • Comprehensive database management tools.
      • Ability to drill down to detail (source record) level.
      • Incremental database refresh.
      • SQL interface to the existing enterprise environment.
  • 36. Categories of OLAP Tools
    • OLAP tools are categorized according to the architecture of the underlying database.
    • Three main categories of OLAP tools include
      • Multi-dimensional OLAP (MOLAP or MD-OLAP)
      • Relational OLAP (ROLAP), also called multi-relational OLAP
      • Managed query environment (MQE)
  • 37. Multi-Dimensional OLAP (MOLAP)
    • Uses specialized data structures and multi-dimensional Database Management Systems (MDDBMSs) to organize, navigate, and analyze data.
    • Data is typically aggregated and stored according to predicted usage to enhance query performance.
  • 38. Multi-Dimensional OLAP (MOLAP)
    • Use array technology and efficient storage techniques that minimize the disk space requirements through sparse data management.
    • Provides excellent performance when data is used as designed, and the focus is on data for a specific decision-support application.
  • 39. Multi-Dimensional OLAP (MOLAP)
    • Traditionally, require a tight coupling with the application layer and presentation layer.
    • Recent trends segregate the OLAP from the data structures through the use of published application programming interfaces (APIs).
  • 40. Typical Architecture for MOLAP Tools
  • 41. MOLAP Tools - Development Issues
    • Underlying data structures are limited in their ability to support multiple subject areas and to provide access to detailed data.
    • Navigation and analysis of data is limited because the data is designed according to previously determined requirements.
  • 42. MOLAP Tools - Development Issues
    • MOLAP products require a different set of skills and tools to build and maintain the database, thus increasing the cost and complexity of support.
  • 43. Relational OLAP (ROLAP)
    • Fastest growing style of OLAP technology.
    • Supports RDBMS products using a metadata layer - avoids need to create a static multi-dimensional data structure - facilitates the creation of multiple multi-dimensional views of the two-dimensional relation.
  • 44. Relational OLAP (ROLAP)
    • To improve performance, some products use SQL engines to support complexity of multi-dimensional analysis, while others recommend, or require, the use of highly denormalized database designs such as the star schema.
  • 45. Typical Architecture for ROLAP Tools
  • 46. ROLAP Tools - Development Issues
    • Middleware to facilitate the development of multi-dimensional applications. (Software that converts the two-dimensional relation into a multi-dimensional structure).
    • Development of an option to create persistent, multi-dimensional structures with facilities to assist in the administration of these structures.
  • 47. Hybrid OLAP (HOLAP)
    • Can use data from either a RDBMS directly or a multi-dimension server.
  • 48. Managed Query Environment (MQE)
    • Relatively new development.
    • Provide limited analysis capability, either directly against RDBMS products, or by using an intermediate MOLAP server.
  • 49. Managed Query Environment (MQE)
    • Deliver selected data directly from DBMS or via a MOLAP server to desktop (or local server) in form of a datacube, where it is stored, analyzed, and maintained locally.
    • Promoted as being relatively simple to install and administer with reduced cost and maintenance.
  • 50. Typical Architecture for MQE Tools
  • 51. MQE Tools - Development Issues
    • Architecture results in significant data redundancy and may cause problems for networks that support many users.
    • Ability of each user to build a custom datacube may cause a lack of data consistency among users.
    • Only a limited amount of data can be efficiently maintained.
  • 52. OLAP Extensions to SQL
    • SQL promoted as easy to learn, non-procedural, free-format, DBMS-independent, and international standard.
    • However, major disadvantage has been inability to represent many of the questions most commonly asked by business analysts.
    • IBM and Oracle jointly proposed OLAP extensions to SQL early in 1999, adopted as an amendment to SQL.
  • 53. OLAP Extensions to SQL
    • Many database vendors including IBM, Oracle, Informix, and Red Brick Systems have already implemented portions of specifications in their DBMSs.
    • Red Brick Systems was first to implement many essential OLAP functions (as Red Brick Intelligent SQL (RISQL)) , albeit in advance of the standard.
  • 54. OLAP Extensions to SQL - RISQL
    • Designed for business analysts.
    • Set of extensions that augments SQL with a variety of powerful operations appropriate to data analysis and decision-support applications such as ranking, moving averages, comparisons, market share, this year versus last year.
  • 55. Use of the RISQL CUME Function
    • Show the quarterly sales for branch office B003, along with the monthly year-to-date figures.
      • SELECT quarter, quarterlySales, CUME(quarterlySales) AS Year-to-Date
      • FROM BranchSales
      • WHERE branchNo = ‘B003’;
  • 56. Use of the RISQL MOVINGAVG / MOVINGSUM Function
    • Show the first six monthly sales for branch office B003 without the effect of seasonality.
    • SELECT month, monthlySales,
      • MOVINGAVG(monthlySales) AS 3-MonthMovingAvg,
      • MOVINGSUM(monthlySales) AS 3-MonthMovingSum
      • FROM BranchSales
      • WHERE branchNo = ‘B003’;
  • 57. Data Mining
    • The process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions (Simoudis, 1996).
    • Involves analysis of data and use of software techniques for finding hidden and unexpected patterns and relationships in sets of data.
  • 58. Data Mining
    • Reveals information that is hidden and unexpected, as little value in finding patterns and relationships that are already intuitive.
    • Patterns and relationships are identified by examining the underlying rules and features in the data.
    • Tends to work from the data up and most accurate results normally require large volumes of data to deliver reliable conclusions.
  • 59. Data Mining
    • Starts by developing an optimal representation of structure of sample data, during which time knowledge is acquired and extended to larger sets of data.
    • Data mining can provide huge paybacks for companies who have made a significant investment in data warehousing.
    • Relatively new technology, however already used in a number of industries.
  • 60. Examples of Applications of Data Mining
    • Retail / Marketing
      • Identifying buying patterns of customers.
      • Finding associations among customer demographic characteristics.
      • Predicting response to mailing campaigns.
      • Market basket analysis.
  • 61. Examples of Applications of Data Mining
    • Banking
      • Detecting patterns of fraudulent credit card use.
      • Identifying loyal customers.
      • Predicting customers likely to change their credit card affiliation.
      • Determining credit card spending by customer groups.
  • 62. Examples of Applications of Data Mining
    • Insurance
      • Claims analysis.
      • Predicting which customers will buy new policies.
    • Medicine
      • Characterizing patient behavior to predict surgery visits.
      • Identifying successful medical therapies for different illnesses.
  • 63. Data Mining Operations
    • Four main operations include:
      • Predictive modeling.
      • Database segmentation.
      • Link analysis.
      • Deviation detection.
    • There are recognized associations between the applications and the corresponding operations.
      • e.g. Direct marketing strategies use database segmentation.
  • 64. Data Mining Techniques
    • Techniques are specific implementations of the data mining operations.
    • Each operation has its own strengths and weaknesses.
    • Data mining tools sometimes offer a choice of operations to implement a technique.
  • 65. Data Mining Techniques
    • Criteria for selection of tool includes
      • Suitability for certain input data types.
      • Transparency of the mining output.
      • Tolerance of missing variable values.
      • Level of accuracy possible.
      • Ability to handle large volumes of data.
  • 66. Data Mining Operations and Associated Techniques
  • 67. Predictive Modeling
    • Similar to the human learning experience
      • uses observations to form a model of the important characteristics of some phenomenon.
    • Uses generalizations of ‘real world’ and ability to fit new data into a general framework.
    • Can analyze a database to determine essential characteristics (model) about the data set.
  • 68. Predictive Modeling
    • Model is developed using a supervised learning approach, which has two phases: training and testing.
      • Training builds a model using a large sample of historical data called a training set.
      • Testing involves trying out the model on new, previously unseen data to determine its accuracy and physical performance characteristics.
  • 69. Predictive Modeling
    • Applications of predictive modeling include customer retention management, credit approval, cross selling, and direct marketing.
    • Two techniques associated with predictive modeling: classification and value prediction, distinguished by nature of the variable being predicted.
  • 70. Predictive Modeling - Classification
    • Used to establish a specific predetermined class for each record in a database from a finite set of possible class values.
    • Two specializations of classification: tree induction and neural induction.
  • 71. Example of Classification using Tree Induction
  • 72. Example of Classification using Neural Induction
  • 73. Predictive Modeling - Value Prediction
    • Used to estimate a continuous numeric value that is associated with a database record.
    • Uses the traditional statistical techniques of linear regression and nonlinear regression.
    • Relatively easy to use and understand.
  • 74. Predictive Modeling - Value Prediction
    • Linear regression attempts to fit a straight line through a plot of the data, such that the line is the best representation of the average of all observations at that point in the plot.
    • Problem is that the technique only works well with linear data and is sensitive to the presence of outliers (i.e., data values, which do not conform to the expected norm).
  • 75. Predictive Modeling - Value Prediction
    • Although nonlinear regression avoids the main problems of linear regression, still not flexible enough to handle all possible shapes of the data plot.
    • Statistical measurements are fine for building linear models that describe predictable data points, however, most data is not linear in nature.
  • 76. Predictive Modeling - Value Prediction
    • Data mining requires statistical methods that can accommodate non-linearity, outliers, and non-numeric data.
    • Applications of value prediction include credit card fraud detection or target mailing list identification.
  • 77. Database Segmentation
    • Aim is to partition a database into an unknown number of segments, or clusters, of similar records.
    • Uses unsupervised learning to discover homogeneous sub-populations in a database to improve the accuracy of the profiles.
  • 78. Database Segmentation
    • Less precise than other operations thus less sensitive to redundant and irrelevant features.
    • Sensitivity can be reduced by ignoring a subset of the attributes that describe each instance or by assigning a weighting factor to each variable.
    • Applications of database segmentation include customer profiling, direct marketing, and cross selling.
  • 79. Example of Database Segmentation using a Scatterplot
  • 80. Database Segmentation
    • Associated with demographic or neural clustering techniques, distinguished by:
      • Allowable data inputs.
      • Methods used to calculate the distance between records.
      • Presentation of the resulting segments for analysis.
  • 81. Link Analysis
    • Aims to establish links (associations) between records, or sets of records, in a database.
    • There are three specializations
      • Associations discovery.
      • Sequential pattern discovery.
      • Similar time sequence discovery.
    • Applications include product affinity analysis, direct marketing, and stock price movement.
  • 82. Link Analysis - Associations Discovery
    • Finds items that imply the presence of other items in the same event.
    • Affinities between items are represented by association rules.
      • e.g. ‘When customer rents property for more than 2 years and is more than 25 years old, in 40% of cases, customer will buy a property. Association happens in 35% of all customers who rent properties’.
  • 83. Link Analysis - Sequential Pattern Discovery
    • Finds patterns between events such that the presence of one set of items is followed by another set of items in a database of events over a period of time.
      • e.g. Used to understand long-term customer buying behavior.
  • 84. Link Analysis - Similar Time Sequence Discovery
    • Finds links between two sets of data that are time-dependent, and is based on the degree of similarity between the patterns that both time series demonstrate.
      • e.g. Within three months of buying property, new home owners will purchase goods such as cookers, freezers, and washing machines.
  • 85. Deviation Detection
    • Relatively new operation in terms of commercially available data mining tools.
    • Often a source of true discovery because it identifies outliers, which express deviation from some previously known expectation and norm.
  • 86. Deviation Detection
    • Can be performed using statistics and visualization techniques or as a by-product of data mining.
    • Applications include fraud detection in the use of credit cards and insurance claims, quality control, and defects tracing.
  • 87. Example of Database Segmentation using a Visualization
  • 88. Data Mining Tools
    • There are a growing number of commercial data mining tools on the marketplace.
    • Important characteristics of data mining tools include:
      • Data preparation facilities.
      • Selection of data mining operations.
      • Product scalability and performance.
      • Facilities for visualization of results.
  • 89. Data Mining and Data Warehousing
    • Major challenge to exploit data mining is identifying suitable data to mine.
    • Data mining requires single, separate, clean, integrated, and self-consistent source of data.
  • 90. Data Mining and Data Warehousing
    • A data warehouse is well equipped for providing data for mining.
    • Data quality and consistency is a prerequisite for mining to ensure the accuracy of the predictive models. Data warehouses are populated with clean, consistent data.
  • 91. Data Mining and Data Warehousing
    • Advantageous to mine data from multiple sources to discover as many interrelationships as possible. Data warehouses contain data from a number of sources.
    • Selecting relevant subsets of records and fields for data mining requires query capabilities of the data warehouse.
  • 92. Data Mining and Data Warehousing
    • Results of a data mining study are useful if there is some way to further investigate the uncovered patterns. Data warehouses provide capability to go back to the data source.