?????? Data Warehousing and OLAP ????? ??????? - PowerPoint PPT Presentation

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?????? Data Warehousing and OLAP ????? ???????

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Data Warehousing and ... Discussions * 5.1 Roadmap Integrated with Data mining Major Group / Sales Analysis Prospects ... 1.3 Datawarehousing 1.4 Three ... – PowerPoint PPT presentation

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Title: ?????? Data Warehousing and OLAP ????? ???????


1
??????Data Warehousing and OLAP????????????
2
Agenda
  1. Introduction
  2. Data Warehouse Theory
  3. System Features
  4. Demo
  5. Discussions

3
1. Introduction
4
1.1 Introduction
  • A data warehouse is a subject-oriented,
    integrated, time-variant, nonvolatile collection
    of data in support of management decisions

5
1.1 Introduction (contd)
  • How are organizations using data warehouse ?
  • Increasing customer focus, which includes the
    analysis of customer buying patterns.
  • Repositioning products and managing product
    portfolios by comparing the performance of sales
    by time or regions, in order to fine-tune
    production strategies
  • Analyzing operations and looking for sources of
    profit
  • Managing the customer relationship, making
    environmental corrections, and managing the cost
    of corporate assets

6
1.2 Data Warehouse Characteristics
  • It is a database designed for analytical tasks,
    using data from multiple applications
  • It supports a relatively small number of users
    with relatively long interactions
  • Its usage is read-intensive
  • Its content is periodically updated

7
1.2 Data Warehouse Characteristics (contd)
  • It contains current and historical data to
    provide a historical perspective of information
  • It contains a few large tables
  • Each query frequently results in a large result
    set and involves frequent full table scan and
    multi-table joins

8
1.3 Datawarehousing
  • The Processing of constructing and using data
    warehouses

Heterogeneous Data Sources
Data Cleaning
Data Integration And Consolidation
Interactive Analysis
Making Strategic Decisions
Constructing Data warehouse
Using Data Warehouse
9
1.4 Three-tier System Architecture
10
2. Data Warehouse Theory
11
2.1 Data Warehouse Theory
  • Why not use Database directly ?
  • The update-driven approach is inefficient.
  • Potentially expensive for frequent queries.
  • Use Data warehouse instead
  • The query-driven approach is enough for making
    strategic decisions.
  • Separate the operational DBMS for daily and
    critical operations.

12
2.2 Data Cube
  • A multidimensional, logical view of the data
  • Concept hierarchy
  • Multiple data granularity ????????
  • Data summarization ????
  • Data generalization ?????

13
  • A 3-dimension Data Cube

14
  • Drill-down on time data for Q1
  • Roll-up on address

15
  • Adding a dimension supplier

16
2.3 Efficient Data Cube Computation
  • The challenges 2N combinations
  • Concept hierarchy and Aggregations
  • makes it more complicated !
  • Materialization of data cube ????
  • Materialize every, none, or some ?
  • Algorithms for selection
  • Based on size
  • Based on sharing,
  • Based on access frequency.

17
2.4 On-Line Analytical Processing (OLAP)
  • Fast on-line processing of data cubes or
    multi-dimensional databases
  • OLAP operations
  • Drilling
  • Pivoting ????
  • Slicing and Dicing
  • Filtering, etc.

18
2.4 On-Line Analytical Processing (Contd)
  • A multidimensional, logical view of the data.
  • Interactive analysis of the data (drill, pivot,
    slice_dice, filter) and Quick response to OLAP
    queries.
  • Summarization and aggregations at every dimension
    intersection.
  • Retrieval and display of data in 2-D or 3-D
    cross-tabs, charts, and graphs, with easy
    pivoting of the axes.
  • Analytical modeling deriving ratios, variance,
    etc. and involving data across many dimensions.
  • Forecasting, trend analysis, and statistical
    analysis.

19
3. System Feature
20
3.1 Data sources supported
  • ODBC-compatible DBMS
  • Oracle, Microsoft SQL, MySQL, IBM DB2, etc.
  • Files
  • MS Access, MS Excel, etc.
  • Text files (CSV-format)

21
3.2 Data Cleansing ????
  • Database schema translation
  • Field selection and mapping
  • Field re-naming
  • Field aggregating and deriving
  • Data filtering
  • Data value conversion
  • Data value mapping
  • Data value function
  • Date value conversion and decomposition

22
3.3 Building of Data Cube
  • Support for multi-dimension data
  • Support for concept hierarchy

23
3.5 Interactive Front-end Tools
  • User-defined multi-dimension
  • User-defined dimension hierarchy
  • User-defined data granularity
  • Real-time graph capabilities
  • Bar chart
  • Pie chart
  • Line chart

24
3.6 Other features
  • Web-based OLAP GUI
  • Easy to access from Internet
  • Easy to integrated with other systems
  • Import / Export capability

25
4. Demo
26
5. Discussions
27
5.1 Roadmap
  • Integrated with Data mining
  • Major Group / Sales Analysis ????
  • Prospects Analysis and Forecast ?????????
  • Association of Customers and Sales ????
  • Market Segment Recommendation ????
  • Other Business Intelligence application
  • Integrated to e-Marketing
  • 1-to-1 Personalization Recommendation ?????
  • Target marketing ????
  • Loyalty program ???????
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