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Datawarehouse & Data Mining

Datawarehouse & Data Mining. Data warehouse Characteristics. At one time, a huge amount of information may be queried as opposed to conventional DBMS that a typical query involves few records.

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Datawarehouse & Data Mining

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  1. Datawarehouse & Data Mining

  2. Data warehouse Characteristics • At one time, a huge amount of information may be queried as opposed to conventional DBMS that a typical query involves few records. • Data changes much more than operational data (in terms of new datatypes, new tables, etc.). DDL changes a lot. • Don’t work with real-time data but snapshots. • Historical data – Time is important • Frequently work with Terabytes of Data • Require different types of indexes and/or search engines. For example, bit-map indexing, or full table scan with partitioning. • Materialized Views are an important part. • Roll-up/Drill-down: Data is summarized with increasing generalization (weekly, quarterly, annually). • Fact Table x Dimension Table (Derived Table, Views, etc.) • Star Schema x Snow flake schema

  3. Typical Data Warehouse functions • Extract and Load • Clean and Transform • Backup and Arquive • Query Management

  4. GUIDELINES 1)      Start extracting data from data sources when it represents the same snapshot time as all other data sources. 2)      Do not execute consistency checks until all the data sources have been loaded into the temporary data store. 3)      Expect the effort required to clean up the source systems to increase exponentially with the number of overlapping data sources. 4)      Always assume that the amount of effort required to clean up data sources is substantially greater than you would expect. 5)      Consider dropping index prior to loading and recreate index afterwards. 6)      Determine what business activities require detailed transaction information. 7)      Read only in separate tablespaces from r/w. 8)      Separate your FACT data from your DIMENSION data. 9)      Consider Partitioning Data. If DBMS doesn’t support this, use each partition as a separate table and using a view that it is a union of all the tables.

  5. Generic Tools for Datawarehouse • Bitmap Index • Materialized Views and Snapshots • Partitioning Tables • 3rd party tools for optimizing SQL statements • Backup Database, Drop tables, Restore Database, re-build indexes.

  6. Extraction, Query and Analysis of Data Searches for patterns. Can use neural networks, statistics, etc. DW x Data Mining

  7. Datawarehouse Builder is a generic DW tool (for custom DW development) runs on top of Oracle 8i and Oracle 9i. OFA (Oracle Financial Analyzer) and Oracle Sales Analyzer are specific tool. Imports data from Oracle General Ledger to Express It runs on top of the Express Database Engine. Oracle Specific Tools for DW

  8. Oracle Data Mining Tool • Darwin: has its own database engine (doesn’t run on Oracle 8i).

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