1. Advanced Topics: Business Intelligence Features
Data Warehousing, Materialized View, Partitioning, SQL Analytics

2. Data Warehousing Purpose: Query and Analysis of Data
Usually contains historical data derived from transaction data (OnLine Trans. Processing or OLTP)
OLAP (OnLine Analytical Processing)
Enable Fast execution of ad-hoc queries

3. Data Warehouse Architecture
Data Mart: small, specialized DW for a dept or so
DW Purpose: Enable analysis using Unified Data

4. OLAP example
Assume one (real-valued) measure value eg Sales Amount and several finite dimensional attributes e.g. Item, Place, Month.
Example fact: “Iced Tea was sold in Auckland in January”. Measure: $20k
Maybe no fact about “Iced Tea in Auckland in August”.
Mapping Item×Place×Time  Sales Amount called a cube. (Think “array”.)
Answer queries based on dimensions time, place group by month, country or year, state, etc.)
All possible combinations need to be fast.

5. Logical Design: Star Schema
Central “Fact” table storing “measure” attributes such as “sales”
Dimension tables store information about the dimension attributes
Queries: Find “sales” by product, region, customers, time (year),..

6. Dimensions Hierarchy of attributes
Analysis based on any level in hierarchy
sales on region, subregion, country_name,…

7. Logical Design: Snowflake Schema
Snowflake: complex improvisation of star.
Products: divided into categories and suppliers (more normalized)
Queries: will be slower

8. Physical Design Tables: Can be regular tables or Partitioned Tables
Indexes: Specialized indexes like bitmap
Additional Objects: Materialized views, Dimensions

9. Partitioned Tables Behave like regular tables
Query on entire table or on a specific partition
Parallelism on multiple partitions
Can load/modify multiple partitions concurrently

10. Query & Analysis Functions
All SQL analysis functions
SELECT SUM(amout_sold), p.pname
FROM sales s, products p, times t
WHERE s.product_id = p.product_id
GROUP BY p.pname;

11. More SQL Analytic Functions
Conversion Functions
ASCIISTR CAST CHARTOROWID COMPOSE CONVERT DECOMPOSE HEXTORAW NUMTODSINTERVAL NUMTOYMINTERVAL RAWTOHEX RAWTONHEX ROWIDTOCHAR ROWIDTONCHAR SCN_TO_TIMESTAMP TIMESTAMP_TO_SCN TO_BINARY_DOUBLE TO_BINARY_FLOAT TO_CHAR (character) TO_CHAR (datetime) TO_CHAR (number) TO_CLOB TO_DATE TO_DSINTERVAL TO_LOB TO_MULTI_BYTE TO_NCHAR (character) TO_NCHAR (datetime) TO_NCHAR (number) TO_NCLOB TO_NUMBER ,…
NUMERIC ABS ACOS ASIN ATAN ATAN2 BITAND CEIL COS COSH EXP FLOOR LN LOG MOD NANVL POWER REMAINDER ROUND (number) SIGN SIN SINH SQRT TAN TANH TRUNC (number) WIDTH_BUCKET
CHARACATER CHR CONCAT INITCAP LOWER LPAD LTRIM NLS_INITCAP NLS_LOWER NLSSORT NLS_UPPER REGEXP_REPLACE REGEXP_SUBSTR REPLACE RPAD RTRIM SOUNDEX SUBSTR TRANSLATE TREAT TRIM UPPER
DATE ADD_MONTHS CURRENT_DATE CURRENT_TIMESTAMP DBTIMEZONE EXTRACT (datetime) FROM_TZ LAST_DAY LOCALTIMESTAMP MONTHS_BETWEEN NEW_TIME NEXT_DAY NUMTODSINTERVAL NUMTOYMINTERVAL ROUND (date) SESSIONTIMEZONE SYS_EXTRACT_UTC SYSDATE SYSTIMESTAMP TO_CHAR (datetime) TO_TIMESTAMP TO_TIMESTAMP_TZ TO_DSINTERVAL TO_YMINTERVAL TRUNC (date) TZ_OFFSET

12. Analytic Functions
AVG, CORR, COVAR_SAMP, COVAR_POP, COUNT, CUME_DIST, DENSE_RANK, FIRST, LAST, LAG, LEAD, MAX, MIN, PERCENTILE_RANK, RANK, ROW_NUMBER, STDDEV, SUM, VARIANCE, …

13. Materialized Views Queries joining fact and dimension tables
Precompute the join and store as Materialized Views
Denormalized and so not in 3NF
Queries are automatically ‘rewritten’ using Materialized view
Materialized views
Facilitate fast execution of queries

14. Summary Fast Query and Analysis
Denormalization into Materialized Views
Queries implicitly ‘rewritten’ under the covers
Research issues:
What queries can be rewritten using the MV
Pushing the updates to the MVs
Refreshing the MVs