CSE6011 Implementing a Warehouse  Monitoring: Sending data from sources  Integrating: Loading, cleansing,...  Processing: Query processing, indexing,...

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Presentation transcript:

CSE6011 Implementing a Warehouse  Monitoring: Sending data from sources  Integrating: Loading, cleansing,...  Processing: Query processing, indexing,...  Managing: Metadata, Design,...

CSE6012 Warehouse Maintenance  Warehouse data  materialized view  Initial loading  View maintenance  Derived Warehouse Data  indexes  aggregates  materialized views  View maintenance

CSE6013 Materialized Views  Define new warehouse relations using SQL expressions does not exist at any source

CSE6014 Differs from Conventional View Maintenance...  Warehouses may be highly aggregated and summarized  Warehouse views may be over history of base data  Process large batch updates  Schema may evolve

CSE6015 Differs from Conventional View Maintenance...  Base data doesn’t participate in view maintenance  Simply reports changes  Loosely coupled  Absence of locking, global transactions  May not be queriable

CSE6016 Processing  ROLAP servers vs. MOLAP servers  Index Structures  What to Materialize?  Algorithms Client Warehouse Source Query & Analysis Integration Metadata

CSE6017 ROLAP Server  Relational OLAP Server relational DBMS ROLAP server tools utilities Special indices, tuning; Schema is “denormalized”

CSE6018 MOLAP Server  Multi-Dimensional OLAP Server multidimensional server M.D. tools utilities could also sit on relational DBMS Product City Date milk soda eggs soap A B Sales

CSE6019 What to Materialize?  Store in warehouse results useful for common queries  Example: day 2 day total sales materialize

CSE60110 Cube Aggregates Lattice city, product, date city, productcity, dateproduct, date cityproductdate all day 2 day use greedy algorithm to decide what to materialize

CSE60111 Dimension Hierarchies all state city

CSE60112 Dimension Hierarchies city, product city, product, date city, date product, date city product date all state, product, date state, date state, product state not all arcs shown...

CSE60113 Interesting Hierarchy all years quarters months days weeks conceptual dimension table

CSE60114 Implementation of OLAP Server  ROLAP: relational OLAP – data are stored in tables in relational databases or extended- relational databases. They use an RDBMS to manage the warehouse data and aggregations using often a star schema.  They support extensions to SQL.  A cell in the multi-dimensional structure is represented by a tuple.  Advantage: scalable (no empty cells for sparse cube).  Disadvantage: no direct access to cells.

CSE60115 Implementation of OLAP Server  MOLAP: multidimensional OLAP – implements the multidimensional view by storing data in special multidimensional data structure (MDDS).  Advantage: fast indexing to pre-computed aggregations. Only values are stored.  Disadvantage: not very scalable and sparse.