In this lecture we look at...
[Section notes PDF 85Kb]

• Decision support systems (DSS)
• Duplicates of live systems, historical archiving
• Primarily read-only
• Load and refresh operations
• Integrity
• Assumptions about initial data
• Large, indexed, redundancy

• Design
• Logical
• Temporal keys, required to distinquish historical data (since:to
  current & during:within interval)
• Physical (Hash indexes, Bitmap indexes)
• Controlled Redundancy
• Synchronisation/update propogation
• Synchronous (update driven)
• Asynchronous (query driven)

• Extract
• pulling from live database system(s)
• Cleansing
• Transformation and Consolidation
• migrating from live or legacy system design
  
  to DSS design
• Load (DSS live/query-able)
• Refresh (latest update)

• Boolean expression complexity
• heavy WHERE clauses
• Join complexity
• Normalised databases, many tables
• Facts distributed across tables
• Joins required to answer complex questions
• Function and Analytic complexity
• Often require non-DBMS functions
• Smaller queries with interleaved code

• Specific example of DSS
• Subject-orientated
• e.g. customers/products
• Non-volatile
• once inserted, items cannot be updated
• Time variant
• Temporal keys
• Accuracy and granularity issues

• By example

• Consider product, customer, sales data
• Each sale represents a specific event
• when a product was purchased
• when a customer bought something
• when a sale was recorded
• Each can be thought of as an axis
  
  or dimension (3D)
• Each occurred at a moment in time (4D)

• Data centralised in ‘fact’ table
• Referencing creates star pattern
• Dimensions as satellite tables
• Normalising creates snowflake schema

• Hypercube is also a multi-processor topology inspired by a 4D shape
• Used by Intel’s iPSC/2
• Good at certain database operations
• e.g. Duplicate removal
• MIMD