Oracle 12c Analytics New Features
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Transcript of Oracle 12c Analytics New Features
Global Maksimum Data & Information Technologies
• Complex Event Processing
• Oracle CEP
• Making hundred of different business decisions for millions of events in a second
• Advanced Analytics
• Oracle Data Mining
• Oracle R Enterprise
• Large scale data analytics
• Ten billion rows in a week
• Data Visualisation
• State of the art data visualisation
• DIY BI
Partitioning• Core Functionality
• Interval-REF Partitioning
• Performance
• Partition Maintenance on multiple partitions
• Partial local and global indexes
• Manageability
• Asynchronous global index maintenance for DROP/TRUNCATE
• Online partition MOVE
• Cascading TRUNCATE/EXCHANGE
Cost Based Optimiser• Adaptive Statistics
• Dynamic Sampling (LEVEL=11)
• Cardinality Feedback Enhancement
• Re-optimisation
• Histograms
• Better and Faster Statistics Gathering
• STATS_ON_LOAD: For CTAS and IAS on empty tables
• Session private statistics on GTT
• Concurrent statistics gathering
• Adaptive Plans
• Join methods
• Parallel distribution methods
Adaptive Query OptimisationSELECT product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND o.quantity > 1 AND p.product_id = o.product_id
NESTED LOOP HASH JOIN
Table scan order_items
Index scan prod_info_idx
Table scan product_information
Stats collector
threshold
Adaptive Query Optimisation• Join method decision deferred until runtime
• Default plan is computed using available statistics
• Alternate sub-plans are pre-computed and stored in the cursor
• Statistic collectors are inserted at key points in the plan
• Data distribution method can also be changed during execution.
Advanced Analytics• Oracle Advanced Analytics 12c
• New Data Mining Algorithms
• EM (Expectation Maximisation)
• SVD - PCA
• Predictive Queries
• Oracle Data Miner/SQL Developer 4.0
• New Graph Node (box,scatter, bar,histogram)
• SQL Query Node
• R Script Node
• Oracle Advanced Analytics/ORE 1.3
• Neural Networks
• Improved integration with OBIEE
Predictive QueriesSELECT cust_income_level, cust_id, Round(prob_anom, 2) prob_anom, Round(pctrank, 3) * 100 pct_rank FROM (SELECT cust_id, cust_income_level, prob_anom, Percent_rank() over( PARTITION BY cust_income_level ORDER BY prob_anom DESC) AS pct_rank FROM (SELECT cust_id, cust_income_level, Prediction_probability(OF ANOMALY,0 using *) over( PARTITION BY cust_income_level) prob_anom FROM customers)) WHERE pct_rank <= .05 ORDER BY cust_income_level, prob_anom desc
SQL Pattern MatchingX Y W Z
SELECT first_x, last_z FROM ticker MATCH_RECOGNIZE ( PARTITION BY name ORDER BY time MEASURES FIRST(x.time) AS first_x LAST(z.time) AS last_z ONE ROW PER MATCH PATTERN (X+ Y+ W+ Z+) DEFINE X AS (price < PREV(price)) Y AS (price > PREV(price)) W AS (price < PREV(price)) Z AS (price > PREV(price))
first_x last_z1 9
13 19
1 9 13 19
Automatic Data Optimisation (ADO)
Policy
Active/HotALTER TABLE sales ILM ADD row store compress advanced row after 2 DAYS OF NO_MODIFICATION;
Frequently AccessedALTER TABLE sales ILM ADD compress for query low after 7 DAYS OF NO_MODIFICATION;
Occasional Access ALTER TABLE sales ILM ADD TIER TO sata_tbs AFTER 1 MONTH OF NO ACCESS;
DormantALTER TABLE sales ILM ADD compress for archive high AFTER 7 MONTHS OF NO ACCESS;