1. Using Shapes of Trends in Active Data Mining Duy Lam Norris Boothe

2. Shape Querying and Active Data Mining Historical time sequences make up a large portion of data stored in computers Historical time sequences make up a large portion of data stored in computers Mining trends in histories useful Mining trends in histories useful Many applications, including observing trends in stock prices, online bids, and rule mining Many applications, including observing trends in stock prices, online bids, and rule mining

3. Overview Overview of SDL Overview of SDL SDL language SDL language Applications to data mining Applications to data mining

4. A (Very) Simple History

5. Shape Definition Language SDL is a shape definition language used to query the “shapes” of histories SDL is a shape definition language used to query the “shapes” of histories Small, powerful language that allows “blurry” matching Small, powerful language that allows “blurry” matching Designed to make it easy and natural to query Designed to make it easy and natural to query Easily implementable Easily implementable Little non-determinism Little non-determinism

6. Alphabet SDL allows you to specify an “alphabet” defining transitions SDL allows you to specify an “alphabet” defining transitions Example: Example: SymbolDescription up Slightly increasing transition Up Highly increasing transition down Slightly decreasing transition Down Highly decreasing transition appears Transition from zero to non-zero disappears Transition from non-zero to zero stable The final value nearly equal to initial value zero Both initial and final value are zero

7. So with this alphabet we can describe a shape So with this alphabet we can describe a shape Use such a description to query a history to produce all subsequences that match the shape Use such a description to query a history to produce all subsequences that match the shape Describing a shape (shape name(parameters) descriptor)

8. (shape spike() (concat Up up down Down))

9. Derived Shapes any any allows a shape to have multiple values allows a shape to have multiple values concat concat shapes can be concatenated together contiguously shapes can be concatenated together contiguously (any up Up) (concat down up down up)

10. Multiple Occurrence Operators Shapes made of multiple contiguous occurrences of the same shape Shapes made of multiple contiguous occurrences of the same shape Resulting subsequences are such that they are neither preceded nor followed by a subsequence that matches P Resulting subsequences are such that they are neither preceded nor followed by a subsequence that matches P (exact 5 (any up Up)) (atleast 3 stable) (atmost 2 (concat disappear appear))

11. Bounded Occurrence Operators in in permits “blurry” matching by allowing users to state an overall shape without specific details permits “blurry” matching by allowing users to state an overall shape without specific details within the specified time period length, we can have a specified number of occurrences of a shape within the specified time period length, we can have a specified number of occurrences of a shape can have arbitrary gaps and can have overlap can have arbitrary gaps and can have overlap (in 7 (nomore 5 up)) (precisely n P) (noless n P) (nomore n P)

12. Bounded Occurrence Operators inorder inorder specifies shapes that must appear in a specific order specifies shapes that must appear in a specific order (inorder P 1 P 2... P n )

13. (in 5 (and (noless 2 (any up Up)) (nomore 1 (any down Down)))) Shape Definition Examples

14. (in 7 (inorder (atleast 2 (any up Up)) (in 4 (noless 3 (any down Down))))))

15. Parameterized Shapes Can parameterize shape definitions instead of using concrete values Can parameterize shape definitions instead of using concrete values (shape spike(upcnt dncnt) (concat (exact upcnt (any up Up)) (exact dncnt (any down Down)))) (shape doublepeak(width ht1 ht2) (in width (inorder spike(ht1 ht1) spike(ht2 ht2))))

16. Advantages of SDL natural and powerful language for expressing shape queries natural and powerful language for expressing shape queries capability of blurry matching capability of blurry matching reduction of output clutter reduction of output clutter efficient implementation efficient implementation

17. SDL’s Expressive Power SDL is equivalent to regular expressions for regular matching SDL is equivalent to regular expressions for regular matching several features enchance its effectivesness, however several features enchance its effectivesness, however greedy matching and “lookahead” capabilities help reduce output clutter greedy matching and “lookahead” capabilities help reduce output clutter

18. SDL’s Expressive Power “blurry” matching enables a much more natural and compact specification of certain shapes “blurry” matching enables a much more natural and compact specification of certain shapes For example, if we wanted precisely one occurrence of each a i in any order For example, if we wanted precisely one occurrence of each a i in any order in SDL: in SDL: regular expressions requires at least exponential size to specify! regular expressions requires at least exponential size to specify! (and (precisely 1 a 1 ) (precisely 1 a 2 )... (precisely 1 a n ))

19. SDL Summary SDL is a small, powerful language for naturally and intuitively expressing shapes found in histories SDL is a small, powerful language for naturally and intuitively expressing shapes found in histories Equivalent in power to regular expressions, but much more effective Equivalent in power to regular expressions, but much more effective Permits “blurry” matching Permits “blurry” matching

20. Using SDL in Active Data Mining

21. Static Data Mining Discovery of rules for Discovery of rules for Associations Associations Sequences Sequences Classification Classification Entire data set is mined Entire data set is mined Inherent weakness: Rules are not static Inherent weakness: Rules are not static

22. Active Data Mining Partition into time periods Partition into time periods Run data mining algorithm on each period Run data mining algorithm on each period Gather rules into a ‘rulebase’ Gather rules into a ‘rulebase’ Create triggers to discover Create triggers to discover Trends in rules Trends in rules Associations between rules Associations between rules

23. Period 3 Rules Active Data Mining Process Period 1 Rules Large Data Base Rule ID History (support, confidence, etc) 12…n 1 2 … Period 2 Rules

24. Selected Rules Active Data Mining Process (cont). Rule ID History (support, confidence, etc) 12…n 1 2 … Shape Definition Language Trigger Definition Language Active Data Mining

25. Active Data Mining Components Shape definitions (SDL) Shape definitions (SDL) (shape name(parameters) descriptor) (shape name(parameters) descriptor) Ex: Ex: (shape spike(upcnt dncnt) (concat (atleast upcnt (any up Up)) (concat (atleast upcnt (any up Up)) (atleast dncnt (any down Down)))) (atleast dncnt (any down Down)))) Queries Queries Triggers Triggers

26. Queries For rule selection For rule selection Syntax: Syntax: (query (shape (history-name start-time end-time))) (query (shape (history-name start-time end-time))) ‘start’ and ‘end’ specify the end points of history ‘start’ and ‘end’ specify the end points of history Result: rules that match the desired shape Result: rules that match the desired shape Ex: (shape ramp() (concat Up Up)) Ex: (shape ramp() (concat Up Up)) (query (ramp() (confidence start end)))

27. (shape upramp(len cnt) (in len (noless cnt (any up Up)))) (shape dnramp(len cnt) (in len (noless cnt (any down Down)))) (query (and (upramp(5 3) (support start 10)) (dnramp(5 3) (confidence start 10)))) Larger Query Example Results: rules where support is increasing but confidence is decreasing

28. Triggers Datastream type functionality Datastream type functionality ECA (Event Condition Action) model used (Chakravarthy et al. 1989) ECA (Event Condition Action) model used (Chakravarthy et al. 1989) Syntax: Syntax: (trigger trigger-name (trigger trigger-name (events events-spec) (condition (shape history-spec)) (actions action-spec)) Events: Events: Rule creation Rule creation History updates History updates

29. Wave Execution Semantics Stratified execution of triggers – similar to Datalog Stratified execution of triggers – similar to Datalog Set of Events Triggers for those Events Queries for those Triggers Set of Actions/ Events

30. Trigger Example Identifying rules where support is increasing, but confidence is decreasing Identifying rules where support is increasing, but confidence is decreasing (trigger detect_up (events updatehistory) (condition (upramp 5 4) (support (- end 5) end))) (actions upward)) (trigger detect_dn (events upward) (condition (dnramp 5 4) (confidence (- end 5) end))) (actions notify))

31. Implementation Implemented on AIX system Implemented on AIX system Part of IBM’s Quest project Part of IBM’s Quest project Successfully tested: Successfully tested: Large set (5 years) of mail order data (2.9 million records) Large set (5 years) of mail order data (2.9 million records) Large set (3 years) of POS (point-of-sale) transactions (6.8 million records) Large set (3 years) of POS (point-of-sale) transactions (6.8 million records)

32. Future Work At time of paper… At time of paper… Integrate constructs into a SQL relational system Integrate constructs into a SQL relational system Improve incremental computations using partial results of current trigger queries Improve incremental computations using partial results of current trigger queries Since then… Since then… Integrated into the Quest Data Mining System Integrated into the Quest Data Mining System Subsumed into IBM’s data mining products, including Intelligent Miner Subsumed into IBM’s data mining products, including Intelligent Miner Referenced for work in Active Data Mining and “blurry” pattern matching Referenced for work in Active Data Mining and “blurry” pattern matching

33. References “Querying Shapes of Histories”, by Rakesh Agrawal, Giuseppe Psaila, Edward L. Wimmers, and Mohamed Zait of the IBM Almden Research Center, 1995 “Querying Shapes of Histories”, by Rakesh Agrawal, Giuseppe Psaila, Edward L. Wimmers, and Mohamed Zait of the IBM Almden Research Center, 1995 “Active Data Mining”, by Rakesh Agrawal and Giuseppe Psaila of the IBM Almden Research Center, 1995 “Active Data Mining”, by Rakesh Agrawal and Giuseppe Psaila of the IBM Almden Research Center, 1995 “The Quest Data Mining System”, by Rakesh Agrawal, Manish Mehta, John Shafer, and Ramakrishnan Srikant of the IBM Almden Research Center in coordination with Andreas Arning and Toni Bollinger of the IBM German Software Laboratory, 1996 “The Quest Data Mining System”, by Rakesh Agrawal, Manish Mehta, John Shafer, and Ramakrishnan Srikant of the IBM Almden Research Center in coordination with Andreas Arning and Toni Bollinger of the IBM German Software Laboratory, 1996 IBM Almden Research Center Website: http://www.almaden.ibm.com/software/quest/ IBM Almden Research Center Website: http://www.almaden.ibm.com/software/quest/ http://www.almaden.ibm.com/software/quest/