1. CoSent: An Active Data Base Technology Natural language-like rule supports conceptual & approximate terms Decompose natural language-like rule to low level rules via knowledge based (TAH) Mimic human cognitive process and thus ease in rule specification Ease in rule maintenance

2. CoSent: An Active Database Technologies Trigger with high-level rules containing conceptual term (e.g., bad, heavy) and approximate operators (e.g., similar-to, near-to, approximate) Allow trigger conditions to be specified with fuzzy and conceptual terms Mimic human cognitive expression CoSent monitors temporal composition events and executes rules with conceptual and approximate terms.

3. Key Features of CoSent User defined rules transformed into low-level range values via knowledge base--Type Abstraction Hierarchies (TAHs) TAHs are typically generated from data sources automatically Leveraged on conventional DBMS (e.g., Oracle, Sybase, Teradata) triggering systems Rule definition is either specified by domain expert or derived by data mining technologies

4. Example of Rule Definitions with Data Mining Technology Find attributes that frequently appear together for a given target attribute. If bad road condition and also bad weather, then cause traffic congestion. If a person wrote many bad checks and also has past eviction, then this person is a poor credit risk. Based on the frequency of occurrence, the derived rules can be ranked according to certain information measure.

5. Conventional vs. Natural Language-Like Rules Natural Language-Like Rule If the weather turns bad, then notify all affected units in that region and all those that are near to that region. Conventional Rule If wind_speed > MAX_WIND_SPEED and wave_height > MAX_WAVE_HEIGHT, then notify affected units in regions.

6. Natural Language-Like Rule Specifications Example 2 If the aircraft has a fuel contamination problem and the aircraft type is similar-to‘C- 5’ based on the fuel type and fueling method, then notify the authority Example 1 If the number of departures of large cargo carrier (e.g., C-5, C-141) becomes significantly low in the past seven days, notify the Air Mobility Command.

7. Example: DoD Transportation Planning Weather Report Table Wind Speed (meters/second) 14.9 13.5 12.2 12 11.8 10.6 10.5 10 8.3 7.9 8.1 7.7 7.1 Wave Height (meter) 3.3 3.1 2.6 2.8 2.3 2.7 2.5 2.3 2.2 2 1.8 Wind Speed (meter/second) 7.4 7.7 7 6.5 6.6 6.5 6.6 6.4 5.9 5.7 6 4.5 4 3.7 Wave Height (meter) 1.9 1.7 1.6 1.5 1.6 1.4 1.5 1.4 1.6 1.4 1.3 1.2 Wind Speed is the hourly average over an eight-minute period for buoys and a two- minute period for land stations Wave height is sampled in a 20-minute period

8. TAH Example Wave Height Wave Height [0.6, 7.2] VERY LOW [0.6, 1.25] LOW [1.25, 1.75] HIGH [1.75, 2.45] VERY HIGH [2.45, 7.2]

9. A Portion of Wave Height TAH

10. Triggering based on Temporal Composite Events Notify the commander if within the past seven days, the total departure of C-5 is significantly low and the filter problem on C-5 is extremely high. C-5 Departure Low 9-134.5 High 134.5-208 Very Low 53-134.5 Signt. Low 9-53 Signt High 162-208 Very High 134.5-162 C-5 Filter Problem Low 0-53 High 53-79 Very Low 36-53 Extra. Low 0-36 Ex High 60-79 Very High 53-60

11. Natural Language-Like Rule Translations Rule Definition TAH Conventional triggering system (e.g.,Oracle, Sybase,Teradata) Low-level rules Natural Language-Like Rules Rule Parser Rule Rep Rule Decomposer Rule Translator Rule Translation/Relaxation

12. CoSent Architecture Trigger Action (output) Rule Parser Relaxation Engine TAHs Rule Base Rule Manager Event Manager Action Manager Natural Language-Like Rule Composite Event Specification and Notification CoSent Server (input) (input/output) Rule Translation/ Relaxation Commercial relational database systems (e.g., Oracle, Sybase, Teradata, etc.)

13. CoSent Demo Natural Language-like rule with conceptual terms :“very high wave height” and ”very strong wind speed” Natural language-like rule with approximate term “nearby” and conceptual term “bad weather” Install trigger by drag-and-drop on the desired location on the map

14. Natural Language-Like Rule Natural language-like rule containing conceptual terms, such as wave_height = “very- high” and wind_speed = “very-strong”, can be translated to range values by domain knowledge. For instance, type abstraction hierarchy. Natural language-like rules reduce the number of rules, thus easing rule maintenance

20. Rules With Approximate Terms Rules can contain approximate terms, such as near-by and approximate, thus ease in rule specification The Trigger can be installed on the desired location on a map by drag-and-drop method The near-by region affected by the bad weather condition is specified by the trigger condition shown by a red circle