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 specificationEase in rule maintenance

CoSent:An Active Database Technologies

Trigger with high-level rules containingconceptual 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 termsMimic human cognitive expression

CoSent monitors temporal composition events and executes rules with conceptual and approximate terms.

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 automaticallyLeveraged on conventional DBMS (e.g., Oracle, Sybase, Teradata) triggering systemsRule definition is either specified by domain expert or derived by data mining technologies

Example of Rule Definitionswith 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.

Conventional vs. NaturalLanguage-Like Rules

Natural Language-Like RuleIf the weather turns bad,

then notify all affected units in that region and all those that are near to that region.

Conventional RuleIf wind_speed > MAX_WIND_SPEED and wave_height > MAX_WAVE_HEIGHT,

then notify affected units in regions.

Natural Language-LikeRule Specifications

Example 2If 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 1If 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.

Example: DoD Transportation Planning

Weather Report Table

Wind Speed(meters/second)

14.913.512.212

11.810.610.510108.37.98.17.77.1

Wave Height(meter)

3.33.13.12.62.82.32.72.52.52.32.222

1.8

Wind Speed(meter/second)

7.47.77

6.56.66.56.66.45.95.76

4.54

3.7

Wave Height(meter)

1.91.71.61.51.61.41.41.51.51.41.61.41.31.2

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

TAH ExampleWave Height

Wave Height[0.6, 7.2]

VERY LOW[0.6, 1.25]

LOW[1.25, 1.75]

HIGH[1.75, 2.45]

VERYHIGH

[2.45, 7.2]

A Portionof WaveHeightTAH

Triggering based on TemporalComposite 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

Low9-134.5

High134.5-208

Very Low53-134.5

Signt. Low9-53

Signt High162-208

Very High134.5-162

C-5 Filter Problem

Low0-53

High53-79

Very Low36-53

Extra. Low0-36

Ex High60-79

Very High53-60

Natural Language-LikeRule Translations

RuleDefinition

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

CoSent Architecture

TriggerAction(output)

Rule Parser

RelaxationEngine

TAHs

Rule Base

RuleManager

EventManager

ActionManager

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.)

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

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

Rules With Approximate Terms

Rules can contain approximate terms, such as near-by and approximate, thus ease in rule specificationThe Trigger can be installed on the desired location on a map by drag-and-drop methodThe near-by region affected by the bad weather condition is specified by the trigger condition shown by a red circle