Transcript CoSent_2000

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
CoSent:
An Active Database Technologies
CoSent monitors temporal composition events
and executes rules with conceptual and
approximate terms.
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
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
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.
Conventional vs. Natural
Language-Like Rules
Conventional Rule
If wind_speed > MAX_WIND_SPEED and
wave_height > MAX_WAVE_HEIGHT,
then notify affected units in regions.
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.
Natural Language-Like
Rule Specifications
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.
Example 2
If the aircraft has a fuel contamination
problem and the aircraft type is similar-to‘C5’ based on the fuel type and fueling method,
then notify the authority
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
10
8.3
7.9
8.1
7.7
7.1
Wave Height
(meter)
3.3
3.1
3.1
2.6
2.8
2.3
2.7
2.5
2.5
2.3
2.2
2
2
1.8
Wind Speed is the
hourly average over an
eight-minute period for
buoys and a twominute period for land
stations
Wave height is
sampled in a 20-minute
period
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
1.4
1.5
1.5
1.4
1.6
1.4
1.3
1.2
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]
A
Portion
of
Wave
Height
TAH
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
Signt. Low Very Low Very High Signt High
9-53
53-134.5 134.5-162 162-208
C-5 Filter Problem
Low
0-53
High
53-79
Extra. Low Very Low Very High
0-36
53-60
36-53
Ex High
60-79
Natural Language-Like
Rule Translations
Natural Language-Like Rules
Rule Parser
Rule
Definition
Rule Rep
Conventional triggering
system (e.g.,Oracle,
Sybase,Teradata)
Rule Decomposer
TAH
Rule Translator
Rule Translation/Relaxation
Low-level rules
CoSent Architecture
Natural Language-Like Rule
(input)
CoSent Server
TAHs
Rule Parser
Rule
Base
Relaxation
Engine
Rule
Manager
Rule Translation/
Relaxation
Action
Manager
Composite Event
Specification and Notification
Event
Manager
Commercial relational database
systems (e.g., Oracle, Sybase,
Teradata, etc.)
(input/output)
Trigger
Action
(output)
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 = “veryhigh” 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
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