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Event-Driven Rule-Based Reasoning using EYE
Ben De Meester
Dörthe Arndt, Pieter Bonte, Jabran Bhatti,
Wim Dereuddre, Ruben Verborgh, Femke Ongenae,
Filip De Turck, Erik Mannens, and Rik Van de Walle
University Ghent – iMinds – Multimedia Lab
ben.demeester@ugent.be | @Ben__DM
http://ceur-ws.org/Vol-1488/paper-08.pdf
OrdRing2015@ISWC | October 11th 2015 | Bethlehem, PA
We present
A nurse call system via reasoning.
Why via (rule) reasoning?
How is it done?
Where are we now?
eHealth Scenario
1. Call launched – select nurse + update call2. Call Redirect – select different nurse3. Call Temp. Accept – Update Call Status4. Corridor – Location update5. Patient location – update location, turn on lights and update nurse6. Presence on – update call, turn on lights & update nurse status7. Presence off – update call, turn off lights & update nurse status7. Presence off – update call, turn off lights & update nurse status8. Corridor – update location, turn off lights & update nurse status
A Nurse call system
Hospital, finding the most suitable nurse.
Most suitable?
Trust relationship
Competences
Location
Status
ACCIO
Healthcare ontology (DL)
How complex?
Assigning the ‘correct’ nurse can be complex
This complex
Filters in sequence
Correct competences
Decisions in sequence
Being closer and with a patient is more important than being
far away and free
Configurability
Every hospital is different
So, you want to assign a nurse?
Needs
Event-based
Nurses move, calls get made, …
Stateful
Keep current states of the nurses
Scalable
1 – 50 wards
Expressive
DL-ontology + complex decision trees
Reasoning techniques
OWL DL-reasoning + SPARQL
Con: Bad performance (e.g., location calculation for SPARQL)
Stream reasoning
Con: not enough expressitivity
OWL-RL + rule reasoning
Con: Not DL (but not necessary for this use case)
Pro: easy mapping from decision trees
Pro: all rules are executed at once
Pro: one system for everything
N3
{ this } => { that }
Turtle superset
Very expressive
that can be rules
built-ins (e.g. time predicates)
Datalog is not expressive enough
Forward and backward reasoning
EYE Reasoner
Performant
Prolog
EYE supports all built-in Prolog predicates
Rule reasoning
File-based
Use case analysis
Small portion dynamic data
Split up static from dynamic
State changes can introduce conflict
Programmatic update
Split up dynamic data
Static
Hospital layout (rooms, wards, logistics)
Ontologies
Rules
Dynamic
Nurse’s state
Nurse’s location
Active calls
Static Dynamic
Programmatic update
Nurse Erik goes from the hallway to room 1
event:
:nurseErik accio:location :room1
knowledge base
:nurseErik accio:location :hallway
Erik cannot be in two places at the same time
Timestamps are expensive
Fixed set of predicates
Architecture
Initializer
Decider
PostUpdater
PreUpdater
EventHandler
Architecture
Initializer (0)
(1) (6)
(2)
(3)
(4)
(5)
Decider
PostUpdater
PreUpdater
EventHandler
Performance improvements for EYE?
Preload the static data
Room location is known before, and doesn’t change
Nurse competences don’t change very day
Multiple parallel reasoning instances
Performance?
0
1000
2000
3000
4000
5000
6000
7000
0 10 20 30 40 50
ExecutionTime(ms)
#wards
Splitting / 1 EYE Splitting / 4 EYEs
No splitting / 1 EYE No splitting / 4 EYEs
Preloading improves IO
0
500
1000
1500
2000
2500
Preloading / 1
EYE
No preloading /
1 EYE
Preloading / 4
EYEs
No preloading /
4 EYEs
Reasoningtime(ms),50wards
reasoning reasoningIO
More consistent reasoning time with multiple parallel instances
0
5
10
15
20
25
30 0
0
15
15
15
15
40
40
50
50
50
50
65
65
65
65
65
90
90
125
ExecutionTime(s)
Time incoming event (s)
1 EYE 4 EYEs
Performance?
0
1000
2000
3000
4000
5000
6000
7000
0 10 20 30 40 50
ExecutionTime(ms)
#wards
Preloading / 1 EYE Preloading / 4 EYEs
No preloading / 1 EYE No preloading / 4 EYEs
Future performance?
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40 50
ReasoningTime(ms)
#wards
Preloading / 4 EYEs
Conclusions
Expressiveness comes at a cost…
But current system works for the current use case.
File-based reasoning is a bottleneck.
next: in-memory reasoning
http://ceur-ws.org/Vol-1488/paper-08.pdf
Our project
ORCA
Our pilot project partners:

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OrdRing2015 - Event-Driven Rule-based Reasoning using EYE

Editor's Notes

  1. Next: running nurse
  2. Next: complex decision tree
  3. Loose completeness, but not important for this use case (pragmatic)