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User Care Preference-based Service Discovery in a Ubiquitous Environments
Dongpil Kwak, Joongsoo Lee, Dohyun Kim, and Younghee Lee
Talk by Joongsoo Lee
Information and Communications Univ,
Daejeon, Korea
2
Introduction
• Ubiquitous or Pervasive Computing Environments
• Networking everywhere
• Providing convenient environment according to human’s context while
minimizing human’s intervention
• Semantic service discovery, context management, and inference engine
are important building blocks
• Service Discovery in Ubiquitous Environments
• Service requester is human or a proxy device
• It can be occurred in background
• Most appropriate service should be returned or the services should be
ranked based on user’s context
3
Introduction
• Context and user preference
• As an example, messaging application
In front of a laptop
Residing in “Room B”
Attends a meeting
Prefers visible interface
Prefers bigger screen
User’s Context Preference
Big screen in Room B
Laptop
Cell phone
Speaker in Room B
Laptop
Service discoveredCandidate services
Servicefiltering
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Problem Identification
• Questions in mind
• How to set up user preference?
• How to valuate user preference?
• How to retrieve appropriate service
based on user preference?
• We are focusing on user care applications
• Such as healthcare services
• Using the five senses (sight, hearing, taste, smell, touch)
Prefers visible interface
Prefers bigger screen
Preference
5
Wearable Sensors in Future
A figure from NTT Docomo [Hirotaka ’03]
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Overall Architecture
• A user experiencing specific symptom make a service discovery
request to know sensitive services
or
Trigger
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Service Discovery Procedure
Service matching with service ontology
User care preference * service effect
Preprocessing knowledge set
Service ranking & retrieval
Service Query Alarming
TV, FM radio, clock, light
Hearing effect -Sight effect +
Light, TV
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User care preference policy
• Preference setting• Using medical database and IR techniques• Multiple effects
• To match factor of service
• Ex) highly quiet preferred and leave in peace sense of sound (-), sense of sight and sound (-)
• Weight values
• To measure the degree of relativity with functional effects of service
• Ex) highly quiet preferred and avoid blue the term ‘highly’ indicating weight
• Each capacity of effect is independent one another
• -1 <= each sense of user care preference <= 1
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User care preference policy
10
Service Ontology based on Functional Effect
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Service Matching
• User care preference (Hearing, Sight)=(0.7, 0.5)• Consider uncertainty of weighted value (Smoothing with Error rate)
• Ex) 15% error rate => (0.7, 0.5, 0.18) , 0% => weighted sum applied
• Normalization User care preference (hearing, sight, uncertain)=(0.51, 0.36, 0.13)
• Service (Hearing, Sight, Touch)=(0.6, 0.3, 0.4)• Each value is the degree of satisfaction among elements of evidence
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Service Matching
Sound (0.51) Sight (0.36) Uncertain (0.13)
Sound (0.6) Sound (0.31) Conflict(0.21) Sound (0.08)
Sight (0.3) Conflict(0.15) Sight (0.11) Sight (0.04)
Touch (0.4) Conflict(0.2) Conflict (0.15) Touch (0.05)Basic probability assignment (negativeSound)= -(0.31+0.08) = -
0.39 Basic probability assignment (negativeSight)= -(0.11 + 0.04) = -
0.15 Basic probability assignment (touch)=0.05
UserCarePreference(Headache)= UserCarePreference(negativeSound, negativeSight, Uncertain)= UserCarePreference(-(0.51), -(0.36), 0.13)
Service(CellPhone)= Service(Sound, Sight, Touch)= Service(0.6, 0.3, 0.4)
Relativity(CellPhone)= Relativity(negativeSound, negativeSight, Tough)= Relativity(-(0.39), -(0.15), 0.05)= -0.49
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Implementation
• Mobile object
• Location recognition and symptom perception
• Medical symptom analyzer in context manager
• Symptom analysis
• Service matching module in service manager
• Measurement of relative degree with services
14
Symptom analysis
• Medical symptom follows
predefined rules to increase
precision
• Three rules are applied for
symptom analysis
• Key terms such as ‘cold’ and ‘hot’
are picked up to compare
‘sense=touch state =negative’ and
‘sense=touch state=positive’ in
keyword table
• It picks up negative terms that
cause a reserve of meaning such as
‘not’, ‘avoid’ et al.
• It picks up terms that means weight
such as ‘highly’, ‘relatively’ et al.
15
Service matching design
• Functional effects of service are
described in abstraction by
ontology
• Functional effects are classified by
five senses
• Its functional effect consists of
service action
• Service action consists of services
16
Summary
• Conclusion
• This work deals with situation when service discovery is executed
according to user’s condition
• Classified service query
• Design for matching with classified query and service
• Expected to increase the satisfaction of users
• Make richer to represent user’s requirement
• Increase service matching
• Applied to Healthcare, User Preference based Service Discovery
• Future work
• More study on human sense & modeling