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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.

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Presentation on theme: "User Care Preference-based Service Discovery in a Ubiquitous Environments Dongpil Kwak, Joongsoo Lee, Dohyun Kim, and Younghee Lee Talk by Joongsoo Lee."— Presentation transcript:

1 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 2 Introduction Ubiquitous or Pervasive Computing Environments Networking everywhere Providing convenient environment according to humans context while minimizing humans 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 users context

3 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 Users ContextPreference Big screen in Room B Laptop Cell phone Speaker in Room B Laptop Service discoveredCandidate services Service filtering

4 4 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 5 Wearable Sensors in Future A figure from NTT Docomo [Hirotaka 03]

6 6 Overall Architecture A user experiencing specific symptom make a service discovery request to know sensitive services or Trigger

7 7 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

8 8 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

9 9 User care preference policy

10 10 Service Ontology based on Functional Effect

11 11 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

12 12 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)= -( ) = Basic probability assignment (negativeSight)= -( ) = 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

13 13 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 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 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 16 Summary Conclusion This work deals with situation when service discovery is executed according to users condition Classified service query Design for matching with classified query and service Expected to increase the satisfaction of users Make richer to represent users requirement Increase service matching Applied to Healthcare, User Preference based Service Discovery Future work More study on human sense & modeling


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