Abstract:
본 발명은 웹 기반 개방형 IPTV 환경에서 사용자 참여의 활성화를 위한 의미론 기반 소셜 네트워크를 이용한 IPTV 커뮤니티 추천 및 구성 프레임워크에 관한 것이다. 이를 위하여 본 발명은 사용자의 개인적 성향과 사회적 특성에 대한 정보가 저장되는 데이터베이스; 상기 사회적 특성에 대한 정보를 기초로 상기 사용자 중심의 인적 네트워크를 구성하는 관계 도출부; 및 상기 사용자 중심의 인적 네트워크와 상기 개인적 성향을 기초로 사용자에게 적합한 커뮤니티를 검색하여 추천하는 커뮤니티 추천부를 포함한다. IPTV, 웹 기반, 사용자 참여, 소셜, 네트워크, 커뮤니티
Abstract:
PURPOSE: A vector modelling for user task support for improving a task performance, a task predicting system and a method based on a behavior pattern in ubiquitous computing environment are provided to prepare performance of a task possessively in advance by predicting a suitable next task from a task ontology based on meaning through a task prediction model. CONSTITUTION: A human behavior research unit(11) acquires behavior data. A task ontology storage(13) stores a task ontology. A user behavior pattern extractor extracts behavior patterns of users. A domain professional unit stores analysis result of behavior patterns in a user behavior pattern storage(16). A context information manager(17) collects context information about actions. A task broker(18) matches the contents and the task ontology of the user behavior pattern storage.
Abstract:
본 발명에 따른 위한 베이지안 네트워크 구축방법은 질병분석을 위한 데이터 베이스를 구축하는 방법에 있어서, (a)질병, 증상, 치료법을 포함하는 U-Health 정보를 분석하여 서비스 제공에 필요한 복수의 U-Health 온톨로지를 구축하는 과정과, (b)구축된 상기 U-Health 온톨로지 사이의 원인 및 결과 관계에 대한 메타 모델을 설정하는 과정과, (c)복수의 U-Health 온톨로지 중, 적어도 둘 이상의 특정 U-Health 온톨로지를 선택하여 노드로 설정하고, 설정된 상기 노드들에 메타모델을 적용하여 소정의 베이지안 네트워크를 생성하는 과정을 포함한다. 온톨로지, 베이지안 네트워크, 질병, 분석, 추론
Abstract:
PURPOSE: A method for detecting a fault using in interaction pattern based on software is provided to minimize a performance deterioration by comparing the important features of each message. CONSTITUTION: An interaction pattern is determined in order to establish an interaction between software components by pattern unit(S101). The determined interaction pattern is checked(S102). A software defection is detected based on an identified interaction pattern(S103).
Abstract:
PURPOSE: A device and a method for recommended a semantic social network-based community are provided to recommend a specific community based on factors for grasping a personal tendency and a social feature of a user. CONSTITUTION: A database(101) stores information about a personal tendency and a social feature of a user. A relation deriving unit(102) configures a human network for the user based on the information for the social feature. A community recommending unit(103) recommends a community suitable for the user based on the human network and the personal tendency for the user.
Abstract:
PURPOSE: A method for recommending tasks in an RFID system is provided to recommend a task that a user can perform together with people who have similar tendency to the user. CONSTITUTION: A communication terminal entering an RFID system is recognized using an RFID(Radio Frequency IDentification). A preferred task list registered in the communication terminal is searched(S300). Tasks executable in the RFID system are selected among the preferred task list in the RFID system(S302). Tasks suitable for spatial characteristic information of the RFID system are selected among the selected tasks(S304). A recommendation task list of the selected tasks is provided to the communication terminal(S324).
Abstract:
A method for building a database and a method for analyzing disease using the same database are provided to offer a reliable and rapid U-Health service by using a suitable Bayesian network for a disease analysis. A method for building a database and a method for analyzing disease using the same database are comprised of the followings. An ontology is comprised through a communication between a user terminal and ontology administration unit(10). A meta model is made up through a communication between a user terminal and a meta model administration unit(20). An abstraction level is set(30). A Bayesian network node is generated(40). A Bayesian network link is formed(50). A specific disease is diagnosed by using an U-Health ontology, a Bayesian network, and a relay ontology(60).