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| A concept of "context-awareness" is suggested with ubiquitous computing. Context awareness technique is used for services that provide proper and intelligent application to the user by accepting context information according to the user’s environment. Context can be considered as a set of information that includes user’s activity, location, personal preferences and current status while the most widely accepted formal definition has been provided by Dey and Abowd: "Context is any information that can be used to characterize the situation of an entity. An entity can be a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves." In the previous study, we classify contexts into unitary and composite contexts. We define the unitary context is a basic building block that is not further dividable. We define the composite context information as enhanced high level context abstraction by integration or composing unitary context information related with multi-entities to decide or supply the service of system. In order to provide composite contextual service, multiple unitary contexts are combined. For example, suppose that a tourist is planning to visit some attraction site near his home. Then, visiting attraction site near home would be considered as composite context. To construct the composite context, the following various unitary contexts will be related to the composite context: weather, allowed time, distance to the site, budget, accompanying people, transportation, etc. Future context-aware service is expected to form such composite context information. While providing a usable context to users is important, it is also essential to reserve such a composite context even if it is not available. To support this composite context-awareness service, event-control-action (ECA) architecture can be used. The pattern matching algorithm which is used in the inference engine of the ECA is the most important part since it decides system performance. For this reason, we analyze existing algorithm of the inference engine such as RETE, TREAT, and LEAPS. The RETE algorithm searches thoroughly all rules, which makes its matching overhead significantly large. The TREAT algorithm provides better run-time performance by removing the burden of managing beta memory. The LEAPS algorithm, on the other hand, computes at most one match per cycle. Therefore, it provides significantly better matching performance than others; however, it may provide wrong result for some applications. |
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| Concept of complex context |
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W. Na, S. Cho, E. Kim, and Y. Choi "Event Detection in Composite Context Aware-Service," in Proc. of ICUFN, Dalian, China, June 2011. [PDF] |
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W. Na, G. Lee, S. Cho, and Y. Choi "Automatic Intelligence Application for Office Environment," in Proc. of ICOIN, Kuala Lumpur, Malaysia, January 2011. [PDF] |
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G. Lee, W. Na, S. Cho, and E. Kim, "Complex Context Information for Video Streaming Service," in Proc. of ICOIN, Kuala Lumpur, Malaysia, January 2011. [PDF] |
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이건우, 나웅수, 조성래, 배형철, 유정석 "복합 상황 인지 서비스의 효율적 처리구조(A Study on Complex Context Aware Services via Smart Office)," HCI2012 학술대회, 강원도, January 2012. [PDF] |
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이건우, 박래혁, 나웅수, 유정석, 조성래, 김응하, 최영일, "Smart Office Service 제공을 위한 복합 상황인지 연구(A Study on the Complex Context Awareness
for Smart Office Service)," 한국통신학회 하계종합학술발표회, 제주, June 2011. [PDF] |
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