• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.27-44
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• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.243-246
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• 2005

Recent ubiquitous computing environments increasingly impact on our lives using the current technologies of sensor network and ubiquitous services. In this paper, we propose ubiquitous architectural framework for ubiquitous sleep aid service(UbiSAS) in the subset of ubiquitous computing for refreshing of human's sleep. And we examine technical feasibility. Human can recover his health through refreshing sleep from fatigue. Ubiquitous architectural framework for UbiSAS in digital home offers agreeable sleeping environment and improves recovery from fatigue. So we present new concept of ubiquitous architectural framework dissolving stress. Specially, we apply context to context-aware framework module. This context is transferred to context adaptive inference engine which has service invocation function in intelligent agent module. Ubiquitous architectural framework for UbiSAS using context adaptive rule inference engine without user intervention is technical issue. That is to say, we should take sleep comfortably during our sleeping. And sensed information during sleeping is changed to context-aware information. This presents significant information in context adaptive rule inference engine for UbiSAS. This information includes all sleeping state during sleeping in context-aware computing technique. So we propose more effective and most suitable ubiquitous architectural framework using context adaptive rule inference engine for refreshing sleep in this paper.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.239-250
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• 2013

Context-aware mobile computing has become the primary approach to realize automatic, autonomous, and user-centric computing in the context of largely increasing the amount of mobile devices used that embed available sensors. However, designing an inference engine nonetheless requires the tasks of analyzing contexts, situations that can be inferred, etc. Moreover, a mobile device has limited resources and limited computation capability, which results in recognizing the common sense of its unsuitable environment for processing inference. Hence, we propose context-situation reasoning elements and their formal models in this paper, and we verify the formal models' applicability by applying them to an example. Finally, we design and implement an inference engine that realize the context-situation inference elements in computing environment, and we experiment an example by using the proposed inference engine to verify applicability and reusability of the inference engine.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1157-1170
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• 2007

LBS must manage various context data and make the best use of this data for application service in ubiquitous environment. Conventional mobile object data management architecture did not consider process of context data. Therefore a new mobile data management framework is needed to process location context data. In this paper, we design a new context management framework for a location based application service. A suggestion framework is consisted of context collector, context manager, rule base, inference engine, and mobile object context database. It describes a form of rule base and a movement process of inference engine that are based on location based application scenario. It also presents an embodiment instance of interface which suggested framework is applied to location context interference of mobile object.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1079-1085
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• 2014

The analysis of the variable continuous big data stram should reach the destination context awareness. This study presented a novel way of context inference of the variable data stream from sensor motes. For assessment of the sensor data, we calculated the difference of each measured value at the time window and determined the belief value of each focal element. It was beneficial that calculate and assessment of factor of situation for context inference with the Dempster-Shfer evidence theory.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.488-501
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. But such services are usually limited to those served on a single mobile device. To resolve the resource limitation problem of mobile devices, a nearby resource sharing research has been studied. Also, not only the nearby resource share but also a resource recommendation through context-based resource reasoning has been studied such as an UMO Project. The UMO Project share and manage the various context information for the personalization resource recommendation and reason based on current context information. Also, should verify resource inference rules for reliable the resource recommendation. But, to create various context information requires huge cost and time in actuality. Thus, we propose a inference verification tool called USim to resolve problem. The proposed inference verification tool provides convenient graphic user interfaces and it easily creates context information. The USim exactly verifies new inference rules through dynamic changes of context information.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.291-296
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• 2006

In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.

• Journal of Software Engineering Society
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• v.24 no.1
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• pp.19-26
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• 2011

Since smart phones have various embedded sensors and high portability/usability, they have emerged as suitable targets to collect information and to provide intelligent services. That is, with a smart phone, we can collect information about user's circumstances and phone usage from sensors and infer his/her current state which is the significant basis for context-aware services. However, a service system should be founded on a context model to ensure reasonable context-awareness, because context information the system needs depends on its target services. Therefore, in this paper, we propose a hierarchical mobile context model for context inference of smart phone users in their daily life. We classify high-level context which can be draw from sensing data into three levels, Context-Behavior-Situation, and define inference methods for each level. With our mobile context model, we can user's meaningful context in his/her daily life besides simple actions or states.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.19-30
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• 2012

The context aware service is the service to provide useful information to the users by recognizing surroundings around people who receive the service via computer based on computing and communication, and by conducting self-decision. But CAS(Context Awareness System) shows the weak point of small-scale context awareness processing capacity due to restricted mobile function under the current mobile environment, memory space, and inference cost increment. In this paper, we propose a mobile cloud context system with using Google App Engine based on PaaS(Platform as a Service) in order to get context service in various mobile devices without any subordination to any specific platform. Inference design method of the proposed system makes use of knowledge-based framework with semantic inference that is presented by SWRL rule and OWL ontology and Jess with rule-based inference engine. As well as, it is intended to shorten the context service reasoning time with mapping the regular reasoning of SWRL to Jess reasoning engine by connecting the values such as Class, Property and Individual which are regular information in the form of SWRL to Jess reasoning engine via JessTab plug-in in order to overcome the demerit of queries reasoning method of SparQL in semantic search which is a previous reasoning method.