A Ptolemy II Based Modeling Approach for Context-Aware Computing

2006 ◽  
Author(s):  
Rui Li ◽  
Renfa Li ◽  
Jigang Wen
Keyword(s):  
Context Aware ◽  
Context Aware Computing ◽  
Modeling Approach

Towards Dynamic Checklists

2021 ◽  
Vol 28 (2) ◽  
pp. 1-33
Author(s):  
Leah Kulp ◽  
Aleksandra Sarcevic ◽  
Megan Cheng ◽  
Randall S. Burd
Keyword(s):  
Medical Setting ◽  
Context Aware ◽  
Team Leaders ◽  
Penetrating Injuries ◽  
Context Aware Computing ◽  
In The Wild

The goal of this in-the-wild study was to understand how different patient, provider, and environment contexts affected the use of a tablet-based checklist in a dynamic medical setting. Fifteen team leaders used the digital checklist in 187 actual trauma resuscitations. The measures of checklist interactions included the number of unchecked items and the number of notes written on the checklist. Of the 10 contexts we studied, team leaders’ arrival after the patient and patients with penetrating injuries were both associated with more unchecked items. We also found that the care of patients with external injuries contributed to more notes written on the checklist. Finally, our results showed that more experienced leaders took significantly more notes overall and more numerical notes than less experienced leaders. We conclude by discussing design implications and steps that can be achieved with context-aware computing towards adaptive checklists that meet the needs of dynamic use contexts.

Efficient Ontology Integration Model for Better Inference in Context Aware Computing

2011 ◽  
Vol 268-270 ◽  
pp. 841-846
Author(s):  
Soo Mi Yang
Keyword(s):  
Video Data ◽  
Smart Devices ◽  
Collaborative Network ◽  
Context Aware ◽  
Integration Model ◽  
Smart Cameras ◽  
Ontology Integration ◽  
Context Aware Computing ◽  
Heterogeneous Devices ◽  
Distributed Ontology

In this paper, we describe efficient ontology integration model for better context inference based on distributed ontology framework. Context aware computing with inference based on ontology is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. However even smart devices, they generally have small memory and power which can manage only part of ontology data. In our efficient ontology integration model, each of agents built in such devices get services not only from a region server, but also peer servers. For such a collaborative network, an effective cache framework that can handle heterogeneous devices is required for the efficient ontology integration. In this paper, we propose a efficient ontology integration model which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of model resulted in better context inference.

Predicting context aware computing performance

Ubiquity ◽  
2003 ◽  
Vol 2003 (February) ◽  
pp. 1-17
Author(s):  
Eli Rohn
Keyword(s):  
Context Aware ◽  
Context Aware Computing ◽  
Computing Performance

scholarly journals Study of a Context Quality Model for UbiPri Middleware

2020 ◽  
Author(s):  
Diandre de Paula ◽  
Daniel Saraiva ◽  
Romeiro Natália ◽  
Nuno Garcia ◽  
Valderi Leithardt
Keyword(s):  
Quality Indicators ◽  
Data Privacy ◽  
Context Information ◽  
Adaptation Process ◽  
Context Aware ◽  
Quality Model ◽  
Context Aware Computing ◽  
Quality Of Context ◽  
The Impact

With the growth of ubiquitous computing, context-aware computing-based applications are increasingly emerging, and these applications demonstrate the impact that context has on the adaptation process. From the context, it will be possible to adapt the application according to the requirements and needs of its users. Therefore, the quality of the context information must be guaranteed so that the application does not have an incorrect or unexpected adaptation process. But like any given data, there is the possibility of inaccuracy and/or uncertainty and so Quality of Context (QoC) plays a key role in ensuring the quality of context information and optimizing the adaptation process. To guarantee the Quality of Context it is necessary to study a quality model to be created, which will have the important function of evaluating the context information. Thus, it is necessary to ensure that the parameters and quality indicators to be used and evaluated are the most appropriate for a given type of application. This paper aims to study a context quality model for the UbiPri middleware, defining its quality indicators to ensure its proper functioning in the process of adaptation in granting access to ubiquitous environments. Keywords: QoC, Model, Context-Aware, Data, Privacy

scholarly journals CONTEXT STORAGE USING CLOUD-BASED MONGODB WITH RULE-BASED RETE ALGORITHM

2021 ◽  
Vol 9 (2) ◽  
pp. 1022-1030
Author(s):  
Shivakumar. C, Et. al.
Keyword(s):  
Sensor Network ◽  
Context Awareness ◽  
Research Work ◽  
Context Aware ◽  
The Novel ◽  
Rule Based ◽  
Smart System ◽  
Context Aware Computing ◽  
Context Data ◽  
Day By Day

In this Context-aware computing era, everything is being automated and because of this, smart system’s count been incrementing day by day.  The smart system is all about context awareness, which is a synergy with the objects in the system. The result of the interaction between the users and the sensors is nothing but the repository of the vast amount of context data. Now the challenging task is to represent, store, and retrieve context data. So, in this research work, we have provided solutions to context storage. Since the data generated from the sensor network is dynamic, we have represented data using Context dimension tree, stored the data in cloud-based ‘MongoDB’, which is a NoSQL. It provides dynamic schema and reasoning data using If-Then rules with RETE algorithm. The Novel research work is the integration of NoSQL cloud-based MongoDB, rule-based RETE algorithm and CLIPS tool architecture. This integration helps us to represent, store, retrieve and derive inferences from the context data efficiently..                       

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