How to Identify the Relevant Elements of “Context” in Context-Aware Information Systems?

Production networks have been established in many industrial domains with globalized supply structures, sourcing strategies or cooperation environments. Knowledge management in such networks requires a context-oriented approach in order to accommodate for individual and organizational needs when providing relevant knowledge for complex tasks, such as value creation. The focus of this paper is on the procedure by which to determine what actually has to be taken into account as part of the ‘context’ for establishing context-oriented knowledge management, how to capture this context, and how to use it. The variability of organisational and individual tasks both at design time and at runtime is essential for understanding context in production networks. The main contribution of the present study is a context modelling method, including variability identification. The application of this method is demonstrated by using an example of a production network from automotive industries.

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“…We propose a CM approach consisting of four steps, which is inspired by a CM approach for e-learning systems [36]. These steps are:…”

Section: B Methods Stepsmentioning

confidence: 99%

Context-oriented Knowledge Management in Production Networks

Sandkuhl

Smirnov

2018

Applied Computer Systems

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“…Situational context: This includes any information which can characterize the state of entity or location. In the domain of ubiquitous computing, context aware application framework should always be able to answer "when", "where" and "what" related to the service requested [13]. In the example of pull based application scenarios (POI services particularly) , "where" denotes context characteristics of location which is addressed by the factors "sensitivity" and "density" for the proposed system; "when" denotes temporal factors represented by "time of the day" and, "what" denotes characteristics of the service requested, which is satisfied by name/type of POI and its usage duration.…”

Section: Context Modeling and Validationmentioning

confidence: 99%

Soft Computing for Scalability in Context Aware Location based Services

Jagwani

Kaushik

2020

IJACSA

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Ubiquitous computing blended with context awareness gives user the facility of "anywhere anytime" computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user's privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity.The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error.

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