Extended TA Algorithm for Adapting a Situation Ontology

Zweigle, Oliver; Häußermann, Kai; Käppeler, Uwe-Philipp; Levi, Paul

doi:10.1007/978-3-642-03986-7_42

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…in museums and galleries), which receive information from dispersed bluetooth devices in a building and show the relevant information to the user based on this data and their individual profile. With the increasing ease of deployment of various sensors and improvements in data-acquisition techniques, it comes as no surprise that a number of studies in recent years have focussed on decentralized and distributed systems, studying various aspects, such as distributed reasoning [31]- [33], context sharing [34], [35], distributed infrastructure [36], and distributed cognition [35], [37]- [44].…”

Section: ) Distributed Contextual Awarenessmentioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to ''context-aware systems'' is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

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Section: ) Distributed Contextual Awarenessmentioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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“…The operator configuration is dependent on the reasoning algorithm. For instance, in case of the Bayesian Networks, the algorithm proposed in [20] can be used to calculate the new values of the CPTs. Moreover apart from the adaptation of the operator configuration, the user feedback should also update the corresponding situation instance in the World Model.…”

Section: System Architecturementioning

confidence: 99%

“…For instance, the quality of the output of a sensor feeding a context source can be determined through the accuracy specified in the sensor data sheet. The confidence of the (sub-)situations detected by operators is then specified by using statistical methods as described in [20].…”

Section: B Logical Plannermentioning

confidence: 99%

“…Moreover during the distributed execution of this reasoning algorithm, an adaptive algorithm for the correction of the CPTs is used to exploit the user feedback. In particular, we implemented the feedback algorithm described in [20], which combines methods from the field of Bayesian Networks and Machine Learning. At a first step the algorithm exploits historical data by using statistical methods to calculate the confidence of each operator.…”

Section: Context Reasoningmentioning

confidence: 99%

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A System for Distributed Context Reasoning

Rizou

Häußermann

Dürr

et al. 2010

2010 Sixth International Conference on Autonomic and Autonomous Systems

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Context aware systems use context information to adapt their behaviour accordingly. In order to derive high level context information from low level context, such as sensor values, context reasoning methods that correlate observable context information, are necessary. Several context reasoning mechanisms have been proposed in the literature. Usually these mechanisms are centralized, leading to suboptimal utilization of network resources and poor system performance in case of largescale scenarios. Therefore to increase the scalability of context reasoning systems the development of methods that distribute the reasoning process is necessary. Existing distributed approaches are method specific and do not provide a generic formalization for distributed reasoning. In this paper we introduce a novel system which enables distributed context reasoning in a generic way that is independent of the reasoning algorithm.

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Web Mining to Create Semantic Content: A Case Study for the Environment

Theocharopoulou

Giannakis

2012

IFIP Advances in Information and Communication Technology

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In this study, the goal is multifold. At first we present a summarized review of terms and facts regarding the branch of ecoinformatics, web mining and the semantic web. In Section 2 we provide some related work derived from the current literature upon the web mining and the production of semantic content. The main part of our work follows presenting a notional model for building semantic content through 2-level web mining. This is achieved in web sites containing environmental data. We conclude mentioning the importance of this contribution from different points of view.

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Extended TA Algorithm for Adapting a Situation Ontology

Cited by 5 publications

References 11 publications

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

A System for Distributed Context Reasoning

Web Mining to Create Semantic Content: A Case Study for the Environment

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