MiRE: A Minimal Rule Engine for context-aware mobile devices

Choi, Chang Koon; Park, I.; Hyun, Soon J.; Lee, Dongman; Sim, D.H.

doi:10.1109/icdim.2008.4746772

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…This changes, if inference should be performed on mobile devices. For this purpose, Choi et al propose MiRE, an inference engine which implements the minimum cores of the conventional rule processing facilities and some special policies to achieve resource-saving and light-weight inferencing support suitable for resource-limited mobile devices [2]. It also allows to register rules and contexts dynamically at run-time, but does not explain how the system becomes aware of the new context and rules to register.…”

Section: Related Workmentioning

confidence: 97%

The ObjectRules Framework - Providing Ad Hoc Context-Dependent Assistance in Dynamic Environments

Schneider

Velten

Haupert

2010

2010 Sixth International Conference on Intelligent Environments

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We present ObjectRules, a framework that allows to implement context-dependent assistance in dynamic environments by conceptually and/or physically attaching factual and procedural knowledge to locations, physical objects, and users. At runtime, the knowledge models of present objects are merged with the location's and user's knowledge models to a transient context model which can be used as input for a rule-based system. We describe the general architecture of the ObjectRules framework and demonstrate its practical application in the context of a smart medicine cupboard.

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Section: Related Workmentioning

confidence: 97%

The ObjectRules Framework - Providing Ad Hoc Context-Dependent Assistance in Dynamic Environments

Schneider

Velten

Haupert

2010

2010 Sixth International Conference on Intelligent Environments

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“…This is an extension of a previous mobile rule engine, MiRE [9]. MiRE is designed with emphasis on light-weight design for mobile contextaware services.…”

Section: B Requirement Analysismentioning

confidence: 99%

A Personalized Navigation Guide Using a Social-Spatial Ontology

Park

Huerta-Cánepa

Lee

et al. 2010

2010 IEEE 34th Annual Computer Software and Applications Conference

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Map services help people find places they wish to visit. Users input the target address and the system returns that location on a map, with additional information such as nearby points of interest, directions to the location, etc. However, these applications mainly focus on general knowledge, failing to provide personalized information that may help users find/identify a place faster. In this paper, we present a map application based on previous user knowledge. We represent two types of knowledge -spatial and social -in the form of a social-location ontology. This ontology is then used to present a destination based on places familiar to the user. By showing an unknown location in terms of a familiar place, we allow the user to find the target location with much briefer instructions. This is especially useful on mobile devices, in which the limited screen size restricts the amount of information that can be displayed. In order to adapt to new knowledge, the ontology evolves as the user visits new places and meets new people. Our evaluation shows that users prefer to have concrete information related to a destination for easier understanding

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“…They have the intrinsic capability to receive events and in addition to trigger a corresponding non-semantic rule when a particular (combination of) event happens. This is also part of the reason for the popularity and success in applying (non-reasoner) forward-chaining production rule engines to small devices [14,42]. As a matter of fact, some ruleentailment reasoners have already been implemented for resource-constrained environments [24,26].…”

Section: Choice Of Rule-entailment Reasonersmentioning

confidence: 99%

“…It implements the rule-entailment approach and RS rules. To avoid an ever-growing fact base, it keeps only the latest context facts and makes extensive use of indexing to speed up duplication checking [14,26]. μOR [3] is designed to help realize ambient intelligent medical devices.…”

Section: Resource-constrained Ontology Reasonersmentioning

confidence: 99%

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Resource-constrained reasoning using a reasoner composition approach

2015

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To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resourceconstrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a "well-suited" amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices.

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MiRE: A Minimal Rule Engine for context-aware mobile devices

Cited by 11 publications

References 6 publications

The ObjectRules Framework - Providing Ad Hoc Context-Dependent Assistance in Dynamic Environments

The ObjectRules Framework - Providing Ad Hoc Context-Dependent Assistance in Dynamic Environments

A Personalized Navigation Guide Using a Social-Spatial Ontology

Resource-constrained reasoning using a reasoner composition approach

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