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© Copyright 2008 Digital Enterprise Research Institute. All rights reserved.
Digital Enterprise Research Institute www.der...
Digital Enterprise Research Institute www.deri.i
e
Overview
1. Self and context awareness
1. Benefits
2. Research Question...
Digital Enterprise Research Institute www.deri.i
e
Self and Context awareness
 Context
– Any information that can be used...
Digital Enterprise Research Institute www.deri.i
e
Self and Context awareness : benefits

Self-awareness benefits
→ Issue...
Digital Enterprise Research Institute www.deri.i
e

Context-awareness benefits
Self and Context awareness : benefits
Imag...
Digital Enterprise Research Institute www.deri.i
e
Self and Context awareness: research challenge
Improvements in
– Hazard...
Digital Enterprise Research Institute www.deri.i
e
Current solutions – sensor ontologies
Sensor features to be described
–...
Digital Enterprise Research Institute www.deri.i
e
Current solutions – context classification
architecture
SOCAM (Service-...
Digital Enterprise Research Institute www.deri.i
e
Current solutions: research challenges
Challenges:
1. Sensor ontologies...
Digital Enterprise Research Institute www.deri.i
e
Our proposal: contextualized cognitive
perspective
1. Contextualized co...
Digital Enterprise Research Institute www.deri.i
e
Our proposal: ontology alignments and
extension
1. Ontology support to ...
Digital Enterprise Research Institute www.deri.i
e
Our proposal: ontology alignments and extension
Sensor-related concept ...
Digital Enterprise Research Institute www.deri.i
e
Our proposal: ontology alignments and extension
Context and situation r...
Digital Enterprise Research Institute www.deri.i
e
Conclusions and future work
Task: improvement of sensor reality underst...
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Contextualised Cognitive Perspective for Linked Sensor Data

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Short position paper, accepted at the Semantic Sensor Network workshop, at ISWC 2010

Published in: Technology, Education
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Transcript of "Contextualised Cognitive Perspective for Linked Sensor Data "

  1. 1. © Copyright 2008 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute www.deri.i e Myriam Leggieri, Alexandre Passant, Manfred Hauswirth DERI NUI Galway, Ireland A contextualised cognitive perspective for Linked Sensor Data
  2. 2. Digital Enterprise Research Institute www.deri.i e Overview 1. Self and context awareness 1. Benefits 2. Research Question 2. Current solutions 1. Sensor ontologies 2. Context classification architectures 3. Research Challenges 3. Our proposal 1. Contextualised cognitive perspective 2. Ontology alignments and extension 4. Conlusions and Future Work
  3. 3. Digital Enterprise Research Institute www.deri.i e Self and Context awareness  Context – Any information that can be used to characterize the situation of entities (i.e. whether a person, place or object) that are considered relevant to the interaction between a user and an application, including the user and the application themselves [DeyAbowd2000]  External context – Measured by hardware sensors – I.e. location, light, sound, movement, touch,  Internal context – Specified by the user or captured monitoring the user’s interaction – I.e. user’s goal, tasks, work context, business processes
  4. 4. Digital Enterprise Research Institute www.deri.i e Self and Context awareness : benefits  Self-awareness benefits → Issue: Find all the sensors acquiring oceanographic data in Cancùn – Solution: Self-awareness: auto-determine • The kind of data acquired • The location → Issue: Observation understanding – Solution: Machine-understandable description of oservations and measurements How is the ocean like at Cancùn right now?
  5. 5. Digital Enterprise Research Institute www.deri.i e  Context-awareness benefits Self and Context awareness : benefits Imagine: Calm ocean detected … but at the same time … another SN detects a movement of earth plates → Issue: Is it generally associated with storm surges?  Solution: Search the LoD cloud
  6. 6. Digital Enterprise Research Institute www.deri.i e Self and Context awareness: research challenge Improvements in – Hazardous detection – Sensor retrieval – Sensor data clustering What is needed 1. Proper ontologies to support detailed descriptions 2. Effective context classification architecture
  7. 7. Digital Enterprise Research Institute www.deri.i e Current solutions – sensor ontologies Sensor features to be described – Sensor / Device, Capabilities, Process, Physical properties, Observation, Networks Current ontologies specialized missed in covering all those features – SWAMO - Interoperability Sensor Web products / Sensor Web services – MMI Device and CSIRO sensor ontologies - System and capabilities, Process composition, Operational and Response model With the except of W3C SSN-Xg ontology – Covers all the basic sensor features; foreseen further integrations
  8. 8. Digital Enterprise Research Institute www.deri.i e Current solutions – context classification architecture SOCAM (Service-oriented Context-Aware Middleware) – Centralized context interpreter COBRA (Context Broker Architecture) – Agent based; centralized context broker (KB, inference, acquisition, etc.) Context Toolkit – P2P architecture, still needs a centralized discoverer
  9. 9. Digital Enterprise Research Institute www.deri.i e Current solutions: research challenges Challenges: 1. Sensor ontologies: 1. Develop and choose the right ontologies to integrate with the SSN-XG one 2. Context classification 1. Storage space issue; one point of failure 2. P2P: network boundaries; user responsability 3. Data not linked together – unless because of classification output; but in this way it is not widely reusable
  10. 10. Digital Enterprise Research Institute www.deri.i e Our proposal: contextualized cognitive perspective 1. Contextualized cognitive approach to sensor data classification  Cognitive: inspired by associative nature of human cognitiveness  Contextualized: delimited to the sensor environment Human Memory LOD Cloud C Environment Unlimited, unweak, decentralized
  11. 11. Digital Enterprise Research Institute www.deri.i e Our proposal: ontology alignments and extension 1. Ontology support to context description  Domain-agnostic ontology to describe sensor-related concepts  Event modelling ontology  Upper-level ontology  Additional concepts: SensorProject, SensorRole, SensorHierarchy
  12. 12. Digital Enterprise Research Institute www.deri.i e Our proposal: ontology alignments and extension Sensor-related concept descriptions
  13. 13. Digital Enterprise Research Institute www.deri.i e Our proposal: ontology alignments and extension Context and situation related concept descriptions
  14. 14. Digital Enterprise Research Institute www.deri.i e Conclusions and future work Task: improvement of sensor reality understanding LoD cloud as – Enhancement to classification – New mean for human cognitiveness emulation Steps – Ontologies extended and aligned – Validation of ontology modelling choices – Continue with the implementation including user feedback Great Challenge: – Effectively browsing the LoD cloud

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