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| Untersuchte Arbeit: Seite: 55, Zeilen: 1 ff (komplett) |
Quelle: Vazquez 2007 Seite(n): 57, 58, 60, 61, Zeilen: 57: 28 ff.; 58: 1 ff.; 60: 14 ff.; 61: 1 ff. |
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Figure 3.6: The Context Infrastructure in Gaia • Context Consumers: They gather context information from providers and synthesizers, reason about it and perform reactive behavior accordingly. • Context Provider Lookup Service: It is used by the context providers, one per environment, in order to publish the kind of context information they provide in order to be found by context consumers. • Context History Service: It contains database records, one per environment, of the past context information to make them available to the requesting parties. • Ontology Server: It stores ontologies, one per environment, for different types of information. 3.5.2 Conclusion Gaia fulfills many of the requirements established for a smart Ubiquitous Computing architecture, mainly those related to intelligence support. It makes use of context predicates for representing context information and OWL ontologies for taxonomical purposes. However, Gaia has two important drawbacks and several inconsistencies. The first main drawback is that, as a requisite, three different elements in the architecture must be deployed and properly configured in the environment: the Context Provider Lookup Service, the Context History Service and the Ontology Server. This constraint prevents Gaia from creating spontaneous emergent pervasive computing environments anywhere, since a deployment phase must me performed beforehand, enforcing an undesirable centralization. The second main disadvantage arises from the fact that core elements in the architecture, such as those three mentioned above, seem to be suitable for installation in desktop computers or servers, but not in embedded computers. The main inconsistencies with Gaia are originated from the initial selection of technologies and the subsequent integration of newer ones, that result in a strange mixture. For instance, representation of context information through predicates was present at the very initial stages, but when OWL ontologies were integrated in Gaia, context predicates remained as knowledge representation mechanisms instead of shifting to RDF, which [could have sound more sensible.] |
• Context Consumers: they gather context information from providers and synthesisers, reason about it and perform reactive behaviour accordingly.
• Context Provider Lookup Service: one per environment, it is used by context providers to publish the kind of context information they provide in order to be found by context consumers. • Context History Service: one per environment, it contains database records of past context information to make them available to requesting parties. [Seite 58] • Ontology Server: one per environment, stores ontologies for the different information types.
Figure 2.15: Gaia Context Infrastructure. Source: [RAMC04]. [Seite 60] 2.5.3 Conclusion Gaia fulfils many of the requirements established for a smart Ubiquitous Computing architecture, mainly those related to intelligence support. Gaia makes use of context predicates for representing context information and OWL ontologies for taxonomical purposes. [...] However, Gaia has two important drawbacks and several inconsistencies. The first main drawback is that Gaia requires three different elements in the architecture to be previously deployed and properly configured in the environment: the Context Provider Lookup Service, the Context History Service and the Ontology Server. This constraint prevents Gaia from creating spontaneous emergent pervasive computing environments anywhere, since a deployment phase must me performed beforehand, enforcing an undesirable centralisation. The second main disadvantage arises from the fact that core elements in the architecture, such as those three mentioned above, seem to be suitable for installation in desktop computers or servers, but not in embedded computers. [...] The main inconsistencies with Gaia are originated from the initial selection of technologies and the subsequent integration of newer ones, that result in a strange mixture. [Seite 61] For instance, representation of context information through predicates was present at the very initial stages. When OWL ontologies were integrated in Gaia, context predicates remained as knowledge representation mechanisms instead of shifting to RDF, which would have sound more sensible. [...] |
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