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Hindemith
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Untersuchte Arbeit:
Seite: 54, Zeilen: 1 ff. (komplett)
Quelle: Vazquez 2007
Seite(n): 56, 57, Zeilen: 56: 17 ff.; 57: 1 ff.
Saa 054a diss.png

Figure 3.5: General Architecture of Gaia

The mapping mechanism of the Application Framework offers the possibility of describing requirements to find the suitable real device to assign a functional behavior (for example, audio output) so that matching devices can be found within the active space to perform that function during a task. The interesting part is how Gaia represented context in the form of a 4-components structure: Context(<ContextType>, <Subject>, <Relater>, <Object>) that in many ways resembles that of the Semantic Web, for example: Context(locatedIn; CoaguChekS; is; RoomLab123). Later, this model evolved into a predicate-based representation of context information:

  • Location(CoaguChekS; isOperating; RoomLab123)
  • InternetConnectionStatus(Gateway; is; OffLine)
  • User(Safdar; role; Admin)

During 2003, Gaia was extended with a semantic middleware layer for context awareness endorsing existing Semantic Web technologies in order to model and annotate context information, perform reasoning and carry out reactive behavior in response to context changes [84]. DAML+OIL (later OWL) was selected to represent the context information following a predicate model. In order to map the predicates onto the ontology, an ontology class is created for each predicate structure. So, the above-mentioned Location predicate becomes a Location ontology class with three possible relationships to denote the information that was previously enclosed in the predicate parameters.

Representing the context in this way, operations such as search, querying, fusion and so forth, become possible. There are several different entities involved in Gaia's context information infrastructure, as depicted in Fig. 3.6.

  • Context Providers: These are the sources of context information, probably obtained by sensors.
  • Context Synthesizers: They retrieve context information from different providers and perform some form of reasoning to infer new information making it available to other agents. Both static rules and machine learning techniques (such as Naive Bayes) can be applied to obtain new information.

[84] Anand Ranganathan and Roy H. Campbell; A middleware for context-aware agents in ubiquitous computing environments; In Proceedings of the ACM/IFIP/USENIX International Middleware Conference, 2003.

Saa 054a source.png

Figure 2.14: Gaia architecture. Source: [RHC+02b].

The mapping mechanism of the Application framework offers the possibility of describing requirements to find the suitable real device to assign a functional behaviour (for example, audio output) so that matching devices can be found within the active space to perform that function during a task.

Specially interesting is how Gaia represented context in the form of a 4-components structure: Context(<ContextType>, <Subject>, <Relater>, <Object>) that in many ways resembles that of the Semantic Web, for example: Context(temperature, roomlab21, is, 24 C) .

[Seite 57]

Later, this model evolved into a predicate-based representation of context information:

• Location(inaki, entering, roomlab21)

• Temperature(roomlab21, ‘‘=’’, 24 C)

• TVStatus(smallTV, is, off)

During 2003, Gaia was extended with a semantic middleware layer for context awareness endorsing existing Semantic Web technologies in order to model and annotate context information, perform reasoning and carry out reactive behaviour in response to context changes [RC03b]. DAML+OIL (later OWL) was selected to represent the context information following a predicate model [MRCM03b] [MRMC03].

In order to map the predicates onto the ontology, an ontology class is created for each predicate structure [RC03a]. So, the Location predicate becomes a Location ontology class with three possible relationships to denote the information that was previously enclosed in the predicate parameters [MRCM03b].

Representing the context in this way, operations such as search, querying, fusion and so forth, become possible.

There are several different entities involved in Gaia’s context information infrastructure depicted in Figure 2.15:

• Context Providers: they are sources of context information, probably obtained by sensors.

• Context Synthesisers: they retrieve context information from different providers and perform some form of reasoning to elicit new information making it available to other agents. Both static rules and machine learning techniques (such as Naïve Bayes) can be applied to obtain new information.


[MRCM03b] Robert E. McGrath, Anand Ranganathan, Roy H. Campbell, and M. Dennis Mickunas. Use of ontologies in pervasive computing environments. Technical Report Technical Report UIUCDCS-R-2003-2332, University of Illinois at Urbana-Champaign, April 2003.

[MRMC03] Robert E. McGrath, Anand Ranganathan, M. Dennis Mickunas, and Roy H. Campbell. Investigations of semantic interoperability in ubiquitous computing environments. In Proceedings of the 15th International Conference Parallel and Distributed Computing and Systems (PDCS), 2003.

[RC03a] Anand Ranganathan and Roy H. Campbell. An infrastructure for context-awareness based on first order logic. Personal and Ubiquitous Computing, 7(6):353–364, 2003.

[RC03b] Anand Ranganathan and Roy H. Campbell. A middleware for context-aware agents in ubiquitous computing environments. In Proceedings of the ACM/IFIP/USENIX International Middleware Conference, 2003.

[RHC+02b] Manuel Román, Christopher Hess, Renato Cerqueira, Anand Ranganathan, Roy H. Campbell, and Klara Nahrstedt. A middleware infrastructure for active spaces. IEEE Pervasive Computing, 1(4):74–83, 2002.

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