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Bench Mark: It is a bench mark algorithm against which the identiﬁed Meta ARM algorithms were to be compared. Full details of the Meta ARM algorithms can be found in Albashiri et al. (). Note that the overview given here is in the context of MADM (Multi-Agent Data Mining) whereas the original algorithms proposed by Albashiri et al. did not operate in an agent context. 4 Meta ARM Model In order to demonstrate the feasibility of our EMADS vision a peer to peer agentbased framework has been designed and implemented, which uses a broker mediatedbased architectural model .
At this point will be analyzed only which kind of organizations are supported, the other speciﬁc characteristics of the organizations will be analyzed in the following categories. Support for the integration with services. Some MAS software engineering has been expanded to the integration with services . At this point is interesting to analyze which kind of integration is supported by the approach (agents invoke services, services invoke agents or bidirectional) and the mechanisms used to facilitate the integration.
The DF (Directory Facilitator) provides a Yellow Pages service by means of which an agent can ﬁnd other agents providing the services required in order to achieve its goals. All Routine communication and registration are managed by these JADE agents. Data mining tasks are managed through the P2P model by the other agents. Fig. 1. Meta ARM Model Architecture In this framework, agents are responsible for accessing local data sources and for collaborative data analysis. The architecture includes: (i) data mining agents, (ii) data agents, (iii) task agents, (iv) user agents, and (v) mediators (JADE agents) for agents coordination.