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Big Data Assurance Evaluation: An SLA-Based Approach.
The Big Data community has started noticing that there is the need to complete Big Data platforms with assurance techniques proving the correct behavior of Big Data
analytics and management. In this paper, we propose a Big Data assurance solution based on Service-Level Agreements (SLAs), focusing on a platform providing Model-based Big Data Analytics-as-a-Service (MBDAaaS)
Implementation of context-aware workflows with Multi-agent Systems
Systems in Ambient Intelligence (AmI) need to manage workflows that represent users’ activities. These workflows can be quite complex, as they may involve multiple participants, both physical and computational, playing different roles. Their execution implies monitoring the development of the activities in the environment, and taking the necessary actions for them and the workflow to reach a certain end. The context-aware approach supports the development of these applications to cope with event processing and regarding information issues. Modeling the actors in these context-aware workflows, where complex decisions and
interactions must be considered, can be achieved with multi-agent systems. Agents are autonomous entities with sophisticated and flexible behaviors, which are able to
adapt to complex and evolving environments, and to collaborate to reach common goals. This work presents architectural patterns to integrate agents on top of an
existing context-aware architecture. This allows an additional abstraction layer on top of context-aware systems, where knowledge management is performed by agents.This approach improves the flexibility of AmI systems and facilitates their design. A case study on guiding users in buildings to their meetings illustrates this approach
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