The trending evolution towards the Internet of things and the general increase in broadband are constantly creating large volumes of data that need to be processed to extract further knowledge. Recently, the cloud computing model has seen the evolution from the initial scenario of a public cloud offering its resources to customers through virtualization and Internet, toward the concept of hybrid cloud, where the classic scenario is enriched with a private (company owned) cloud e.g., for the management of sensible data. In this work, we propose a software layer for the deployment and dynamic scaling of virtual clusters on a hybrid cloud. This system can be used for cloud bursting in the context of big data applications. Our work shows that although the execution is significantly influenced by the intercloud bandwidth, a dynamic off-premise provisioning mechanism could allow the user to significantly increase the application performance.
In recent years, the significant advantages brought to business processes by process mining account for its evolution as a major concern in both industrial and academic research. In particular, increasing attention has been turned to compliance monitoring as a way to identify when a sequence of events deviates from the expected behaviour. As we are entering the IoT era, an increasing variety of smart objects can be introduced in business processes (e.g., tags to track products in a plant, smartphones and badge swiping to draw the activities of customers and employees in a shopping center, etc.). All these objects produce large volumes of log data in the form of streams, which need to be run-time analysed to extract further knowledge about the underlying business process and to identify unexpected, non-conforming events.Albeit rather straightforward on a small log file, compliance verification techniques may show poor performances when dealing with big data and streams, thus calling for scalable approaches.This work investigates the possibility of spreading the compliance monitoring task over a network of computing nodes, achieving the desired scalability. The monitor is realised through the existing SCIFF framework for compliance checking, which provides a high level logic-based language for expressing the properties to be monitored and nicely supports the partitioning
Abstract. This paper presents the distributed implementation of ALIAS, an architecture composed of several cooperating intelligent agents. This system is particularly suited to solve problems in cases where knowledge about the problem domain is incomplete and agents may need to form reasonable hypotheses. In ALIAS agents are equipped with hypothetical reasoning capabilities, performed by means of abduction: if the knowledge available to a logic agent is insu cient to solve a query, the agent could abduce new hypotheses. Each agent is characterized by a local knowledge base represented by an abductive logic program. Agents might di er in their knowledge bases, but must agree on assumed hypotheses. That global knowledge base is dynamically created and managed by means of a shared tuple space. The prototype, developed using Java and Prolog, can run on a TCP IP network of computers. In the paper, we also discuss some experimental results to evaluate prototype e ciency.
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