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Filtering and finding items of interest in large volumes of data, such as products in an e-commerce application or invoices in an ERP web platform can be a burdensome task, either for novice users that do not have insights on how the data is modeled or for those users who are already accustomed to the used system, but usually their filtering needs are significantly more complex. Natural language processing...
This paper presents two bio-inspired methods addressing the problem of business process optimization. We provide a comparative approach for optimal business process flow selection and resource allocation using two of the most important bio-inspired meta-heuristics: Ant Colony Optimization and Bee Colony Optimization. Our approach does not use predefined rules for making the decisions when selecting...
This paper presents a firefly-based method for business process optimization. Each artificial firefly has a candidate business process associated which is modeled as a causal matrix. The evolution of a candidate business process is achieved using genetic operators which aim to perform structural and resource allocation modifications. To establish whether a candidate business process is optimal, the...
In this paper we propose an energy aware dynamic consolidation algorithm for virtualized service centers based on reinforcement learning. The energy awareness is enacted by using the Energy Aware Context Model (EACM) to programmatically represent the current service center context situation by means of ontologies. We have defined the EACM model entropy metric for evaluating the service center greenness...
This paper addresses the problem of run-time management of a service center energy efficiency by using a context aware self-adapting algorithm. The algorithm adapts the service center energy consumption to the incoming workload by considering service center predefined Green Performance Indicators (GPI) and Key Performance Indicators (KPI). The service center energy performance context is obtained...
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