Intertwining Laplace Transformations of Linear Partial Differential Equations

We propose a generalization of Laplace transformations to the case of linear partial differential operators (LPDOs) of arbitrary order in R^n. Practically all previously proposed differential transformations of LPDOs are particular cases of this transformation (intertwining Laplace transformation, ILT). We give a complete algorithm of construction of ILT and describe the classes of operators in R^n suitable for this transformation. Keywords: Integration of linear partial differential equations, Laplace transformation, differential transformationComment: LaTeX, 25 pages v2: minor misprints correcte

Implementing the Duty Trip Support Application

We are in the process of developing an agent and ontology-based Duty Trip Support application. The goal of this paper is to consider issues arising when implementing such a system. In addition to the description of our current implementation, which is also critically analyzed, other possible approaches are considered as well.software agents, agent systems, ontologies, transport objects, agent-non-agent integration.

Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations

Today, data centers deal with fast growing data volumes. To deliver services, they deploy growing amount of heterogeneous hardware. As a result, it becomes practically impossible to apply human-based data center management. For instance, in a real-world data center, with 500+ computers, delivering data, computational, and network services, it becomes impossible to visualize, and understand, causal relationships among variables describing performance of monitored resources. However, it is possible to collect data describing behavior of individual nodes. Hence, such data may be used to analyze/model system performance. In particular, it may be applied to recognize and predict anomalies in system behavior. Furthermore, collected data should allow finding the cause(s) of anomalies. Therefore, “data-driven approaches” have been applied to the real-world data, to find, so called, Root Cause of anomalies