A Model-Based Monitoring Architecture for Heterogeneous Enterprise Services and Information Systems

Runtime management of distributed information systems is a complex and costly activity. One of the main challenges that must be addressed is obtaining a complete and updated view of all the managed runtime resources. This article presents a monitoring architecture for heterogeneous and distributed information systems. It is composed of two elements: an information model and an agent infrastructure. The model negates the complexity and variability of these systems and enables the abstraction over non-relevant details. The infrastructure uses this information model to monitor and manage the modeled environment, performing and detecting changes in execution time. The agents infrastructure is further detailed and its components and the relationships between them are explained. Moreover, the proposal is validated through a set of agents that instrument the JEE Glassfish application server, paying special attention to support distributed configuration scenarios.

Model-to-Code transformation from product-line architecture models to aspectJ

Software Product Line Engineering has significant advantages in family-based software development. The common and variable structure for all products of a family is defined through a Product-Line Architecture (PLA) that consists of a common set of reusable components and connectors which can be configured to build the different products. The design of PLA requires solutions for capturing such configuration (variability). The Flexible-PLA Model is a solution that supports the specification of external variability of the PLA configuration, as well as internal variability of components. However, a complete support for product-line development requires translating architecture specifications into code. This complex task needs automation to avoid human error. Since Model-Driven Development allows automatic code generation from models, this paper presents a solution to automatically generate AspectJ code from Flexible-PLA models previously configured to derive specific products. This solution is supported by a modeling framework and validated in a software factory.

Risk-based methodology for parameter calibration of a reservoir flood control model

Flash floods are of major relevance in natural disaster management in the Mediterranean region. In many cases, the damaging effects of flash floods can be mitigated by adequate management of flood control reservoirs. This requires the development of suitable models for optimal operation of reservoirs. A probabilistic methodology for calibrating the parameters of a reservoir flood control model (RFCM) that takes into account the stochastic variability of flood events is presented. This study addresses the crucial problem of operating reservoirs during flood events, considering downstream river damages and dam failure risk as conflicting operation criteria. These two criteria are aggregated into a single objective of total expected damages from both the maximum released flows and stored volumes (overall risk index). For each selected parameter set the RFCM is run under a wide range of hydrologic loads (determined through Monte Carlo simulation). The optimal parameter set is obtained through the overall risk index (balanced solution) and then compared with other solutions of the Pareto front. The proposed methodology is implemented at three different reservoirs in the southeast of Spain. The results obtained show that the balanced solution offers a good compromise between the two main objectives of reservoir flood control management

Elimination of variability sources in NIR spectra for the determination of fat content in olive fruits

Models for prediction of oil content as percentage of dried weight in olive fruits were comput- ed through PLS regression on NIR spectra. Spectral preprocessing was carried out by apply- ing multiplicative signal correction (MSC), Sa vitzky–Golay algorithm, standard normal variate correction (SNV), and detrending (D) to NIR spectra. MSC was the preprocessing technique showing the best performance. Further reduction of variability was performed by applying the Wold method of orthogonal signal correction (OSC). The calibration model achieved a R 2 of 0.93, a SEPc of 1.42, and a RPD of 3.8. The R 2 obtained with the validation set remained 0.93, and the SEPc was 1.41.