Managing schema evolution in a container‐based persistent system

Managing schema evolution is a problem every persistent system has to cope with to be useful in practice. Schema evolution consists basically of supporting class modification and dealing with data objects created and stored under the old class definitions. Several proposals have been made to handle this problem in systems that follow a full orthogonally persistent approach, but, until now, there has not been any proposal to support it in container‐based persistent systems. In this paper we describe a schema evolution management system designed for Barbados. Barbados is a complete programming environment which is based on an architecture of containers to provide persistent storage. Barbados does not provide full orthogonal persistence, but, as will be described in this paper, its architecture has several other advantages. Among them is the fact that this model is especially suitable for solving the schema evolution problem. Copyright © 2002 John Wiley & Sons, Ltd.     

Multi-agent neural business control system

Small to medium sized companies require a business control mechanism in order to monitor their modus operandi and analyse whether they are achieving their goals. A tool for the decision support process was developed based on a multi-agent system that incorporates a case-based reasoning system and automates the business control process. The case-based reasoning system automates the organization of cases and the retrieval stage by means of a Maximum Likelihood Hebbian Learning-based method, an extension of the Principal Component Analysis which groups similar cases by automatically identifying clusters in a data set in an unsupervised mode. The multi-agent system was tested with 22 small and medium sized companies in the textile sector located in the northwest of Spain during 29 months, and the results obtained have been very satisfactory.     

A dandelion-encoded evolutionary algorithm for the delay-constrained capacitated minimum spanning tree problem

This paper proposes an evolutionary algorithm with Dandelion-encoding to tackle the Delay-Constrained Capacitated Minimum Spanning Tree (DC-CMST) problem. This problem has been recently proposed, and consists of finding several broadcast trees from a source node, jointly considering traffic and delay constraints in trees. A version of the problem in which the source node is also included in the optimization process is considered as well in the paper. The Dandelion code used in the proposed evolutionary algorithm has been recently proposed as an effective way of encoding trees in evolutionary algorithms. Good properties of locality has been reported on this encoding, which makes it very effective to solve problems in which the solutions can be expressed in form of trees. In the paper we describe the main characteristics of the algorithm, the implementation of the Dandelion-encoding to tackled the DC-CMST problem and a modification needed to include the source node in the optimization. In the experimental section of this article we compare the results obtained by our evolutionary with that of a recently proposed heuristic for the DC-CMST, the Least Cost (LC) algorithm. We show that our Dandelion-encoded evolutionary algorithm is able to obtain better results that the LC in all the instances tackled.     

Design and performance evaluation of throughput-aware rate adaptation protocols for IEEE 802.11 wireless networks

Experimental studies have shown that traditional rate adaptation schemes for 802.11 wireless networks suffer from significant throughput degradation in highly congested networks. To address this problem, this paper makes the following two main contributions. First, we design a method to accurately estimate the per-packet transmission times, and we use these measurements to provide a broad classification of the network state and to identify network congestion. Second, we design a new Throughput-Aware Rate Adaptation (TARA) scheme, which uses the congestion estimates to mitigate the negative impact of link-layer collisions on the operations of rate adaptation, without requiring changes in the 802.11 MAC specification. Another key feature of our solution is to minimize probing overheads and limit unnecessary rate decreases by predicting the throughput gain that could be brought about by a change in the transmission rate. Through experiments conducted across a variety of network scenarios and traffic patterns, we show that TARA achieves significantly higher throughput than the other rate adaptation algorithms implemented in the legacy Madwifi driver.     

A UML 2.0 profile to define security requirements for Data Warehouses

Many Data Warehouses (DWs) fail to provide the appropriate information because the users' requirements are not correctly modeled. In addition, the security requirements are considered in the final implementation, and do not take the users' necessities into consideration. However, as DWs store confidential and sensitive information, it is crucial to take security measures into account from early DW design phases, and to enforce them. This paper proposes a profile which uses the Unified Modeling Language (UML) extensibility mechanisms. This profile allows us to define security requirements for DWs at the business level, taking into account the information requirements modeled with a previous profile. Our proposal is aligned with Model Driven Architecture (MDA), thus permitting the transformation of security requirements throughout the entire DW life cycle. Finally, in order to show the benefits of our profile, we develop a case study related to the management of a pharmacy consortium business.     

Invariant Signatures of Closed Planar Curves

We prove that any subset of ℝ² parametrized by a C ¹ periodic function and its derivative is the Euclidean invariant signature of a closed planar curve. This solves a problem posed by Calabi et al. (Int. J. Comput. Vis. 26:107–135, 1998). Based on the proof of this result, we then develop some cautionary examples concerning the application of signature curves for object recognition and symmetry detection as proposed by Calabi et al.

A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients

This paper presents a reinforcement learning (RL) approach for anemia management in patients undergoing chronic renal failure. Erythropoietin (EPO) is the treatment of choice for this kind of anemia but it is an expensive drug and with some dangerous side-effects that should be considered especially for patients who do not respond to the treatment. Therefore, an individualized treatment appears to be necessary. RL is a suitable approach to tackle this problem. Moreover, resulting policies are similar to medical protocols, and hence, they can easily be transferred to daily practice. A cohort of 64 patients are included in the study. An implementation of the Q-learning algorithm based on a state-aggregation table and another implementation using the multi-layer perceptron as a function approximator (Q-MLP) are compared with the protocols followed in the Nephrology Unit. The policy obtained by the Q-MLP approach outperforms the hospital policy in terms of the ratio of patients that are within the targeted range of hemoglobin (11.5–12.5 g/dl) at the end of the analyzed period, since an increase of 25% is observed. It ensures an improvement in patients’ quality-of-life and considerable economic savings for the health care system due to both the expensiveness of EPO treatment and the costs incurred by the health care system in order to alleviate problems related to EPO over-dosing. It should be pointed out that the approach presented here is completely general, and therefore, it can be applied to any problem of drug dosage optimization.     

Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water – was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition, and rates among the three study areas and indicates the importance of analysing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g. urban expansion, roads, and land-use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.     

Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored. The results indicate that a combination of vegetation indices as extra bands into Landsat TM multi-spectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multi-spectral bands improved the overall classification accuracy (OCA) by 5.6% and the overall kappa coefficient (OKC) by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes that have complex stand structures and large patch sizes.     

A hybrid proposal for cross-sectoral analysis of knowledge management

At present time, although many theoretical formulations have been successfully proposed, there is a lack of ICT-based tools to support practical deployment of knowledge management (KM) in real settings. To bridge this gap, a hybrid artificial intelligence system is proposed in present study, aimed at gaining deeper knowledge about KM practices in four different economic sectors. By means of soft computing, companies are diagnosed according to their status regarding KM and subsequent explanations about crucial KM practices and perspectives are generated. Interesting conclusions are then derived from these explanations, allowing KM managers to optimise their decisions and obtain better results. Experimental results of real-life data from Spanish companies associated with different economic sectors validate the proposed combination of techniques.