Abstract
We propose a methodology to monitor the quality of the meta-data used to describe content in web portals. It is based on the analysis of the meta-data using statistics, visualization and data mining tools. The methodology enables the site's editor to detect and correct problems in the description of contents, thus improving the quality of the web portal and the satisfaction of its users. We also define a general architecture for a platform to support the proposed methodology. We have implemented this platform and tested it on a Portuguese portal for management; executives. The results validate the methodology proposed.

Abstract
In this paper we describe a platform that enables Web site automation and monitoring. The platform automatically gathers high quality site activity data, both from the server and client sides. Web adapters, such as rec-ommender systems, can be easily plugged into the platform, and take advantage of the up-to-date activity data. The platform also includes a module to support the editor of the site to monitor and assess the effects of automation. We illustrate the features of the platform on a case study, where we show how it can be used to gather information not only to model the behavior of users but also the impact of the personalization mechanism. Copyright © 2008, Association for the Advancement of Artificial Intelligence.

Abstract
The design, production and distribution of small series of health fashionable goods for specific target groups of wide impact in terms of market for the European industry as elderly, disables, diabetics and obese people represents a challenging opportunity for European companies which are asked to supply the demand with affordable price and eco-compatible products. Added to this challenge, textile, clothing and footwear manufactures seek for innovative collaborative networking solutions that could provide an entire digital life-cycle for the products and services required by the market. Aligned with this need, the EU CoReNet project aims to design and develop a new smart collaborative consumer-driven framework with the related services and components. This paper addresses the multidisciplinary complexity of customer-oriented and eco-friendly networks for health fashionable goods in particular addressing business requirements analysis, value chain issues, co-planning production and co-design topics in collaborative business processes tailored for high variability of the consumers demand and expectations.

Abstract
Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data because observations are not independent. Several studies have analyzed how different performance estimation methods compare with each other for approximating the true loss incurred by a given forecasting model. However, these studies do not address how the estimators behave for model selection: the ability to select the best solution among a set of alternatives. This paper addresses this issue. The goal of this work is to compare a set of estimation methods for model selection in time series forecasting tasks. This objective is split into two main questions: (i) analyze how often a given estimation method selects the best possible model; and (ii) analyze what is the performance loss when the best model is not selected. Experiments were carried out using a case study that contains 3111 time series. The accuracy of the estimators for selecting the best solution is low, despite being significantly better than random selection. Moreover, the overall forecasting performance loss associated with the model selection process ranges from 0.28 to 0.58%. Yet, no considerable differences between different approaches were found. Besides, the sample size of the time series is an important factor in the relative performance of the estimators.

Abstract
Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our study is to fill in this gap. We have performed experiments with 12 various outlier elimination methods and 10 classification algorithms on 50 different datasets. The results were then processed by the proposed reduction method, whose aim is identify the most useful workflows for a given set of tasks (datasets). The reduction method has identified that just three OEMs that are generally useful for the given set of tasks. We have shown that the inclusion of these OEMs is indeed useful, as it leads to lower loss in accuracy and the difference is quite significant (0.5%) on average.

Abstract
The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals. The timely prediction of these events is crucial for mitigating their consequences and improving healthcare. One of the most common approaches to tackle early anomaly detection problems is through standard classification methods. In this paper we propose a novel method that uses a layered learning architecture to address these tasks. One key contribution of our work is the idea of pre-conditional events, which denote arbitrary but computable relaxed versions of the event of interest. We leverage this idea to break the original problem into two hierarchical layers, which we hypothesize are easier to solve. The results suggest that the proposed approach leads to a better performance relative to state of the art approaches for critical health episode prediction.