In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Personalization is a key factor for differentiating services and retaining customers in World Wide Web sites. On the other hand, designing and implementing an efficient personalization strategy is still a challenge, because of the complexity of the techniques used and the variety of sites and customers, which are always evolving. This paper presents a functional model of personalization strategies that allows not only a simple and concise specification of those strategies, but also their simulation and validation. We demonstrate our model through e-Personal, a framework for estimating the effectiveness of personalization strategies. The framework guides the user through the process of specifying a strategy and estimates its impact based on previous interactions of customers with the site. It is based on our functional model and we illustrate its utilization for designing personalization strategies for a web portal. Our experiments are based on actual logs and show that the proposed framework enhances significantly the personalization process, indicating the goodness of the strategy design, the reliability of input data, and the impact of implementation decisions on the effectiveness of personalized sites.

The solution of the mining association rules problem in customer transactions was introduced by Agrawal, Imielinski and Swami in 1993. Their approach was extended in several directions such as adding or replacing the confidence and support by other measures, or how to also account for quantitative attributes. In this paper we present an algorithm that can be used in the context of several of the extensions provided in the literature while preserving its performance, as illustrated by a case study. Our approach is targeted at two of the most computationally demanding phases in the process of generating association rules: the enumeration of the candidate sets and the verification of which of them are frequent. The minimization of the cost of these phases is achieved by pruning early candidate sets based on additional quantitative information about the transactions. In summary, we explore certain multidimensional properties of the data allowing us to combine this additional information as a pruning criterion. Based on synthetically generated data, our strategy reduced the number of candidate sets examined by the algorithm up to 15%. Furthermore, it also reduced the execution time significantly, in the order of 23%.

Although caching and the creation of cache server hierarchies has became a popular strategy for reducing user waiting time and network traffic, there is no recipe for determining the best hierarchy configuration given a set of machines and the workload that they have to serve. This paper presents a novel approach for analyzing the performance of cache proxy hierarchies that is based on two metrics: hierarchical hit ratio and cache efficiency. This approach allows users to easily quantify trade-offs among configurations, facilitating the tuning of cache hierarchies. We illustrate our approach by analyzing possible configurations for a cache server hierarchy.