LIAAD - Indexed Articles in Conferences

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    Definição de uma Framework para Avaliação dos Portais Regionais, Camarários, Turísticos e Empresariais das Cidades e Regiões Digitais
    ( 2008) Ricardo Campos
    Financiado pelo POS_C, o projecto Cidades e Regiões Digitais pretende desenvolver a Sociedade de Informação e do Conhecimento ao nível regional de forma a criar competências regionais aplicadas que criem valor económico para as regiões, aumentem a qualidade de vida dos seus cidadãos, promovam a competitividade das suas empresas e o seu desenvolvimento sustentado. Neste artigo definimos uma framework de avaliação dos portais Regionais, Camarários, Turísticos e Empresariais das Cidades e Regiões Digitais, tendo por base a análise de 15 websites pertencentes a quatro casos de estudo seleccionados da execução do projecto 'Dos Projectos às Regiões Digitais: que Desafios?'. O objectivo desta investigação é entender a forma como estes projectos influenciaram a presença on-line da região em que se inserem. Os resultados mostram-se globalmente positivos ao nível das acessibilidades, conteúdos e navegabilidade. Ressalta uma reduzida preocupação com a segurança e privacidade dos dados e a necessi
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    A platform to support web site adaptation and monitoring of its effects: A case study
    ( 2008) Alípio Jorge ; José Paulo Leal ; Carlos Manuel Soares ; 4981 ; 5125 ; 5001
    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.
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    Os Sistemas de Informação Regionais das Cidades e Regiões Digitais na vertente Infraestrutural
    ( 2009) Ricardo Campos
    Neste artigo vamos focar os sistemas de informação regionais implementados no âmbito do projecto Cidades e Regiões Digitais (CRD). O artigo traduz parte de uma investigação financiada pela medida 1.3 do POS_C. O objectivo deste artigo é proceder a uma leitura crítica da implementação do sistema de informação regional do projecto CRD na vertente infraestrutural. As conclusões obtidas têm por base a execução do projecto acima referenciado e revelam uma aposta forte na vertente das infra-estruturas, nem sempre aproveitada da melhor forma pelas autarquias, principais beneficiárias do projecto
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    A Multi-Agent Approach for Web Adaptation.
    ( 2009) Jorge Morais
    Web growth has brought several problems to users. The large amount of information that exists nowadays in some particular Websites turns the task of finding useful information very difficult. Knowing users' visiting pattern is crucial to owners, so that they may transform or customize the Website. This problem originated the concept known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. This paper describes a proposal for a doctoral thesis. The main goal of this work is to follow a multi-agent approach for Web adaptation. The idea is that all knowledge administration about the Website and its users, and the use of that knowledge to adapt the site to fulfil user's needs, are made by an autonomous intelligent agent society in a negotiation environment. The complexity of the problem and the inherently distributed nature of the Web, which is an open, heterogeneous and decentralized network, are reasons that justify the multi-agent approa
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    DESAMBIGUATING WEB SEARCH RESULTS BY TOPIC AND TEMPORAL CLUSTERING: A PROPOSAL
    ( 2009) Ricardo Campos ; Alípio Jorge ; Gaël Dias
    With so much information available on the web, looking for relevant documents on the Internet has become a difficult task. Temporal features play an important role with the introduction of a time dimension and the possibility to restrict a search by time, recreating a particular moment of a web page set. Despite its importance, temporal information is still under-considered by current search engines, limiting themselves to the capture of the most recent snapshot of the information. This poses two interesting problems. On the one hand, the information captured today may be gone tomorrow. On the other hand, if historical data still exists, it may be lost, due to the fact that temporal information is not considered. The solution to these problems may be in the introduction of timelines, through web archives or temporal search engines, which, individually or together, can favour the exploration and disambiguation of the information. In this paper, we describe the architecture of a temporal
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    MEC - Monitoring Clusters' Transitions
    ( 2010) João Gama ; Márcia Barbosa Oliveira
    In this paper we address the problem of monitoring the evolution of clusters, which has become an important research issue in recent years. We adopt two main strategies for cluster characterization - representation by enumeration and representation by comprehension -, and propose the MEC (Monitoring the Evolution of Clusters) framework, which was developed along the lines of the change mining paradigm. MEC includes a taxonomy of various types of cluster transitions, a tracking mechanism that depends on cluster representation, and a transition detection algorithm. Our tracking mechanism can be subdivided in two methods, devised to monitor clusters' transitions: one based on graph transitions, and another based on clusters' overlap. These are the most relevant contributions of this work. To demonstrate the feasibility and applicability of MEC we present illustrative examples, using datasets from different knowledge areas, such as Economy and Education. Our results are encouraging and demonstrate the ability of MEC framework to provide an efficient diagnosis of clusters' transitions.
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    Monitoring Incremental Histogram Distribution for Change Detection in Data Streams
    ( 2010) Pedro Pereira Rodrigues ; Raquel Sebastião ; João Gama ; João Bernardes
    Histograms are a common technique for density estimation and they have been widely used as a tool in exploratory data analysis. Learning histograms from static and stationary data is a well known topic. Nevertheless, very few works discuss this problem when we have a continuous flow of data generated from dynamic environments. The scope of this paper is to detect changes from high-speed time-changing data streams. To address this problem, we construct histograms able to process examples once at the rate they arrive. The main goal of this work is continuously maintain a histogram consistent with the current status of the nature. We study strategies to detect changes in the distribution generating examples, and adapt the histogram to the most recent data by forgetting outdated data. We use the Partition Incremental Discretization algorithm that was designed to learn histograms from high-speed data streams. We present a method to detect whenever a change in the distribution generating e
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    Understanding Clusters' Evolution
    ( 2010) João Gama ; Márcia Barbosa Oliveira
    In this paper we are interested in the study of evolving data, whose distribution may be non-stationary.We address the problem of monitoring the evolution of clusters over time. We adopt two main strategies for cluster characterization - representation by enumeration and representation by comprehension -, and propose the MEC framework, which was developed along the lines of Change Mining paradigm. MEC includes a taxonomy of various types of clusters' transitions, a tracking mechanism that depends on the cluster representation, and a transition detection algorithm. Our tracking mechanism can be subdivided in two novel methods that were designed to monitor the evolution of clusters' structures: one based on graph transitions, and another based on the overlapping degree. These are the most relevant contributions of this work. We experimentally evaluate our framework with a real world case study.
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    Bipartite Graphs for Monitoring Clusters Transitions
    ( 2010) João Gama ; Márcia Barbosa Oliveira
    The study of evolution has become an important research issue, especially in the last decade, due to a greater awareness of our world's volatility. As a consequence, a new paradigm has emerged to respond more effectively to a class of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory, and a transition detection algorithm. To demonstrate its feasibility and applicability we present real world case studies, using datasets extracted from Banco de Portugal and the Portuguese Institute of Statistics. We also test our approach in a benchmark dataset from TSDL. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions
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    Utility-based Fraud Detection
    ( 2011) Lopes Elsa ; Luís Torgo
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    Anáiise de Variância Dual
    ( 2011) Adelaide Figueiredo
    Consideremos a abordagem dual da abordagem clássica de estatística multivariada em que os indivíduos estão fixos e as variáveis são escolhidas aleatoriamente de uma população de variáveis. Supomos k grupos de variáveis centradas e reduzidas e associamos a cada grupo uma distribuição de Watson. Para vermos se estes grupos são distintos testamos se as direções privilegiadas das distribuições de Watson diferem significativamente usando a análise de variância dual. Analisamos a potência deste teste para dois grupos e diferentes dimensões de esfera. Pretendemos aplicar esta abordagem a dados reais.
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    2D-Interval Predictions for Time Series
    ( 2011) Luís Torgo ; Orlando Shigueo Junior
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    Using Web Snippets and Query-logs to Measure Implicit Temporal Intents in Queries
    ( 2011) Ricardo Campos ; Gaël Dias ; Alípio Jorge
    Understanding the user's temporal intent by means of query formulation is a particular hard task that can become even more difficult if the user is not clear in his purpose. For example, a user who issues the query Lady Gaga may wish to find the official web site of this popular singer or other information such as informative or even rumor texts. But, he may also wish to explore biographic data, temporal information on discography release and expected tour dates. Finding this information, however, may prove to be particularly difficult, if the user does not specify the query in terms of temporal intent. Thus, having access to this data, will allow search mechanisms to improve search results especially for time-implicit queries. In this paper, we study different approaches to automatically determine the temporal nature of queries. On the one hand, we exploit web snippets, a content-related resource. On the other hand, we exploit Google and Yahoo! completion engines, which provide query-
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    Future Retrieval: What Does the Future Talk About?
    ( 2011) Ricardo Campos ; Alípio Jorge ; Gaël Dias
    Predicting the future has always been one of the main aims of human beings in order to adapt their behavior and max- imize their chances of success. With the advent of the Web, which indexes a wealth of temporal information, a great number of research have been proposed in the area of Tem- poral Information Retrieval, but Future Retrieval has re- mained a di cult problem to handle. In this paper, we pro- pose to understand what the future is about. In particular, we present an exploratory study to understand how the tem- poral features impact upon the classi cation and clustering of di erent \genres" of future-related texts.
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    Regressão linear com variáveis intervalares
    ( 2011) Paula Brito ; Sónia Dias
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    Linear Regression for Interval and Histogram Variables
    ( 2011) Sónia Dias ; Paula Brito
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    Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems
    ( 2011) Alípio Jorge ; Carlos Manuel Soares ; Marcos Aurélio Domingues
    Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that enables the use of common two-dimensional top-N recommender algorithms for the generation of recommendations using additional dimensions (e.g., contextual or background information). We empirically evaluate our approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, on two real world data sets. The empirical results demonstrate that DaVI enables the application of existing twodimensional recommendation algorithms to exploit the useful information in multidimensional data.
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    An Exploratory Study on the impact of Temporal Features on the Classification and Clustering of Future-Related Web Documents
    ( 2011) Alípio Jorge ; Ricardo Campos ; Gaël Dias
    In the last few years, a huge amount of temporal written information has become widely available on the Internet with the advent of forums, blogs and social networks. This gave rise to a new challenging problem called future retrieval, which consists of extracting future temporal information, that is known in advance, from web sources in order to answer queries that combine text of a future temporal nature. This paper aims to confirm whether web snippets can be used to form an intelligent web that can detect future expected events when their dates are already known. Moreover, the objective is to identify the nature of future texts and understand how these temporal features affect the classification and clustering of the different types of future-related texts: informative texts, scheduled texts and rumor texts. We have conducted a set of comprehensive experiments and the results show that web documents are a valuable source of future data that can be particularly useful in identifying
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    Visualizing the Evolution of Social Networks
    ( 2011) Márcia Barbosa Oliveira ; João Gama
    In recent years we witnessed an impressive advance in the social networks field, which became a "hot" topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of trajectory. Our visualization strategy is based on trajectories of network's actors in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors by comparing the shape and position of the trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.
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    Using k-top Retrieved Web Snippets to Date Temporal Implicit Queries based on Web Content Analysis
    ( 2011) Ricardo Campos
    Temporal Information Retrieval (T-IR) has been a topic of great interest in recent years. Its purpose is to improve the information retrieval of documents by exploiting temporal information. However and despite the relative maturity of the area and an increasing involvement of the IR community in recent years, few works have fully used temporal information for exploration and search purposes [1], causing many queries not to be inferred by search engines. Such a lack lies in the fact that the used retrieval models continue to represent documents and queries simplistically, ignoring the underlying temporal semantics. Inferring the user intentions and the period he has in mind, may therefore play an extremely important role in the retrieval of the results. Our work goes in this direction. We aim at introducing a temporal analysis framework for analyzing documents in a temporal dimension in order to identify and understand the temporal nature of any given query, namely implicit ones (e.g.,