Bruno Possas

Despite the recent advances in search quality, the fast increase in the size of the
Web collection has introduced new challenges for Web ranking algorithms. In fact, there are
still many situations in which the users are presented with imprecise or very poor results.
One of the key difficulties is the fact that users usually submit very short and ambiguous
queries, and they do not fully specify their information needs. That is, it is necessary to
improve the query formation… Exibir mais

Despite the recent advances in search quality, the fast increase in the size of the
Web collection has introduced new challenges for Web ranking algorithms. In fact, there are
still many situations in which the users are presented with imprecise or very poor results.
One of the key difficulties is the fact that users usually submit very short and ambiguous
queries, and they do not fully specify their information needs. That is, it is necessary to
improve the query formation process if better answers are to be provided. In this work we ... Exibir menos

Search engines process queries conjunctively to restrict the size of the answer set.
Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web
pages and the terms used to compose the Web queries. The combination of these two
features might lead to irrelevant query results, particularly in the case of more specific
queries composed of three or more terms. To deal with this problem we propose a new
technique for automatically structuring Web… Exibir mais

Search engines process queries conjunctively to restrict the size of the answer set.
Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web
pages and the terms used to compose the Web queries. The combination of these two
features might lead to irrelevant query results, particularly in the case of more specific
queries composed of three or more terms. To deal with this problem we propose a new
technique for automatically structuring Web queries as a set of smaller subqueries. To ...
Exibir menos

This work presents a new approach for ranking documents in the vector space
model. The novelty lies in two fronts. First, patterns of term co-occurrence are taken into
account and are processed efficiently. Second, term weights are generated using a data
mining technique called association rules. This leads to a new ranking mechanism called
the set-based vector model. The components of our model are no longer index terms but
index termsets, where a termset is a set of index… Exibir mais

This work presents a new approach for ranking documents in the vector space
model. The novelty lies in two fronts. First, patterns of term co-occurrence are taken into
account and are processed efficiently. Second, term weights are generated using a data
mining technique called association rules. This leads to a new ranking mechanism called
the set-based vector model. The components of our model are no longer index terms but
index termsets, where a termset is a set of index terms. Termsets capture the intuition that ... Exibir menos

The objective of this paper is to present an extension to the set-based model (SBM), which is an effective technique for computing term weights based on co-occurrence patterns, for processing conjunctive and phrase queries. The intuition that semantically related term occurrences often occur closer to each other is taken into consideration. The novelty is that all known approaches that account for co-occurrence patterns was initially designed for processing disjunctive (OR) queries, and our… Exibir mais

The objective of this paper is to present an extension to the set-based model (SBM), which is an effective technique for computing term weights based on co-occurrence patterns, for processing conjunctive and phrase queries. The intuition that semantically related term occurrences often occur closer to each other is taken into consideration. The novelty is that all known approaches that account for co-occurrence patterns was initially designed for processing disjunctive (OR) queries, and our extension provides a simple, effective and efficient way to process conjunctive (AND) and phrase queries. This technique is time efficient and yet yields nice improvements in retrieval effectiveness. Experimental results show that our extension improves the average precision of the answer set for all collection evaluated, keeping computational cost small. For the TReC-8 collection, our extension led to a gain, relative to the standard vector space model, of 23.32% and 18.98% in average precision curves for conjunctive and phrase queries, respectively. Exibir menos

This work presents a method for online generation of query related suggestions for
a Web search engine. The method uses association rules to extract related queries from the
log of sbumitted queries to the search engine. Experimental results were performed on a
real log containing more than 2.3 million queries submitted to a commercial search engine.
For the top 5 related terms our method presented correct suggestions in 90.5% of the time.
Using queries randomly selected… Exibir mais

This work presents a method for online generation of query related suggestions for
a Web search engine. The method uses association rules to extract related queries from the
log of sbumitted queries to the search engine. Experimental results were performed on a
real log containing more than 2.3 million queries submitted to a commercial search engine.
For the top 5 related terms our method presented correct suggestions in 90.5% of the time.
Using queries randomly selected from a log we obtained 93.45% of correct suggestions. A ... Exibir menos

SBM), which is an effective technique for computing term weights based on co-occurrence patterns, employing the information about the proximity among query terms in documents. The intuition that semantically related term occurrences often occur closer to each other is taken into consideration, leading to a new information retrieval model called proximity set-based model (PSBM). The novelty is that the proximity information is used as a pruning strategy to determine only related co-occurrence… Exibir mais

SBM), which is an effective technique for computing term weights based on co-occurrence patterns, employing the information about the proximity among query terms in documents. The intuition that semantically related term occurrences often occur closer to each other is taken into consideration, leading to a new information retrieval model called proximity set-based model (PSBM). The novelty is that the proximity information is used as a pruning strategy to determine only related co-occurrence term patterns. This technique is time efficient and yet yields nice improvements in retrieval effectiveness. Experimental results show that PSBM improves the average precision of the answer set for all four collections evaluated. For the CFC collection, PSBM leads to a gain relative to the standard vector space model (VSM), of 23% in average precision values and 55% in average precision for the top 10 documents. PSBM is also competitive in terms of computational performance, reducing the execution time of the SBM in 21% for the CISI collection. Exibir menos

The objective of this paper is to present a new technique for computing term
weights for index terms, which leads to a new ranking mechanism, referred to as set-based
model. The components in our model are no longer terms, but termsets. The novelty is that
we compute term weights using a data mining technique called association rules, which is
time efficient and yet yields nice improvements in retrieval effectiveness. The set-based
model function for computing the similarity… Exibir mais

The objective of this paper is to present a new technique for computing term
weights for index terms, which leads to a new ranking mechanism, referred to as set-based
model. The components in our model are no longer terms, but termsets. The novelty is that
we compute term weights using a data mining technique called association rules, which is
time efficient and yet yields nice improvements in retrieval effectiveness. The set-based
model function for computing the similarity between a document and a query considers ... Exibir menos

The field of knowledge discovery and data mining (KDD), spurred by
advances in data collection technology, is concerned with the process of deriving interesting
and useful patterns from large datasets. The KDD process is computational and data-
intensive and is inherently interactive and iterative in nature. In fact, interactivity is often the
key to facilitating effective data understanding and knowledge discovery. In such an
environment, response time is crucial because… Exibir mais

The field of knowledge discovery and data mining (KDD), spurred by
advances in data collection technology, is concerned with the process of deriving interesting
and useful patterns from large datasets. The KDD process is computational and data-
intensive and is inherently interactive and iterative in nature. In fact, interactivity is often the
key to facilitating effective data understanding and knowledge discovery. In such an
environment, response time is crucial because lengthy time delay between responses of ... Exibir menos

Most current work on discovery of association rules assumes that the database from
which the rules are determined is static. The mining operation is performed just once and
therefore there is no need of knowledge management integration techniques. However,
there are several domains where the database is updated on a regular basis. In these
dynamic databases, it is hard to maintain the discovered rules since the updates may not
only invalidate some existing rules but also… Exibir mais

Most current work on discovery of association rules assumes that the database from
which the rules are determined is static. The mining operation is performed just once and
therefore there is no need of knowledge management integration techniques. However,
there are several domains where the database is updated on a regular basis. In these
dynamic databases, it is hard to maintain the discovered rules since the updates may not
only invalidate some existing rules but also make other rules relevant. We present an ... Exibir menos

Encontrar as regras de associação presentes em grandes bases de dados é um
importante problema em Mineração de Dados. Existe uma grande necessidade de
desenvolver algoritmos paralelos para esse problema, uma vez que ele corresponde a um
processo computacional muito custoso. No entanto, a maioria dos algoritmos propostos
para minerar tais regras seguem uma busca iterativa, que imp: íe a necessidade de
sincronização ao final de cada iteração, degradando o desempenho. Outra… Exibir mais

Encontrar as regras de associação presentes em grandes bases de dados é um
importante problema em Mineração de Dados. Existe uma grande necessidade de
desenvolver algoritmos paralelos para esse problema, uma vez que ele corresponde a um
processo computacional muito custoso. No entanto, a maioria dos algoritmos propostos
para minerar tais regras seguem uma busca iterativa, que imp: íe a necessidade de
sincronização ao final de cada iteração, degradando o desempenho. Outra deficiência ... Exibir menos

A utilização efetiva e contínua de técnicas de mineração de dados e di?cultada pela constante adição de novas transações, que resultam em bases de dados enormes, e por mudancas nos critérios utilizados na atividade de mineração, no caso de regras de associação, o suporte e a con?anca. O problema neste caso e que esse dinamismo pode invalidar algumas regras existentes e provocar o surgimento de novas regras relevantes. Neste artigo apresentamos PELICANO, um algoritmo e?ciente para geração… Exibir mais

A utilização efetiva e contínua de técnicas de mineração de dados e di?cultada pela constante adição de novas transações, que resultam em bases de dados enormes, e por mudancas nos critérios utilizados na atividade de mineração, no caso de regras de associação, o suporte e a con?anca. O problema neste caso e que esse dinamismo pode invalidar algumas regras existentes e provocar o surgimento de novas regras relevantes. Neste artigo apresentamos PELICANO, um algoritmo e?ciente para geração incremental de regras de associação, que se baseia apenas nos itemsets maximais frequentes e na ocorrência de itens em transações para atualizar a base de regras de associação. Os itemsets maximais são usados para realizar uma enumeração descendente de todos os itemsets frequentes, minimizando o número de conjuntos candidatos processados para a atualização dos itemsets maximais frequentes. PELICANO difere de outros algoritmos incrementais principalmente por permitir variações no valor do suporte mínimo e por acessar, precisamente uma vez, a base de dados com as novas transações, minimizando custos de entrada/saída. Avaliamos nosso algoritmo realizando minerações incrementais tanto em bases de dados sintéticas como reais, as quais ?caram até 15 vezes mais rápida usando PELICANO. Exibir menos

The goal of this work is to present an extension to the vector model that accounts for the
correlation among query terms, by using association rules, a popular data mining technique.
In Information Retrieval, the vector model allows retrieving a set of documents from a term-
based query, where both query terms and documents are vectors in a vector space.
Although the vector model has been used succesfully for decades, there are no practical
and ef? cient mechanisms that… Exibir mais

The goal of this work is to present an extension to the vector model that accounts for the
correlation among query terms, by using association rules, a popular data mining technique.
In Information Retrieval, the vector model allows retrieving a set of documents from a term-
based query, where both query terms and documents are vectors in a vector space.
Although the vector model has been used succesfully for decades, there are no practical
and ef? cient mechanisms that account for correlations among query terms in each ... Exibir menos

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… Exibir mais

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 ... Exibir menos

Mineração de dados é uma área de pesquisa emergente, cujo objetivo principal é
extrair padrões e regras implícitos em banco de dados. Mui tos algoritmos para mineração
de regras de associação foram propostos. Entretanto, a pesquisa tem dado atenção
principalmente à algoritmos seqüenciais. Neste artigo apresentamos a paralelização de um
algoritmo para determinação de regras de associação, utilizando o paradigma de memória
compartilhada. Os resultados indicam que a nossa… Exibir mais

Mineração de dados é uma área de pesquisa emergente, cujo objetivo principal é
extrair padrões e regras implícitos em banco de dados. Mui tos algoritmos para mineração
de regras de associação foram propostos. Entretanto, a pesquisa tem dado atenção
principalmente à algoritmos seqüenciais. Neste artigo apresentamos a paralelização de um
algoritmo para determinação de regras de associação, utilizando o paradigma de memória
compartilhada. Os resultados indicam que a nossa paralelização é escalável até oito ... Exibir menos