Studying research productivity is a challenging task that is important for understanding how scie... more Studying research productivity is a challenging task that is important for understanding how science evolves and crucial for agencies (and governments). In this context, we propose an approach for quantifying the scientific performance of a community (group of researchers) based on the similarity between its publication profile and a reference community’s publication profile. Unlike most approaches that consider citation analysis, which requires access to the content of a publication, we only need the researchers’ publication records. We investigate the similarity between communities and adopt a new metric named Volume Intensity. Our goal is to use Volume Intensity for measuring the internationality degree of a community. Our experimental results , using Computer Science graduate programs and including both real and random scenarios, show we can use publication profile as a performance indicator.
With the advent of XML as the basis for many data-centric applications, issues regarding the effe... more With the advent of XML as the basis for many data-centric applications, issues regarding the effective retrieval of XML data have become prevalent. In this context, XML query evaluation presents unique challenges mainly because existing relational query algorithms cannot be directly applied to process XML data for diverse reasons: XML data conform to a tree-format rather than a tabular one, do not follow a strict schema, and are typically textual with repetitive information. A number of data structures—known as structural summaries—have been defined to compensate for the XML data repetition and lack of schema. So far, these summaries have been explored mainly as secondary indexes that can identify nodes reachable from specific path patterns. This dissertation shows that such summaries can also indicate new data clustering and partitioning policies that are very beneficial for XML processing. Even though this aspect has started to receive some attention, there is yet to exist a comprehensive study on using summaries as data clustering technique and on their partitioning properties with respect to XML query processing. Furthermore, various questions regarding the structural summaries behavior when processing both stored data and streams of data are still open. Therefore, this dissertation examines query processing over XML data by exploring and extending the role of the structural aggregation properties provided by the summaries. Specifically, it evaluates and proposes algorithms for processing path queries over the partitions defined by the summaries. It introduces how the summaries can be employed as access methods and discusses the advantages and drawbacks of such context. It considers the typical query evaluation scenario of processing stored documents and returning the document nodes that satisfy a query (XPath semantics). Finally, it takes the role of structural aggregation one step further and introduces how the summaries can improve the performance of stream processing within the context of XML filtering. The overall objective is to show that structural aggregation methods can be employed efficiently in a variety of scenarios that are way more complex than the traditional secondary path indexing.
Este artigo apresenta a presença feminina nas sub-áreas da Computação brasileira a partir da part... more Este artigo apresenta a presença feminina nas sub-áreas da Computação brasileira a partir da participação em comitês de programa de simpósios da Sociedade Brasileira de Computação ao longo do tempo.
Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion, 2013
ABSTRACT A recent work shows that research groups with well connected academic networks tend to b... more ABSTRACT A recent work shows that research groups with well connected academic networks tend to be more prolific. Hence, recommending collaborations is essential for increasing a group’s connections, then boosting the group research as a collateral advantage. In this work, we propose two new metrics for recommending new collaborations or intensification of existing ones. Each metric considers a social principle (homophily and proximity) that is relevant within the academic context. We also propose new measures for evaluating the recommendations based on social concepts (novelty, diversity and coverage) that have never been used for such a goal. Our experimental evaluation shows that considering our new metrics improves the quality of the recommendations.
Proceedings 20th International Conference of the Chilean Computer Science Society, 2000
... Nina Edelweiss - Patrícia N. Hübler - Mirella M. Moro - Giovani Demartini Universidade Federa... more ... Nina Edelweiss - Patrícia N. Hübler - Mirella M. Moro - Giovani Demartini Universidade Federal do Rio Grande do Sul Instituto de Informática Porto Alegre - RS - Brasil { nina, hubler, mirella, demartin }@inf.ufrgs.br * This work was partially supported by CNPq - Brazil Abstract ...
Studying research productivity is a challenging task that is important for understanding how scie... more Studying research productivity is a challenging task that is important for understanding how science evolves and crucial for agencies (and governments). In this context, we propose an approach for quantifying the scientific performance of a community (group of researchers) based on the similarity between its publication profile and a reference community’s publication profile. Unlike most approaches that consider citation analysis, which requires access to the content of a publication, we only need the researchers’ publication records. We investigate the similarity between communities and adopt a new metric named Volume Intensity. Our goal is to use Volume Intensity for measuring the internationality degree of a community. Our experimental results , using Computer Science graduate programs and including both real and random scenarios, show we can use publication profile as a performance indicator.
With the advent of XML as the basis for many data-centric applications, issues regarding the effe... more With the advent of XML as the basis for many data-centric applications, issues regarding the effective retrieval of XML data have become prevalent. In this context, XML query evaluation presents unique challenges mainly because existing relational query algorithms cannot be directly applied to process XML data for diverse reasons: XML data conform to a tree-format rather than a tabular one, do not follow a strict schema, and are typically textual with repetitive information. A number of data structures—known as structural summaries—have been defined to compensate for the XML data repetition and lack of schema. So far, these summaries have been explored mainly as secondary indexes that can identify nodes reachable from specific path patterns. This dissertation shows that such summaries can also indicate new data clustering and partitioning policies that are very beneficial for XML processing. Even though this aspect has started to receive some attention, there is yet to exist a comprehensive study on using summaries as data clustering technique and on their partitioning properties with respect to XML query processing. Furthermore, various questions regarding the structural summaries behavior when processing both stored data and streams of data are still open. Therefore, this dissertation examines query processing over XML data by exploring and extending the role of the structural aggregation properties provided by the summaries. Specifically, it evaluates and proposes algorithms for processing path queries over the partitions defined by the summaries. It introduces how the summaries can be employed as access methods and discusses the advantages and drawbacks of such context. It considers the typical query evaluation scenario of processing stored documents and returning the document nodes that satisfy a query (XPath semantics). Finally, it takes the role of structural aggregation one step further and introduces how the summaries can improve the performance of stream processing within the context of XML filtering. The overall objective is to show that structural aggregation methods can be employed efficiently in a variety of scenarios that are way more complex than the traditional secondary path indexing.
Este artigo apresenta a presença feminina nas sub-áreas da Computação brasileira a partir da part... more Este artigo apresenta a presença feminina nas sub-áreas da Computação brasileira a partir da participação em comitês de programa de simpósios da Sociedade Brasileira de Computação ao longo do tempo.
Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion, 2013
ABSTRACT A recent work shows that research groups with well connected academic networks tend to b... more ABSTRACT A recent work shows that research groups with well connected academic networks tend to be more prolific. Hence, recommending collaborations is essential for increasing a group’s connections, then boosting the group research as a collateral advantage. In this work, we propose two new metrics for recommending new collaborations or intensification of existing ones. Each metric considers a social principle (homophily and proximity) that is relevant within the academic context. We also propose new measures for evaluating the recommendations based on social concepts (novelty, diversity and coverage) that have never been used for such a goal. Our experimental evaluation shows that considering our new metrics improves the quality of the recommendations.
Proceedings 20th International Conference of the Chilean Computer Science Society, 2000
... Nina Edelweiss - Patrícia N. Hübler - Mirella M. Moro - Giovani Demartini Universidade Federa... more ... Nina Edelweiss - Patrícia N. Hübler - Mirella M. Moro - Giovani Demartini Universidade Federal do Rio Grande do Sul Instituto de Informática Porto Alegre - RS - Brasil { nina, hubler, mirella, demartin }@inf.ufrgs.br * This work was partially supported by CNPq - Brazil Abstract ...
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