Concept Based Author Recommender System for CiteSeer

The information explosion in today's electronic world has created the need for information filtering techniques that help users filter out extraneous content to identify the right information they need to make important decisions. Recommender systems are one approach to this problem, based on presenting potential items of interest to a user rather than requiring the user to go looking for them. In this paper we propose a recommender system that recommends research papers of potential interest to the author from the CiteSeer database. For each author participating in the study, we create a user profile based on their previously published papers. Based on similarities between the user profile and profiles for documents in the collection, additional papers are recommended to the author. We introduce a novel way of representing the user profiles as tree of concepts and an algorithm for computing the similarity between the user profiles and document profiles using a tree-edit distance measure. Experiments with a group of volunteers show that our tree based algorithm provides better recommendations than a traditional vector-space model based technique

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Directive-based Approach to Heterogeneous Computing

El mundo de la computación de altas prestaciones está sufriendo grandes cambios que incrementan notablemente su complejidad. La incapacidad de los sistemas monoprocesador o incluso multiprocesador de mantener el incremento de la potencia de cómputo para suplir las necesidades de la comunidad científica ha forzado la irrupción de arquitecturas hardware masivamente paralelas y de unidades específicas para realizar operaciones concretas. Un buen ejemplo de este tipo de dispositivos son las GPU (Unidades de procesamiento gráfico). Estos dispositivos, tradicionalmente dedicados a la programación gráfica, se han convertido recientemente en una plataforma ideal para implementar cómputos masivamente paralelos. La combinación de GPUs para realizar tareas intensivas en cómputo con multi-procesadores para llevar tareas menos intensas pero con lógica de control más compleja, se ha convertido en los últimos años en una de las plataformas más comunes para la realización de cálculos científicos a bajo coste, dado que la potencia desplegada en muchos casos puede alcanzar la de clústers de pequeño o mediano tamaño, con un coste inicial y de mantenimiento notablemente inferior. La incorporación de GPUs en clústers ha permitido también aumentar la capacidad de éstos. Sin embargo, la complejidad de la programación de GPUs, y su integración con códigos existentes, dificultan enormemente la introducción de estas tecnologías entre usuarios menos expertos. En esta tésis exploramos la utilización de modelos de programación basados en directivas para este tipo de entornos, multi-core, many-core, GPUs y clústers, donde el usuario medio ve disminuida notablemente su productividad debido a la dificultad de programación en estos entornos. Para explorar la mejor forma de aplicar directivas en estos entornos, hemos desarrollado un conjunto de herramientas software altamente flexibles (un compilador y un runtime), que permiten explorar diversas técnicas con relativamente poco esfuerzo. La irrupción del estándar de programación de directivas de OpenACC nos permitió demostrar la capacidad de estas herramientas, realizando una implementación experimental del estándar (accULL) en muy poco tiempo y con un rendimiento nada desdeñable. Los resultados computacionales aportados nos permiten demostrar: (a) La disminución en el esfuerzo de programación que permiten las aproximaciones basadas en directivas, (b) La capacidad y flexibilidad de las herramientas diseñadas durante esta tésis para explorar estas aproximaciones y finalmente (c) El potencial de desarrollo futuro de accULL como herramienta experimental en OpenACC en base al rendimiento obtenido actualmente frente al rendimiento de otras aproximaciones comerciales

Special Libraries, October 1963

Volume 54, Issue 8https://scholarworks.sjsu.edu/sla_sl_1963/1007/thumbnail.jp

Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential