Improving search engines with open Web-based SKOS vocabularies

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe volume of digital information is increasingly larger and even though organiza-tions are making more of this information available, without the proper tools users have great difficulties in retrieving documents about subjects of interest. Good infor-mation retrieval mechanisms are crucial for answering user information needs. Nowadays, search engines are unavoidable - they are an essential feature in docu-ment management systems. However, achieving good relevancy is a difficult problem particularly when dealing with specific technical domains where vocabulary mismatch problems can be prejudicial. Numerous research works found that exploiting the lexi-cal or semantic relations of terms in a collection attenuates this problem. In this dissertation, we aim to improve search results and user experience by inves-tigating the use of potentially connected Web vocabularies in information retrieval en-gines. In the context of open Web-based SKOS vocabularies we propose a query expan-sion framework implemented in a widely used IR system (Lucene/Solr), and evaluated using standard IR evaluation datasets. The components described in this thesis were applied in the development of a new search system that was integrated with a rapid applications development tool in the context of an internship at Quidgest S.A.Fundação para a Ciência e Tecnologia - ImTV research project, in the context of the UTAustin-Portugal collaboration (UTA-Est/MAI/0010/2009); QSearch project (FCT/Quidgest

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Optimizing E-Commerce Product Classification Using Transfer Learning

The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not just reflects badly on the website, it could also lead to lost customers. With ecommerce sites nowadays providing their portal as a platform for third party merchants to sell their products as well, maintaining a consistency in product categorization becomes difficult. Therefore, automating this process could be of great utilization. This task of automation done on the basis of text could lead to discrepancies since the website itself, its various merchants, and users, all could use different terminologies for a product and its category. Thus, using images becomes a plausible solution for this problem. Dealing with images can best be done using deep learning in the form of convolutional neural networks. This is a computationally expensive task, and in order to keep the accuracy of a traditional convolutional neural network while reducing the hours it takes for the model to train, this project aims at using a technique called transfer learning. Transfer learning refers to sharing the knowledge gained from one task for another where new model does not need to be trained from scratch in order to reduce the time it takes for training. This project aims at using various product images belonging to five categories from an ecommerce platform and developing an algorithm that can accurately classify products in their respective categories while taking as less time as possible. The goal is to first test the performance of transfer learning against traditional convolutional networks. Then the next step is to apply transfer learning to the downloaded dataset and assess its performance on the accuracy and time taken to classify test data that the model has never seen before

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

Management of Scientific Images: An approach to the extraction, annotation and retrieval of figures in the field of High Energy Physics

El entorno de la información en la primera década del siglo XXI no tiene precedentes. Las barreras físicas que han limitado el acceso al conocimiento están desapareciendo a medida que los métodos tradicionales de acceso a información se reemplazan o se mejoran gracias al uso de sistemas basados en computador. Los sistemas digitales son capaces de gestionar colecciones mucho más grandes de documentos, confrontando a los usuarios de información con la avalancha de documentos asociados a su tópico de interés. Esta nueva situación ha creado un incentivo para el desarrollo de técnicas de minería de datos y la creación de motores de búsqueda más eficientes y capaces de limitar los resultados de búsqueda a un subconjunto reducido de los más relevantes. Sin embargo, la mayoría de los motores de búsqueda en la actualidad trabajan con descripciones textuales. Estas descripciones se pueden extraer o bien del contenido o a través de fuentes externas. La recuperación basada en el contenido no textual de documentos es un tema de investigación continua. En particular, la recuperación de imágenes y el desentrañar la información contenida en ellas están suscitando un gran interés en la comunidad científica. Las bibliotecas digitales se sitúan en una posición especial dentro de los sistemas que facilitan el acceso al conocimiento. Actúan como repositorios de documentos que comparten algunas características comunes (por ejemplo, pertenecer a la misma área de conocimiento o ser publicados por la misma institución) y como tales contienen documentos considerados de interés para un grupo particular de usuarios. Además, facilitan funcionalidades de recuperación sobre las colecciones gestionadas. Normalmente, las publicaciones científicas son las unidades más pequeñas gestionadas por las bibliotecas digitales científicas. Sin embargo, en el proceso de creación científica hay diferentes tipos de artefactos, entre otros: figuras y conjuntos de datos. Las figuras juegan un papel particularmente importante en el proceso de publicación científica. Representan los datos en una forma gráfica que nos permite mostrar patrones sobre grandes conjuntos de datos y transmitir ideas complejas de un modo fácilmente entendible. Los sistemas existentes para bibliotecas digitales facilitan el acceso a figuras, pero solo como parte de los ficheros sobre los que se serializa la publicación entera. El objetivo de esta tesis es proponer un conjunto de métodos ytécnicas que permitan transformar las figuras en productos de primera clase dentro del proceso de publicación científica, permitiendo que los investigadores puedan obtener el máximo beneficio a la hora de realizar búsquedas y revisiones de bibliografía existente. Los métodos y técnicas propuestos están orientados a facilitar la adquisición, anotación semántica y búsqueda de figuras contenidas en publicaciones científicas. Para demostrar la completitud de la investigación se han ilustrado las teorías propuestas mediante ejemplos en el campo de la Física de Partículas (también conocido como Física de Altas Energías). Para aquellos casos en los que se han necesitadoo en las figuras que aparecen con más frecuencia en las publicaciones de Física de Partículas: los gráficos científicos denominados en inglés con el término plots. Los prototipos que propuestas más detalladas han desarrollado para esta tesis se han integrado parcialmente dentro del software Invenio (1) para bibliotecas digitales, así como dentro de INSPIRE, una de las mayores bibliotecas digitales en Física de Partículas mantenida gracias a la colaboración de grandes laboratorios y centros de investigación como son el CERN, SLAC, DESY y Fermilab. 1). http://invenio-software.org

PB-ADVISOR: A private banking multi-investment porfolio.

Private banking is a business area in which the investor requires tailor-made advice. Because of the current market situation, investors are requiring answers to difficult questions and looking for assurance from wealth managers. Private bankers need to have deep knowledge about an innumerable list of products and their characteristics as well as the suitability of each product for the client’s characteristics to be able to offer an optimal portfolio according to client expectations. Client and portfolio diversity calls for new recommendation and advice systems focused on their specific characteristics. This paper presents PB-ADVISOR, a system aimed at recommending investment portfolios based on fuzzy and semantic technologies to private bankers. The proposed system provides private bankers with a powerful tool to support their decision process and help deal with complex investment portfolios. The system has been evaluated in a real scenario obtaining promising results