Browsing by Author "Espinoza, Mauricio"

Now showing 1 - 6 of 6

Automatic RDF-ization of big data semi-structured datasets
(Universidad de Cuenca, 2016-11-16) Gualán, Ronald; Freire, Renán; Tello, Andrés; Espinoza, Mauricio; Saquicela Galarza, Víctor Hugo
Linked data adoption continues to grow in many fields at a considerable pace. However, some of the most important datasets usually remain underexploited because of two main reasons: the huge volume of the datasets and the lack of methods for automatic conversion to RDF. This paper presents an automatic approach to tackle these problems by leveraging recent Big Data tools and a program for automatic conversion from a relational model to RDF. Overall, the process can be summarized in three steps: 1) bulk transfer of data from different sources to Hive/HDFS; 2) transformation of data on Hive to RDF using D2RQ; and 3) storing the resulting RDF in CumulusRDF. By using these Big Data tools, the platform will cope with the handling of big amounts of data available in different sources, which can include structured or semi-structured data. Moreover, since the RDF data are stored in CumulusRDF in the final step, users or applications can consume the resulting data by means of web services or SPARQL queries. Finally, an evaluation in the hydro-meteorological domain demonstrates the soundness of our approach.
Desarrollo de un sistema informático para la administración operativa de la dirección técnica de Telecomunicaciones de ETAPA
(2003) Peñaloza Buestán, Edwin Xavier; Chablay Morales, Fabián Vicente; Espinoza, Mauricio
Explotación de información en el dominio geo-hídrico ecuatoriano utilizando tecnología semántica
(Universidad de Cuenca, 2015) Lupercio, Lucía; Baculima, Fernando; Espinoza, Mauricio; Saquicela Galarza, Víctor Hugo; Universidad de Cuenca; Dirección de Investigación de la Universidad de Cuenca; DIUC
In this article, we present an approach for exploiting the information in the Ecuadorian geo-hydrological domain, in tune with current trends relative to data integration from different sources and the publication of these data by applying the principles of Linked Data. Our approach uses GeoSPARQL as query language, which provides the ability to perform spatial operations for exploiting and discovery of resources that represent geographic entities. These resources are stored on distributed RDF data stores, which correspond to data from different institutions involved in geo-hydrological domain. This proposal facilitates the integration and use of information from the existing links between resources that refer to the same real-world phenomenon. Finally, the resources exploited are displayed on a graphic tool named, map4rdf, allowing a unified vision of national hydric reality. This tool has been enhanced in our approach, becoming also in a technological contribution.
Integration and massive storage of hydro-meteorological data combining big data & semantic web technologies
(Universidad de Cuenca, 2015) Tello, Andrés; Freire, Renán; Espinoza, Mauricio; Saquicela Galarza, Víctor Hugo; Universidad de Cuenca; Dirección de Investigación de la Universidad de Cuenca
Ecuador contains an immense collection of hydro-meteorological data, informing us via standards how to locate and invoke them. If we want to make such data easier to understand and use, we need to store them in a common repository and annotate them by means of descriptive metadata. This paper proposes an approach for the massive storage, integration and semantic annotation of hydro-meteorological data using an open source integration container, NoSQL databases and Semantic Web technologies. The main contributions of this paper are: i) a shared common repository of hydro-meteorological data, ii) automatic semantic annotation to formally describe the data sources, and iii) efficient mechanisms for searching and retrieving hydro meteorological data.
Ontología DHARMA para la construcción de arquitectura de sistemas empresariales
(Universidad de Cuenca, 2016-11-16) Pérez, Wilson; Abad, Karina; Carvallo, Juan Pablo; Espinoza, Mauricio; Saquicela Galarza, Víctor Hugo
The construction of enterprise context models, a fundamental tool for the design of modern information systems, is usually a cumbersome task, largely as a consequence of the lack of communication between administrative staff and technical consultants in charge of its construction. In order to make this task easier, previous works proposed the reuse of context elements identified in various industrial cases, which have been packaged and stored in a catalog specifically designed for this purpose. The authors propose to go one step further, using semantic technologies to exploit the catalog, storing its content in a semantic repository designed to support the construction of context models. In order to construct the repository, NeOn ontology was adopted to develop a network ontology which integrates different domains and their vocabularies, based on different scenarios that allows the capturing of the requirements, reuse and enrichment of ontologies.
Plataforma para la búsqueda por contenido visual y semántico de imágenes médicas
(Universidad de Cuenca, 2015) La Cruz, Alexandra; Tello, Andrés; Espinoza, Mauricio; Saquicela Galarza, Víctor Hugo; Gonzalez, Patricia; González, Patricia; Sarmiento, Yoredy; Ramírez-Montalván, Washintong; Solano-Quinde, Lizandro; Vidal, María-Esther; Universidad de Cuenca; Dirección de Investigación de la Universidad de Cuenca; DIUC
This paper present a framework ontology-independent for the automatic semantic annotation of medical images. The automatic annotation is done by (a) semantifying of DICOM medical images (RDF-ization) automatically; (b) Integration of different biomedical ontologies, through the matching process between ontologies and DICOM metadata, making this approach ontology-independent; (c) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Additionally applying context based image retrieval, make possible the retrieval of medical imaging by similarity of images features. This platform is being built on a distributed architecture, which improve the way of classification, distribution and searching on image repository.