Using Semantic Web technologies in the development of data warehouses: A systematic mapping

The exploration and use of Semantic Web technologies have attracted considerable attention from researchers examining data warehouse (DW) development. However, the impact of this research and the maturity level of its results are still unclear. The objective of this study is to examine recently published research articles that take into account the use of Semantic Web technologies in the DW arena with the intention of summarizing their results, classifying their contributions to the field according to publication type, evaluating the maturity level of the results, and identifying future research challenges. Three main conclusions were derived from this study: (a) there is a major technological gap that inhibits the wide adoption of Semantic Web technologies in the business domain;(b) there is limited evidence that the results of the analyzed studies are applicable and transferable to industrial use; and (c) interest in researching the relationship between DWs and Semantic Web has decreased because new paradigms, such as linked open data, have attracted the interest of researchers.This study was supported by the Universidad de La Frontera, Chile, PROY. DI15-0020. Universidad de la Frontera, Chile, Grant Numbers: DI15-0020 and DI17-0043

A UML Profile for Variety and Variability Awareness in Multidimensional Design: An application to Agricultural Robots

Variety and variability are an inherent source of information wealth in schemaless sources, and executing OLAP sessions on multidimensional data in their presence has recently become an object of research. However, all models devised so far propose a ``rigid'' view of the multidimensional content, without taking into account variety and variability. To fill this gap, in this paper we propose V-ICSOLAP, an extension of the ICSOLAP UML profile that supports extensibility and type/name variability for each multidimensional element, as well as complex data types for measures and levels. The real case study we use to motivate and illustrate our approach is that of trajectory analysis for agricultural robots. As a proof-of-concept for V-ICSOLAP, we propose an implementation that relies on the PostgreSQL multi-model DBMS and we evaluate its performances. We also provide a validation of our UML profile by ranking it against other meta-models based on a set of quality metrics

Modelling of Spatial Big Data Analysis and Visualization

Today’s advanced survey tools open new approaches and opportunities for Geoscience researchers to create new Models, Systems and frameworks to support the lifecycle of special big data. Mobile Mapping Systems use LIDAR technology to provide efficient and accurate way to collect geographic features and its attribute from field, whichhelps city planning departments and surveyors to design and update city GIS maps with a high accuracy. It is not only about heterogenic increase in the volume of point cloud data, but also it refers to several other characteristics such as its velocity and variety. However,the vast amount of Point Cloud data gathered by Mobile Mapping Systemleads to new challenges for researches, innovation and business development to solve its five characters: Volume, Velocity, Variety, and Veracity then achievethe Value of SBD. Cloud Computing has provided a new paradigm to publish and consume new spatial models as a service plus big data utilities , services which can be utilized to overcome Point Cloud data analysis and visualization challenges. This paper presentsa model With Cloud-Based Spatial,big data Services,using spatial joinservices capabilities to relate the analysis results to its location on map,describe how Cloud Computing supports the visualizing and analyzing spatial big data and review the related scientific model’s examples

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Una extensión a los esquemas preconceptuales para el refinamiento en la representación de eventos y la notación matemática

An event is an occurrence within a particular software system or domain. Software and scientific models are representations of computing and natural systems. Such models have software and scientific components—domain knowledge elements. Scientists and business analysts use such models and their components for recognizing a domain, e.g., pre-conceptual schemas (PCS) used in software engineering. Scientific software domains (SSD) comprise fields in engineering and science, which are focused on developing and simulating scientific software systems for event or phenomenon research. Event-based software development has increased in scientific domains. Approaches to event-driven modeling are used from software/scientific modeling. Some advances have emerged in such approaches for integrating software and scientific components in science and engineering projects. However, scientists and business analysts lack a computational model for SSD in order to integrate both components in the same model. PCS notation includes software components based on structural and dynamic features, which allow for representing events and mathematical operations. Nonetheless, PCS lack scientific components for representing events in SSD. In this Ph.D. Thesis, we propose an extension to pre-conceptual schemas for refining event representation and mathematical notation. Such an extension comprises scientific components as graphical, linguistic, and mathematical structures for the sake of such refinement. We validate our proposal by using both an experimental process and a software application. Extension to PCS is included as a new work product for representing events in SSD. Therefore, the extended PCS are intended to be computing models for scientists and business analysts in scientific software development and simulation processes.Un evento es una ocurrencia en un sistema de software o dominio particular. Los modelos científicos y de software son representaciones de sistemas informáticos o naturales. Esos modelos tienen componentes científicos y de software (elementos del conocimiento del dominio). Científicos y analistas de negocio usan estos modelos y sus componentes para reconocer un dominio. Un ejemplo de esos modelos son los esquemas preconceptuales (EP), que se usan en ingeniería de software. Los dominios de software científico comprenden áreas en ingeniería y ciencia que se enfocan en el desarrollo y simulación de sistemas de software científico para la investigación de eventos o fenómenos. El desarrollo de software dirigido por eventos se viene incrementando en dominios científicos. Enfoques de modelado basado en eventos se usan desde el modelado científico y el modelado de software. En estos enfoques surgen algunos avances para integrar componentes científicos y componentes de software en proyectos de ingeniería y ciencia. Sin embargo, científicos y analistas de negocio carecen de un modelo computacional para dominios de software científico que integre ambos componentes en el mismo modelo. La notación de los EP incluye componentes de software que se basan en características estructurales y dinámicas, los cuales permiten representar eventos y operaciones matemáticas. No obstante, los EP carecen de componentes científicos para representar eventos en dominios de software científico. En esta Tesis Doctoral se propone una extensión a los esquemas preconceptuales para el refinamiento en la representación de eventos y la notación matemática. Esta extensión integra componentes científicos (estructuras gráficas, lingüísticas y matemáticas) para lograr este refinamiento. También, se valida la propuesta mediante un proceso experimental y una aplicación de software. La extensión a los EP se incluye como un nuevo producto de trabajo para representar eventos en dominios de software científico. Por lo tanto, se pretende que los EP extendidos sean modelos de computación, para científicos y analistas de negocio en procesos de desarrollo y simulación de software científico.MincienciasDoctorad

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

原子力プラントのライフサイクル情報管理に基づく廃炉シミュレーションに関する研究

Tohoku University博士（情報科学）thesi

Relatório de Estágio - Solução de BI Roaming Data Science (RoaDS) em ambiente Vodafone

A telecom company (Vodafone), had the need to implement a Business Intelligence solution for Roaming data across a wide set of different data sources. Based on the data visualization of this solution, its key users with decision power, can make a business analysis and needs of infrastructure and software expansion. This document aims to expose the scientific papers produced with the various stages of production of the solution (state of the art, architecture design and implementation results), this Business Intelligence solution was designed and implemented with OLAP methodologies and technologies in a Data Warehouse composed of Data Marts arranged in constellation, the visualization layer was custom made in JavaScript (VueJS). As a base for the results a questionnaire was created to be filled in by the key users of the solution. Based on this questionnaire it was possible to ascertain that user acceptance was satisfactory. The proposed objectives for the implementation of the BI solution with all the requirements was achieved with the infrastructure itself created from scratch in Kubernetes. This BI platform can be expanded using column storage databases created specifically with OLAP workloads in mind, removing the need for an OLAP cube layer. Based on Machine Learning algorithms, the platform will be able to perform the predictions needed to make decisions about Vodafone's Roaming infrastructure