The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets

Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s$\cdot$he wants to explore other facets of a multi-facetted information space. For instance, in a multimedia dataset different facets such as keywords, authors, publication category, organisations and figures can be of interest. The facet simultaneous visualisation can help to gain insights on the information retrieved and call for further searches. Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of multisets -- and are in fact linked not only to the publication itself, but to any chosen reference. These references allow to navigate inside the dataset and perform visual queries. We explore here the case of scientific publications based on Arxiv searches.Comment: Extension of the hypergraph framework shortly presented in arXiv:1809.00164 (possible small overlaps); use the theoretical framework of hb-graphs presented in arXiv:1809.0019

A web service based on RESTful API and JSON Schema/JSON Meta Schema to construct knowledge graphs

Data visualisation assists domain experts in understanding their data and helps them make critical decisions. Enhancing their cognitive insight essentially relies on the capability of combining domain-specific semantic information with concepts extracted out of the data and visualizing the resulting networks. Data scientists have the challenge of providing tools able to handle the overall network lifecycle. In this paper, we present how the combination of two powerful technologies namely the REST architecture style and JSON Schema/JSON Meta Schema enable data scientists to use a RESTful web service that permits the construction of knowledge graphs, one of the preferred representations of large and semantically rich networks.Comment: 5 pages; 6 figure

Visualization and analytics of codicological data of Hebrew books

The goal is to provide a proper data model, using a common vocabulary, to decrease the heterogenous nature of these datasets as well as its inherent uncertainty caused by the descriptive nature of the field of Codicology. This research project was developed with the goal of applying data visualization and data mining techniques to the field of Codicology and Digital Humanities. Using Hebrew manuscript data as a starting point, this dissertation proposes an environment for exploratory analysis to be used by Humanities experts to deepen their understanding of codicological data, to formulate new, or verify existing, research hypotheses, and to communicate their findings in a richer way. To improve the scope of visualizations and knowledge discovery we will try to use data mining methods such as Association Rule Mining and Formal Concept Analysis. The present dissertation aims to retrieve information and structure from Hebrew manuscripts collected by codicologists. These manuscripts reflect the production of books of a specific region, namely "Sefarad" region, within the period between 10th and 16th.A presente dissertação tem como objetivo obter conhecimento estruturado de manuscritos hebraicos coletados por codicologistas. Estes manuscritos refletem a produção de livros de uma região específica, nomeadamente a região "Sefarad", no período entre os séculos X e XVI. O objetivo é fornecer um modelo de dados apropriado, usando um vocabulário comum, para diminuir a natureza heterogénea desses conjuntos de dados, bem como sua incerteza inerente causada pela natureza descritiva no campo da Codicologia. Este projeto de investigação foi desenvolvido com o objetivo de aplicar técnicas de visualização de dados e "data mining" no campo da Codicologia e Humanidades Digitais. Usando os dados de manuscritos hebraicos como ponto de partida, esta dissertação propõe um ambiente para análise exploratória a ser utilizado por especialistas em Humanidades Digitais e Codicologia para aprofundar a compreensão dos dados codicológicos, formular novas hipóteses de pesquisa, ou verificar existentes, e comunicar as suas descobertas de uma forma mais rica. Para melhorar as visualizações e descoberta de conhecimento, tentaremos usar métodos de data mining, como a "Association Rule Mining" e "Formal Concept Analysis"

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Revisiting Urban Dynamics through Social Urban Data

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?  To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.  After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.  A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.  The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities