Exploration and Visualization of Big Graphs - The DBpedia Case Study

Increasingly, the data and information visualization is becoming strategic for the exploration and explanation of large data sets. The Big Data paradigm pushes for new ways, new technological solutions to deal with the big volume and the big variety of data today. Not surprisingly, a plethora of new tools have emerged, each of them with pros and cons, but all espousing the cause of "Bigness of Data". In this paper, we take one of this emerging tools, namely Neo4J, and stress its capabilities in order to import, query and visualize data coming from a \emph{big} case study: DBpedia. We will describe each step in this study focusing on the used strategies for overcoming the different problems mainly due to the intricate nature of the case study and its volume. We confront with both the intensional schema of DBpedia and its extensional part in order to obtain the best result in its visualization. Finally, an attempt to define some criteria to simplify the large-scale visualization of DBpedia will be made, providing some examples and considerations which have arisen. The ultimate goal of this work is to investigate techniques and approaches to get more insights from the visual representation and analytics of large graph databases

A comparative study of state-of-the-art linked data visualization tools

Data visualization tools are of great importance for the exploration and the analysis of Linked Data (LD) datasets. Such tools allow users to get an overview, understand content, and discover interesting insights of a dataset. Visualization approaches vary according to the domain, the type of data, the task that the user is trying to perform, as well as the skills of the user. Thus, the study of the capabilities that each approach offers is crucial in supporting users to select the proper tool/technique based on their need. In this paper we present a comparative study of the state-of-the-art LD visualization tools over a list of fundamental use cases. First, we define 16 use cases that are representative in the setting of LD visual exploration, examining several tool's aspects; e.g., functionality capabilities, feature richness. Then, we evaluate these use cases over 10 LD visualization tools, examining: (1) if the tools have the required functionality for the tasks; and (2) if they allow the successful completion of the tasks over the DBpedia dataset. Finally, we discuss the insights derived from the evaluation, and we point out possible future directions

Dynamic Discovery of Type Classes and Relations in Semantic Web Data

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method

A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration

This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data. The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the ‘Best Student Paper Award’)

Exploring a text corpus via a knowledge graph

Semantic enrichment methods may be used to identify relevant entities in textual documents. These extracted entities are part of knowledge graphs and thus linked by semantic relationships. This work explores the idea of navigating the semantic relationships among extracted entities as a way to search a text corpus. A modular software system (including document management, semantic enrichment, data consolidation, and data integration) has been designed, to offer a visual user interface for such navigation on top of an arbitrary corpus of textual documents. The software, called arca, has been used in a real use case: to search in the book catalogue of a publishing house. The evaluation carried out with a set of potential users has shown so far the feasibility and effectiveness of the approach. Critical issues and potential limitations of the paradigm have also been found and are discussed

Analytical metadata modeling for next generation BI systems

Business Intelligence (BI) systems are extensively used as in-house solutions to support decision-making in organizations. Next generation BI 2.0 systems claim for expanding the use of BI solutions to external data sources and assisting the user in conducting data analysis. In this context, the Analytical Metadata (AM) framework defines the metadata artifacts (e.g., schema and queries) that are exploited for user assistance purposes. As such artifacts are typically handled in ad-hoc and system specific manners, BI 2.0 argues for a flexible solution supporting metadata exploration across different systems. In this paper, we focus on the AM modeling. We propose SM4AM, an RDF-based Semantic Metamodel for AM. On the one hand, we claim for ontological metamodeling as the proper solution, instead of a fixed universal model, due to (meta)data models heterogeneity in BI 2.0. On the other hand, RDF provides means for facilitating defining and sharing flexible metadata representations. Furthermore, we provide a method to instantiate our metamodel. Finally, we present a real-world case study and discuss how SM4AM, specially the schema and query artifacts, can help traversing different models instantiating our metamodel and enabling innovative means to explore external repositories in what we call metamodel-driven (meta)data exploration.Peer ReviewedPostprint (author's final draft

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