SEMANTIC LINKING SPATIAL RDF DATA TO THE WEB DATA SOURCES

Large amounts of spatial data are hold in relational databases. Spatial data in the relational databases must be converted to RDF for semantic web applications. Spatial data is an important key factor for creating spatial RDF data. Linked Data is the most preferred way by users to publish and share data in the relational databases on the Web. In order to define the semantics of the data, links are provided to vocabularies (ontologies or other external web resources) that are common conceptualizations for a domain. Linking data of resource vocabulary with globally published concepts of domain resources combines different data sources and datasets, makes data more understandable, discoverable and usable, improves data interoperability and integration, provides automatic reasoning and prevents data duplication. The need to convert relational data to RDF is coming in sight due to semantic expressiveness of Semantic Web Technologies. One of the important key factors of Semantic Web is ontologies. Ontology means “explicit specification of a conceptualization”. The semantics of spatial data relies on ontologies. Linking of spatial data from relational databases to the web data sources is not an easy task for sharing machine-readable interlinked data on the Web. Tim Berners-Lee, the inventor of the World Wide Web and the advocate of Semantic Web and Linked Data, layed down the Linked Data design principles. Based on these rules, firstly, spatial data in the relational databases must be converted to RDF with the use of supporting tools. Secondly, spatial RDF data must be linked to upper level-domain ontologies and related web data sources. Thirdly, external data sources (ontologies and web data sources) must be determined and spatial RDF data must be linked related data sources. Finally, spatial linked data must be published on the web. The main contribution of this study is to determine requirements for finding RDF links and put forward the deficiencies for creating or publishing linked spatial data. To achieve this objective, this study researches existing approaches, conversion tools and web data sources for relational data conversion to the spatial RDF. In this paper, we have investigated current state of spatial RDF data, standards, open source platforms (particularly D2RQ, Geometry2RDF, TripleGeo, GeoTriples, Ontop, etc.) and the Web Data Sources. Moreover, the process of spatial data conversion to the RDF and how to link it to the web data sources is described. The implementation of linking spatial RDF data to the web data sources is demonstrated with an example use case. Road data has been linked to the one of the related popular web data sources, DBPedia. SILK, a tool for discovering relationships between data items within different Linked Data sources, is used as a link discovery framework. Also, we evaluated other link discovery tools e.g. LIMES, Silk and results are compared to carry out matching/linking task. As a result, linked road data is shared and represented as an information resource on the web and enriched with definitions of related different resources. By this way, road datasets are also linked by the related classes, individuals, spatial relations and properties they cover such as, construction date, road length, coordinates, etc

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’)

JedAI-spatial: a system for 3-dimensional Geospatial Interlinking

Τα γεωχωρικά δεδομένα αποτελούν ένα σημαντικό κομμάτι των δεδομένων του Σημασιολογικού Ιστού (Semantic Web), αλλά μέχρι στιγμής οι πηγές του δεν περιέχουν αρκετούς συνδέσμους στο Linked Open Data cloud. Η Διασύνδεση Γεωχωρικών Δεδομένων (Geospatial Interlinking) έχει ως στόχο να καλύψει αυτό το κενό συνδέοντας τις γεωμετρίες με καθιερωμένες τοπολογικές σχέσεις, όπως αυτές του Dimensionally Extended 9-Intersection Model. Έχουν προταθεί διάφοροι αλγόριθμοι στη βιβλιογραφία για την επίλυση αυτού του προβληματος. Στο πλαίσιο αυτής της διπλωματικής εργασίας, αναπτύσσουμε το JedAI-spatial, ένα καινοτόμο σύστημα ανοιχτού κώδικα, το οποίο οργανώνει τους κύριους υπάρχοντες αλγορίθμους σύμφωνα με τρεις διαστάσεις: i. Το Space Tiling διαφοροποιεί τους αλγόριθμους διασύνδεσης σε αυτούς που βασίζονται σε πλέγμα (grid-based), δέντρα (tree-based) ή κατατμήσεις (partition-based), σύμφωνα με την μέθοδο τους για τη μείωση του χώρου αναζήτησης και συνεπώς της τετραγωνικής πολυπλοκότητας αυτού του προβλήματος. Η πρώτη κατηγορία περιέχει τεχνικές Σημασιολογικού Ιστού, η δεύτερη καθιερωμένες τεχνικές για χωρική διασύνδεση (spatial join) στην κύρια μνήμη από την κοινότητα των βάσεων δεδομένων , ενώ η τρίτη περιλαμβάνει παραλλαγές του βασικού αλγορίθμου plane-sweep της υπολογιστικής γεωμετρίας. ii. Το Budget awareness διαχωρίζει τους αλγόριθμους διασύνδεσης σε budget-agnostic και budget-aware. Οι μέν απαρτίζονται από batch τεχνικές, που παράγουν αποτελέσματα μόνο μετά την επεξεργασία όλων των δεδομένων, ενώ οι δε λειτουργούν με έναν προοδευτικό τρόπο που παράγει αποτελέσματα σταδιακά - ο στόχος τους είναι να επικυρώσουν τις τοπολογικά συσχετιζόμενες γεωμετρίες πριν από τις μη-συσχετιζόμενες. iii. Η Μέθοδος Εκτέλεσης διαφοροποιεί τους αλγορίθμους σε σειριακούς, οι οποίοι εκτελούνται χρησιμοποιώντας ένα πυρήνα (CPU core), και παράλληλους (parallel), οι οποίοι αξιοποιούν την κατανεμημένη εκτέλεση πάνω στο Apache Spark. Στα πλαίσια της διπλωματικής πραγματοποιήθηκαν εκτενή πειράματα με τις μεθόδους και των 3 διαστάσεων, με τα πειραματικά αποτελέσματα να παρέχουν μία ενδιαφέρουσα εικόνα όσον αφορά τη σχετική απόδοση των αλγορίθμων.Geospatial data constitutes a considerable part of Semantic Web data, but so far, its sources lack enough links in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with established topological relations, such as those of the Dimensionally Extended 9-Intersection Model. Various algorithms have already been proposed in the literature for this task. In the context of this master thesis, we develop JedAI-spatial, a novel, open-source system that organizes the main existing algorithms according to three dimensions: i. Space Tiling distinguishes interlinking algorithms into grid-, tree- and partition-based, according to their approach for reducing the search space and, thus, the computational cost of this inherently quadratic task. The former category includes Semantic Web techniques that define a static or dynamic EquiGrid and verify pairs of geometries whose minimum bounding rectangles intersect at least one common cell. Tree-based algorithms encompass established main-memory spatial join techniques from the database community, while the partition-based category includes variations of the cornerstone of computational geometry, i.e., the plane sweep algorithm. ii. Budget-awareness distinguishes interlinking algorithms into budget-agnostic and budget-aware ones. The former constitute batch techniques that produce results only after completing their processing over the entire input data, while the latter operate in a pay-as-you-go manner that produces results progressively - their goal is to verify related geometries before the non-related ones. iii. Execution mode distinguishes interlinking algorithms into serial ones, which are carried out using a single CPU-core, and parallel ones, which leverage massive parallelization on top of Apache Spark. Extensive experimental evaluations were performed along these 3 dimensions, with the experimental outcomes providing interesting insights about the relative performance of the considered algorithms

GeoXBRL: Integration Standard between Geographical and Business Data

Context: The usage of geographic data and geovisualization in financial auditing and analytics is increasing among auditors and accountants. Business information systems can process and analyze data through geographic approaches (e.g. financial insight on a virtual geographic map). Although many business information systems have been developed considering geographic data as one of data sources, there is no a standard for modeling both kind of data together (i.e. business and geographical). Conversely, it is also known that Geographic Markup Language (GML) and eXtensible Business Reporting Language (XBRL) are W3C technologies worldwide used for representing geographic and business data, respectively. Given this absence of standardization to represent geospatial data within business taxonomies, this paper proposes the GeoXBRL to fill this gap. This paper specifies an integration between geographic and business/financial data. For this, W3C technologies such as XLink, XML schema, GML and XBRL have been used to make the data structure of this integration. As an assessment, a XML-based representation and a Java-based web application have been developed on a real-world business taxonomy (available on US-SEC website). A comparison is shown between previous business data scenario and the new one proposed in this paper. Finally, this proposal allows to explain how to use the GeoXBRL. Some comparisons with current tools and technologies are shown in order to illustrate the GeoXBRL features and contributions

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

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

Casual Information Visualization on Exploring Spatiotemporal Data

The goal of this thesis is to study how the diverse data on the Web which are familiar to everyone can be visualized, and with a special consideration on their spatial and temporal information. We introduce novel approaches and visualization techniques dealing with different types of data contents: interactively browsing large amount of tags linking with geospace and time, navigating and locating spatiotemporal photos or videos in collections, and especially, providing visual supports for the exploration of diverse Web contents on arbitrary webpages in terms of augmented Web browsing