SexTant: Visualizing Time-Evolving Linked Geospatial Data

We present SexTant, a Web-based system for the visualization and exploration of time-evolving linked geospatial data and the creation, sharing, and collaborative editing of "temporally-enriched" thematic maps which are produced by combining dierent sources of such data

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems

Improving knowledge discovery from synthetic aperture radar images using the linked open data cloud and Sextant

In the last few years, thanks to projects like TELEIOS, the linked open data cloud has been rapidly populated with geospatial data some of it describing Earth Observation products (e.g., CORINE Land Cover, Urban Atlas). The abundance of this data can prove very useful to the new missions (e.g., Sentinels) as a means to increase the usability of the millions of images and EO products that are expected to be produced by these missions. In this paper, we explain the relevant opportunities by demonstrating how the process of knowledge discovery from TerraSAR-X images can be improved using linked open data and Sextant, a tool for browsing and exploration of linked geospatial data, as well as the creation of thematic maps

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems

Wildfire monitoring using satellite images, ontologies and linked geospatial data

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications

A Semantic Safety Check System for Emergency Management

There has been an exponential growth and availability of both structured and unstructured data that can be leveraged to provide better emergency management in case of natural disasters and humanitarian crises. This paper is an extension of a semantics-based web application for safety check, which uses of semantic web technologies to extract different kinds of relevant data about a natural disaster and alerts its users. The goal of this work is to design and develop a knowledge intensive application that identifies those people that may have been affected due to natural disasters or man-made disasters at any geographical location and notify them with safety instructions. This involves extraction of data from various sources for emergency alerts, weather alerts, and contacts data. The extracted data is integrated using a semantic data model and transformed into semantic data. Semantic reasoning is done through rules and queries. This system is built using front-end web development technologies and at the back-end using semantic web technologies such as RDF, OWL, SPARQL, Apache Jena, TDB, and Apache Fuseki server. We present the details of the overall approach, process of data collection and transformation and the system built. This extended version includes a detailed discussion of the semantic reasoning module, research challenges in building this software system, related work in this area, and future research directions including the incorporation of geospatial components and standards

Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service

Developing Ontology-based Data access techniques for temporal data

Η παρούσα πτυχιακή εργασία αφορά τη επέκταση του συστήματος Ontop-spatial για την υποστήριξη χρονικών δεδομένων (temporal data). Το Ontop-Spatial είναι ένα σύστημα το οποίο υποστηρίζει την επεξεργασία γεωχωρικών επερωτήσεων στη γλώσσα GeoSPARQL, επαναγράφοντάς τις στη γλώσσα SQL ώστε να αποττιμηθούν τελικά σε μια γεωχωρική βάση δεδομένων που είναι συνδεδεμένη με το Ontop-spatial. Με παρόμοια τεχνική, χρησιμοποιώντας οντολογίες και αντιστοιχήσεις δεδομένων από το σχεσιακό μοντέλο στο μοντέλο RDF γίνεται δυνατή η μετάφραση σε πραγματικό χρόνο και των χρονικών επερωτήσεων που εκφράζονται στη γλώσσα stSPARQL στα αντίστοιχα SQL ερωτήματα τα οποία μπορούν να αποτιμηθούν σε μια χρονική βάση δεδομένων. Η γλώσσα stSPARQL είναι μια επέκταση της γλώσσας SPARQL με χρονικά και γεωχωρικά χαρακτηριστικά. Στην παρούσα πτυχιακή περιγράφουμε την προσέγγισή μας και διεξάγουμε πειραματική μελέτη για να αξιολογήσουμε την απόδοση της υλοποίησης.We propose an additional enhancement on top of the already extended Ontop's SPARQL-to-SQL translation, which supports geospatial data, with the addition of stSPARQL-to-SQL translation regarding temporal data. Ontop is a mature, open-source Ontology-Based Data Access (OBDA) system that allows posing SPARQL queries on top of relational data sources through provided ontologies and mappings. The system Ontop-spatial is an extension of the system Ontop that performs on-the-fly GeoSPARQL-to-SQL translation on top of geospatial enabled databases. GeoSPARQL is a spatial extension of the SPARQL query language and has been standardized by OGC. In this thesis, we extend the system Ontop-spatial with the ability to execute temporal queries as well, on top of temporal enabled databases

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop

Representation and querying of valid time of triples in linked geospatial data

We introduce the temporal component of the stRDF data model and the stSPARQL query language, which have been recently proposed for the representation and querying of linked geospatial data that changes over time. With this temporal component in place, stSPARQL becomes a very expressive query language for linked geospatial data, going beyond the recent OGC standard GeoSPARQL, which has no support for valid time of triples. We present the implementation of the stSPARQL temporal component in the system Strabon, and study its performance experimentally. Strabon is shown to outperform all the systems it has been compared with. © 2013 Springer-Verlag Berlin Heidelberg