Search

The linked open data cloud is constantly evolving as datasets get continuously updated with newer versions. As a result, representing, querying, and visualizing the temporal dimension of linked data is crucial. This is especially important for geospatial datasets that form the backbone of large scale open data publication efforts in many sectors of the economy (e.g., the public sector, the Earth Observation sector). Although there has been some work on the representation and querying of linked geospatial data that change over time, to the best of our knowledge, there is currently no tool that offers spatio-temporal visualization of such data. This is in contrast with the existence of many tools for the visualization of the temporal evolution of geospatial data in the GIS area. In this article, 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 different sources of such data. We present the architecture of Sextant, give examples of its use and present applications in which we have deployed it. © 2015 Elsevier B.V.

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. © 2014 Elsevier B.V. All rights reserved.

We present a real-time wildfire monitoring service that exploits satellite images and linked geospatial data to detect hotspots and monitor the evolution of fire fronts. The service makes heavy use of scientific database technologies (array databases, SciQL, data vaults) and linked data technologies (ontologies, linked geospatial data, stSPARQL) and is implemented on top of MonetDB and Strabon. The service is now operational at the National Observatory of Athens and has been used during the previous summer by emergency managers monitoring wildfires in Greece.

Strabon is a very scalable and efficient RDF store for storing and querying geospatial data that changes over time. We present the geospatial and temporal features of Strabon and we demonstrate their utilization in the fire monitoring and the burn scar mapping applications of the National Observatory of Athens. © 2013 Springer-Verlag.

We present Strabon, a new RDF store that supports the state of the art semantic geospatial query languages stSPARQL and GeoSPARQL. To illustrate the expressive power offered by these query languages and their implementation in Strabon, we concentrate on the new version of the data model stRDF and the query language stSPARQL that we have developed ourselves. Like GeoSPARQL, these new versions use OGC standards to represent geometries where the original versions used linear constraints. We study the performance of Strabon experimentally and show that it scales to very large data volumes and performs, most of the times, better than all other geospatial RDF stores it has been compared with. © 2012 Springer-Verlag Berlin Heidelberg.

The recent availability of geospatial information as linked open data has generated new interest in geospatial query processing and reasoning, a topic with a long tradition of research in the areas of databases and artificial intelligence. In this paper we survey recent advances in this important research topic, concentrating on issues of data modeling and querying. © 2012 Springer-Verlag.

TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults) and Semantic Web technologies (stRDF and stSPARQL) implemented on top of a state of the art column store database system (MonetDB). We demonstrate a first prototype of the TELEIOS Virtual Earth Observatory (VEO) architecture, using a forest fire monitoring application as example.

Hedrocele represents an unusual variant of the rare posterior perineal hernia and results from a defect in the rectogenital septum. We report two cases of obstructive defecation syndrome (ODS) related to presence of hedrocele successfully treated by laparoscopy-assisted transanal surgery.

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England. © 2011 by the authors; licensee MDPI, Basel, Switzerland.

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. flood emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a flood emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor-Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application. © 2011 Springer-Verlag Berlin Heidelberg.

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. flood emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a flood emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor-Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application. © 2011 Springer-Verlag Berlin Heidelberg.

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. good emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a good emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor-Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application.

The new Italian cytological classification (SIAPEC 2014) of thyroid nodules, in line with those of Bethesda and BTA-RCPath, replaces the previous TIR3 class with two new classes (TIR3A and TIR3B), which correspond to different risks of malignancy and clinical actions required. The present study was conducted to evaluate the diagnostic accuracy of the new SIAPEC classification as opposed to its previous version (SIAPEC 2007). Preoperative cytology was compared with the final histology obtained from 650 consecutive patients who underwent total thyroidectomy for multinodular goiter. Of this total, 434 patients (group A) had their cytological diagnosis based on the old SIAPEC 2007 classification and 216 patients (group B) had their cytological diagnosis based on the SIAPEC 2014 classification. In group A 111 patients (25.6%) had a TIR3 diagnosis, while in group B 52 patients (24.1%) received a TIR3 diagnosis, of whom 30 had TIR3A and 22 had TIR3B. In group A, 46 (41.4%) out of the 111 patients with TIR3 diagnosis had, based on histology, a thyroid carcinoma. In group B, only 2 (6.7%) out of 30 patients with TIR3A diagnosis had a thyroid carcinoma. This rate of malignancy was significantly lower (p less than 0.001) than that observed in patients with TIR3B diagnosis, in which 12 (54.5%) out of 22 patients had a carcinoma. The observations here reported show that, in respect to the previous version, the new Italian cytological classification provides greater diagnostic accuracy for detecting thyroid nodule malignancy. The new Italian cytological classification (SIAPEC 2014) of thyroid nodules, in line with those of Bethesda and BTA-RCPath, replaces the previous TIR3 class with two new classes (TIR3A and TIR3B), which correspond to different risks of malignancy and clinical actions required. The present study was conducted to evaluate the diagnostic accuracy of the new SIAPEC classification as opposed to its previous version (SIAPEC 2007). Preoperative cytology was compared with the final histology obtained from 650 consecutive patients who underwent total thyroidectomy for multinodular goiter. Of this total, 434 patients (group A) had their cytological diagnosis based on the old SIAPEC 2007 classification and 216 patients (group B) had their cytological diagnosis based on the SIAPEC 2014 classification. In group A 111 patients (25.6%) had a TIR3 diagnosis, while in group B 52 patients (24.1%) received a TIR3 diagnosis, of whom 30 had TIR3A and 22 had TIR3B. In group A, 46 (41.4%) out of the 111 patients with TIR3 diagnosis had, based on histology, a thyroid carcinoma. In group B, only 2 (6.7%) out of 30 patients with TIR3A diagnosis had a thyroid carcinoma. This rate of malignancy was significantly lower (p

As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as increasingly heterogeneous data formats. Still, state-of-the-art approaches typically rely on copying and transforming data into one (or few) repositories. Queries, on the other hand, are often ad-hoc and supported by pre-cooked operators which are not adaptive enough to optimize access to data. As data formats and queries increasingly vary, there is a need to depart from the current status quo of static query processing primitives and build dynamic, fully adaptive architectures. We build ViDa, a system which reads data in its raw format and processes queries using adaptive, just-in-time operators. Our key insight is use of virtualization, i.e., abstracting data and manipulating it regardless of its original format, and dynamic generation of operators. ViDa's query engine is generated just-in-time; its caches and its query operators adapt to the current query and the workload, while also treating raw datasets as its native storage structures. Finally, ViDa features a language expressive enough to support heterogeneous data models, and to which existing languages can be translated. Users therefore have the power to choose the language best suited for an analysis.