Dynamic Branching in Qualitative Constraint Networks via Counting Local Models

We introduce and evaluate dynamic branching strategies for solving Qualitative Constraint Networks (QCNs), which are networks that are mostly used to represent and reason about spatial and temporal information via the use of simple qualitative relations, e.g., a constraint can be "Task A is scheduled after or during Task C". In qualitative constraint-based reasoning, the state-of-the-art approach to tackle a given QCN consists in employing a backtracking algorithm, where the branching decisions during search are governed by the restrictiveness of the possible relations for a given constraint (e.g., after can be more restrictive than during). In the literature, that restrictiveness is defined a priori by means of static weights that are precomputed and associated with the relations of a given calculus, without any regard to the particulars of a given network instance of that calculus, such as its structure. In this paper, we address this limitation by proposing heuristics that dynamically associate a weight with a relation, based on the count of local models (or local scenarios) that the relation is involved with in a given QCN; these models are local in that they focus on triples of variables instead of the entire QCN. Therefore, our approach is adaptive and seeks to make branching decisions that preserve most of the solutions by determining what proportion of local solutions agree with that decision. Experimental results with a random and a structured dataset of QCNs of Interval Algebra show that it is possible to achieve up to 5 times better performance for structured instances, whilst maintaining non-negligible gains of around 20% for random ones

Collective Singleton-Based Consistency for Qualitative Constraint Networks

Partial singleton closure under weak composition, or partial singleton (weak) path-consistency for short, is essential for approximating satisfiability of qualitative constraints networks. Briefly put, partial singleton path-consistency ensures that each base relation of each of the constraints of a qualitative constraint network can define a singleton relation in the corresponding partial closure of that network under weak composition, or in its corresponding partially (weak) path-consistent subnetwork for short. In particular, partial singleton path-consistency has been shown to play a crucial role in tackling the minimal labeling problem of a qualitative constraint network, which is the problem of finding the strongest implied constraints of that network. In this paper, we propose a stronger local consistency that couples partial singleton path-consistency with the idea of collectively deleting certain unfeasible base relations by exploiting singleton checks. We then propose an efficient algorithm for enforcing this consistency that, given a qualitative constraint network, performs fewer constraint checks than the respective algorithm for enforcing partial singleton path-consistency in that network. We formally prove certain properties of our new local consistency, and motivate its usefulness through demonstrative examples and a preliminary experimental evaluation with qualitative constraint networks of Interval Algebra

On neighbourhood singleton-style consistencies for qualitative spatial and temporal reasoning

Given a qualitative constraint network (QCN), a singleton-style consistency focuses on each base relation (atom) of a constraint separately, rather than the entire constraint altogether. This local consistency is essential for tackling fundamental reasoning problems associated with QCNs, such as minimal labeling, but can suffer from redundant constraint checks, especially when checks occur far from where the pruning usually takes place. In this paper, we propose singleton-style consistencies that are applied just on the neighbourhood of a singleton-checked constraint instead of the whole network. We make a theoretical comparison with existing consistencies and consequently prove some properties of the new ones. Further, we propose algorithms to enforce our consistencies, as well as parsimonious variants thereof, that are more efficient in practice than the state of the art. An experimental evaluation with random and structured QCNs of Allen's Interval Algebra in the phase transition region demonstrates the potential of our approach.acceptedVersionPeer reviewe

Wildfire monitoring via the integration of remote sensing with innovative information technologies

In the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA) volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers, including land use/land cover, administrative boundaries, road and rail network, points of interest, and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters and that have activated Emergency Support Services at a European level in the framework of the operational GMES projects SAFER and LinkER. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA's in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integration of state-of-the-art Information Technologies that have become available in the framework of the TELEIOS (EC/ICT) project. TELEIOS aims at the development of fully automatic processing chains reliant on a) the effective storing and management of the large amount of EO and GIS data, b) the post-processing refinement of the fire products using semantics, and c) the creation of thematic maps and added-value services. The first objective is achieved with the use of advanced Array Database technologies, such as MonetDB, to enable efficiency in accessing large archives of image data and metadata in a fully transparent way, without worrying for their format, size, and location, as well as efficiency in processing such data using state-of-the-art implementations of image processing algorithms expressed in a high-level Scientific Query Language (SciQL). The product refinement is realized through the application of update operations that incorporate human evidence and human logic, with semantic content extracted from thematic information coming from auxiliary geo-information layers and sources, for reducing considerably the number of false alarms in fire detection, and improving the credibility of the burnt area assessment. The third objective is approached via the combination of the derived fire-products with Linked Geospatial Data, structured accordingly and freely available in the web, using Semantic Web technologies. These technologies are built on top of a robust and modular computational environment, to facilitate several wildfire applications to run efficiently, such as real-time fire detection, fire-front propagation monitoring, rapid burnt area mapping, after crisis detailed burnt scar mapping, and time series analysis of burnt areas. The approach adopted allows ISARS/NOA to routinely serve requests from the end-user community, irrespective of the area of interest and its extent, the observation time period, or the data volume involved, granting the opportunity to combine innovative IT solutions with remote sensing techniques and

Operational Wildfire Monitoring and Disaster Management Support Using State-of-the-art EO and Information Technologies

Fires have been one of the main driving forces in the evolution of plants and ecosystems, determining the current structure and composition of the Landscapes. However, significant alterations in the fire regime have occurred in the recent decades, primarily as a result of socioeconomic changes, increasing dramatically the catastrophic impacts of wildfires as it is reflected in the increase during the 20th century of both, number of fires and the annual area burnt. Therefore, the establishment of a permanent robust fire monitoring system is of paramount importance to implement an effective environmental management policy. Such an integrated system has been developed in the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA). Volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters in the framework of the operational GMES projects SAFER and LinkER addressing fire emergency response and emergency support needs for the entire European Union. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA’s in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integra

Architecture for the heterogeneous federation of future internet experimentation facilities

International audienceInternet systems are currently too complex to be entirely designed in advance and therefore must be thoroughly evaluated in realistic environments. Experimentally driven research is at the heart of Future Internet Research and Experiment (FIRE) facilities, which target various experimenter profiles, ranging from core Internet communities and sensor networks to clouds and web services. Such facilities exist in relative isolation to the detriment of innovative research ideas that could arise from the mixture of their diverse technologies and resources, and their combined power. Internet research communities can benefit from gaining access to a larger number and variety of resources through a federation of these facilities. To this end, we present an architecture to support such a federation of Future Internet experimentation facilities, based on use cases and requirements from infrastructure owners, as well as services and first line support communities

Operational wildfire monitoring and disaster management support using state-of-the-art EO and Information Technologies

textabstractFires have been one of the main driving forces in the evolution of plants and ecosystems, determining the current structure and composition of the Landscapes. However, significant alterations in the fire regime have occurred in the recent decades, primarily as a result of socioeconomic changes, increasing dramatically the catastrophic impacts of wildfires as it is reflected in the increase during the 20th century of both, number of fires and the annual area burnt. Therefore, the establishment of a permanent robust fire monitoring system is of paramount importance to implement an effective environmental management policy. Such an integrated system has been developed in the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA). Volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters in the framework of the operational GMES projects SAFER and LinkER addressing fire emergency response and emergency support needs for the entire European Union. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA’s in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integration of innovative Information Technologies that have become available in the framework of the TELEIOS (EC/ICT) project. Through this activity a fully automatic processing chain has been developed reliant on, a) the effective storing and management of the large amount of EO and GIS data, b) the post-processing refinement of the fire products using semantics, and c) the timely creation of fire extent and damage thematic maps. These technologies are built on top of a robust and modular computational environment, to facilitate several wildfire applications to run efficiently, such as real-time fire detection, fire-front propagation monitoring, rapid burnt area mapping, after crisis detailed burnt scar mapping, and time series analysis of burnt areas. The approach adopted allows ISARS/NOA to routinely serve requests from the end-user community, such as Civil Protection and Forestry Services, irrespective of the location and size of the area of interest, the observation time period, or the size of data volume involved, granting the opportunity to combine innovative IT solutions with remote sensing techniques and algorithms for wildfire monitoring and management

Building Virtual Earth Observatories using Ontologies and Linked Geospatial Data

TELEIOS is a European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery of knowledge that can be used in applications. To achieve this, TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults), Semantic Web technologies (stRDF and stSPARQL) and linked geospatial data. In this technical communication we outline the TELEIOS advancements to the state of the art and give an overview of its technical contributions up to today