Michael Sioutis

Data Models and Query Languages for Linked Geospatial Data

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.

Consistency of Chordal RCC-8 Networks

We consider chordal RCC-8 networks and show that we can check their consistency by enforcing partial path consistency with weak composition. We prove this by using the fact that RCC-8 networks with relations from the maximal tractable subsets H8, C8, and Q8 of RCC-8 have the patchwork property. The use of partial path consistency has important practical consequences that we demonstrate with the implementation of the new reasoner PyRCC∇, which is developed by extending the state of the art reasoner PyRCC8. Given an RCC-8 network with only tractable RCC-8 relations, we show that it can be solved very efficiently with PyRCC∇ by making its underlying constraint graph chordal and running path consistency on this sparse graph instead of the completion of the given network. In the same way, partial path consistency can be used as the consistency checking step in backtracking algorithms for networks with arbitrary RCC-8 relations resulting in very improved pruning for sparse networks while incurring a penalty for dense networks.

Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, satellite image archives have been constantly increasing in size in the last few years (now reaching petabyte sizes), and have become a valuable source of information for many science and application domains (environment, oceanography, geology, archaeology, security, etc.). TELEIOS is a recent 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 databases, linked geospatial data, ontologies and techniques for discovering knowledge from satellite images and auxiliary data sets. In this paper we outline the vision of TELEIOS (now in its second year), and give details of its original contributions on knowledge discovery from satellite images and auxiliary datasets, ontologies, and linked geospatial data.

Tackling Large Qualitative Spatial Networks of Scale-Free-Like Structure

We improve the state-of-the-art method for checking the consistency of large qualitative spatial networks that appear in the Web of Data by exploiting the scale-free-like structure observed in their underlying graphs. We propose an implementation scheme that triangulates the underlying graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them. We generate random scale-free-like qualitative spatial networks using the Barabasi-Albert (BA) model with a preferential attachment mechanism. We test our approach on the already existing random datasets that have been extensively used in the literature for evaluating the performance of qualitative spatial reasoners, our own generated random scale-free-like spatial networks, and real spatial datasets that have been made available as Linked Data. The analysis and experimental evaluation of our method presents significant improvements over the state-of-the-art approach, and establishes our implementation as the only possible solution to date to reason with large scale-free-like qualitative spatial networks efficiently.

Pushing the Envelope in Graph Compression

We improve the state-of-the-art method for the compression of web and other similar graphs by introducing an elegant technique which further exploits the clustering properties observed in these graphs. The analysis and experimental evaluation of our method shows that it outperforms the currently best method of Boldi et al. by achieving a better compression ratio and retrieval time. Our method exhibits vast improvements on certain families of graphs, such as social networks, by taking advantage of their compressibility characteristics, and ensures that the compression ratio will not worsen for any graph, since it easily falls back to the state-of-the-art method.

An Efficient Approach for Tackling Large Real World Qualitative Spatial Networks

We improve the state-of-the-art method for checking the consistency of large qualitative spatial networks that appear in the Web of Data by exploiting the scale-free-like structure observed in their constraint graphs. We propose an implementation scheme that triangulates the constraint graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them. We generate random scale-free-like qualitative spatial networks using the Barabasi-Albert (BA) model with a preferential attachment mechanism. We test our approach on the already existing random datasets that have been extensively used in the literature for evaluating the performance of qualitative spatial reasoners, our own generated random scale-free-like spatial networks, and real spatial datasets that have been made available as Linked Data. The analysis and experimental evaluation of our method presents significant improvements over the state-of-the-art approach, and establishes our implementation as t...

Building virtual earth observatories using ontologies and linked geospatial data

Advances in remote sensing technologies have enabled public and commercial organizations to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation (EO) data has been constantly increasing in volume in the last few years, and is currently reaching petabytes in many satellite archives. For example, the multi-mission data archive of the TELEIOS partner German Aerospace Center (DLR) is expected to reach 2PB next year, while ESA estimates that it will be archiving 20PB of data before the year 2020. As the volume of data in satellite archives has been increasing, so have the scientific and commercial applications of EO data. Nevertheless, it is estimated that up to 95% of the data present in existing archives has never been accessed, so the potential for increasing exploitation is very big.

On the Effect of Locality in Compressing Social Networks

We improve the state-of-the-art method for graph compression by exploiting the locality of reference observed in social network graphs. We take advantage of certain dense parts of those graphs, which enable us to further reduce the overall space requirements. The analysis and experimental evaluation of our method confirms our observations, as our results present improvements over a wide range of social network graphs.

Ordering Spatio-Temporal Sequences to Meet Transition Constraints: Complexity and Framework

Time and space are fundamental concepts of study in Artificial Intelligence and, in particular, Knowledge Representation. In this paper, we investigate the task of ordering a temporal sequence of qualitative spatial configurations to meet certain transition constraints. This ordering is constrained by the use of conceptual neighbourhood graphs defined on qualitative spatial constraint languages. In particular, we show that the problem of ordering a sequence of qualitative spatial configurations to meet such transition constraints is \(\mathcal{NP}\)-complete for the the well known languages of RCC-8, Interval Algebra, and Block Algebra. Based on this result, we also propose a framework where the temporal aspect of a sequence of qualitative spatial configurations is constrained by a Point Algebra network, and again show that the enhanced problem is in \(\mathcal{NP}\) when considering the aforementioned languages. Our results lie within the area of Graph Traversal and allow for many practical and diverse applications, such as identifying optimal routes in mobile robot navigation, modelling changes of topology in biological processes, and computing sequences of segmentation steps used in image processing algorithms.

A SAT approach for maximizing satisfiability in qualitative spatial and temporal constraint networks

In this paper, we focus on a recently introduced problem in the context of spatial and temporal qualitative reasoning, called the MAX-QCN problem. This problem involves obtaining a spatial or temporal configuration that maximizes the number of satisfied constraints in a qualitative constraint network (QCN). To efficiently solve the MAX-QCN problem, we introduce and study two families of encodings of the partial maximum satisfiability problem (PMAX-SAT). Each of these encodings is based on, what we call, a forbidden covering with regard to the composition table of the considered qualitative calculus. Intuitively, a forbidden covering allows us to express, in a more or less compact manner, the non-feasible configurations for three spatial or temporal entities. The experimentation that we have conducted with qualitative constraint networks from the Interval Algebra shows the interest of our approach.