Concepts for the Representation, Storage, and Retrieval of Spatio-Temporal Objects in 3D/4D Geo-Informations-Systems

The quickly increasing number of spatio-temporal applications in fields like environmental management or geology is a new challenge to the development of database systems. This thesis addresses three areas of the problem of integrating spatio-temporal objects into databases. First, a new representational model for continuously changing, spatial 3D objects is introduced and transferred into a small system of classes within an object-oriented database framework. The model extends simplicial cell complexes to the spatio-temporal setting. The problem of closure under certain operations is investigated. Second, internal data structures are introduced that represent instances of the (user-level) spatio-temporal classes. A new technique provides a compromise between compact storage and efficient retrieval of spatio-temporal objects. These structures correspond to temporal graphs and support updates as well as the maintainance of connected components over time. Third, it is shown how to realise further operations on the new type of objects. Among these operations are range queries, intersection tests, and the Euclidean distance function

Towards a compact representation of temporal rasters

Big research efforts have been devoted to efficiently manage spatio-temporal data. However, most works focused on vectorial data, and much less, on raster data. This work presents a new representation for raster data that evolve along time named Temporal k^2 raster. It faces the two main issues that arise when dealing with spatio-temporal data: the space consumption and the query response times. It extends a compact data structure for raster data in order to manage time and thus, it is possible to query it directly in compressed form, instead of the classical approach that requires a complete decompression before any manipulation. In addition, in the same compressed space, the new data structure includes two indexes: a spatial index and an index on the values of the cells, thus becoming a self-index for raster data.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941. Published in SPIRE 201

Markov Chain Methods For Analyzing Complex Transport Networks

We have developed a steady state theory of complex transport networks used to model the flow of commodity, information, viruses, opinions, or traffic. Our approach is based on the use of the Markov chains defined on the graph representations of transport networks allowing for the effective network design, network performance evaluation, embedding, partitioning, and network fault tolerance analysis. Random walks embed graphs into Euclidean space in which distances and angles acquire a clear statistical interpretation. Being defined on the dual graph representations of transport networks random walks describe the equilibrium configurations of not random commodity flows on primary graphs. This theory unifies many network concepts into one framework and can also be elegantly extended to describe networks represented by directed graphs and multiple interacting networks.Comment: 26 pages, 4 figure