GraCT: A Grammar based Compressed representation of Trajectories

We present a compressed data structure to store free trajectories of moving objects (ships over the sea, for example) allowing spatio-temporal queries. Our method, GraCT, uses a $k^2$-tree to store the absolute positions of all objects at regular time intervals (snapshots), whereas the positions between snapshots are represented as logs of relative movements compressed with Re-Pair. Our experimental evaluation shows important savings in space and time with respect to a fair baseline.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094

PA-Tree: A Parametric Indexing Scheme for Spatio-temporal Trajectories

Abstract. Many new applications involving moving objects require the collec-tion and querying of trajectory data, so efficient indexing methods are needed to support complex spatio-temporal queries on such data. Current work in this domain has used MBRs to approximate trajectories, which fail to capture some basic properties of trajectories, including smoothness and lack of internal area. This mismatch leads to poor pruning when such indices are used. In this work, we revisit the issue of using parametric space indexing for historical trajectory data. We approximate a sequence of movement functions with single continuous polynomial. Since trajectories tend to be smooth, our approximations work well and yield much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this new approximation method. Experiments show that PA-tree construction costs are orders of magnitude lower than that of com-peting methods. Further, for spatio-temporal range queries, MBR-based methods require 20%–60 % more I/O than PA-trees with clustered indicies, and 300%– 400 % more I/O than PA-trees with non-clustered indicies.

Mining Dense Regions from Vehicular Mobility in Streaming Setting

The detection of congested areas can play an important role in the development of systems of traffic management. Usually, the problem is investigated under two main perspectives which concern the representation of space and the shape of the dense regions respectively. However, the adoption of movement tracking technologies enables the generation of mobility data in a streaming style, which adds an aspect of complexity not yet addressed in the literature. We propose a computational solution to mine dense regions in the urban space from mobility data streams. Our proposal adopts a stream data mining strategy which enables the detection of two types of dense regions, one based on spatial closeness, the other one based on temporal proximity. We prove the viability of the approach on vehicular data streams in the urban space

Untersuchungen zur Heissrissneigung vollaustenitischer unstabilisierter Stähle des Typs X 3 Cr Ni 16 16 beim Unterpulverschweissen

Abstract. The development of a spatiotemporal access method suitable for objects moving on fixed networks is a very attractive challenge due to the great number of real-world spatiotemporal database applications and fleet management systems dealing with this type of objects. In this work, a new indexing technique, named Fixed Network R-Tree (FNR-Tree), is proposed for objects constrained to move on fixed networks in 2-dimensional space. The general idea that describes the FNR-Tree is a forest of 1-dimensional (1D) R-Trees on top of a 2-dimensional (2D) R-Tree. The 2D R-Tree is used to index the spatial data of the network (e.g. roads consisting of line segments), while the 1D R-Trees are used to index the time interval of each object’s movement inside a given link of the network. The performance study, comparing this novel access method with the traditional R-Tree under various datasets and queries, shows that the FNR-Tree outperforms the R-Tree in most cases.

On multi-column foreign key discovery

Proceedings of the VLDB Endowment31805-81