Adaptive Geospatial Joins for Modern Hardware

Geospatial joins are a core building block of connected mobility applications. An especially challenging problem are joins between streaming points and static polygons. Since points are not known beforehand, they cannot be indexed. Nevertheless, points need to be mapped to polygons with low latencies to enable real-time feedback. We present an adaptive geospatial join that uses true hit filtering to avoid expensive geometric computations in most cases. Our technique uses a quadtree-based hierarchical grid to approximate polygons and stores these approximations in a specialized radix tree. We emphasize on an approximate version of our algorithm that guarantees a user-defined precision. The exact version of our algorithm can adapt to the expected point distribution by refining the index. We optimized our implementation for modern hardware architectures with wide SIMD vector processing units, including Intel's brand new Knights Landing. Overall, our approach can perform up to two orders of magnitude faster than existing techniques

Adaptive geospatial joins for modern hardware

Geospatial joins are a core building block of connected mobility applications. An especially challenging problem are joins between streaming points and static polygons. Since points are not known beforehand, they cannot be indexed. Nevertheless, points need to be mapped to polygons with low latencies to enable real-time feedback. We present an adaptive geospatial join that uses true hit filtering to avoid expensive geometric computations in most cases. Our technique uses a quadtree-based hierarchical grid to approximate polygons and stores these approximations in a specialized radix tree. We emphasize on an approximate version of our algorithm that guarantees a user-defined precision. The exact version of our algorithm can adapt to the expected point distribution by refining the index. We optimized our implementation for modern hardware architectures with wide SIMD vector processing units, including Intel’s brand new Knights Landing. Overall, our approach can perform up to two orders of magnitude faster than existing techniques

Adaptive main-memory indexing for high-performance point-polygon joins

Connected mobility applications rely heavily on geospatial joins that associate point data, such as locations of Uber cars, to static polygonal regions, such as city neighborhoods. These joins typically involve expensive geometric computations, which makes it hard to provide an interactive user experience. In this paper, we propose an adaptive polygon index that leverages true hit fltering to avoid expensive geometric computations in most cases. In particular, our approach closely approximates polygons by combining quadtrees with true hit filtering, and stores these approximations in a query-effcient radix tree. Based on this index, we introduce two geospatial join algorithms: an approximate one that guarantees a user-defined precision, and an exact one that adapts to the expected point distribution. In summary, our technique outperforms existing CPU-based joins by up to two orders of magnitude and is competitive with state-of-the-art GPU implementations

Weiterentwicklung analytischer Datenbanksysteme

This thesis contributes to the state of the art in analytical database systems. First, we identify and explore extensions to better support analytics on event streams. Second, we propose a novel polygon index to enable efficient geospatial data processing in main memory. Third, we contribute a new deep learning approach to cardinality estimation, which is the core problem in cost-based query optimization.Diese Arbeit trägt zum aktuellen Forschungsstand von analytischen Datenbanksystemen bei. Wir identifizieren und explorieren Erweiterungen um Analysen auf Eventströmen besser zu unterstützen. Wir stellen eine neue Indexstruktur für Polygone vor, die eine effiziente Verarbeitung von Geodaten im Hauptspeicher ermöglicht. Zudem präsentieren wir einen neuen Ansatz für Kardinalitätsschätzungen mittels maschinellen Lernens

Improving Search and Discovery of Geospatial Information in Australia and New Zealand using Semantic Web Techniques

This thesis proposes a set of techniques to make it easier for end users of spatial catalogue systems to locate datasets which they can then use for their own purposes. While other methods are used to locate spatial datasets, catalogue systems continue to be a common choice and are actively supported by those with jurisdiction over datasets in both the public and private sectors

Traveler Mobility and Activity Pattern Inference UsingPersonal Smartphone Applications and ArtificialIntelligence Methods

Recent advances in communication technologies have enabled researchers to collect travel data from location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode, purpose, etc.) of people’s travel. This thesis investigates the application of artificial intelligence methods to infer mode of transport, trip purpose and transit itinerary from traveler trajectories gathered by smartphones. Supervised, Random Forest models are used to detect mode, purpose and transit itinerary of trips. Deep learning models, in particular, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are also employed to infer mode of transport and trip purpose. The research also explores the use of Generative Adversarial Networks (GANs), as a semi-supervised learning approach, to classify trip mode. Moreover, we investigate the application of multi-task learning to simultaneously infer mode and purpose. The research uses several different data sources. Trip trajectory data was collected by the MTL Trajet smartphone Travel Survey App, in 2016. Also, other complementary datasets, such as locational data from social media, land-use, General Transit Feed Specification (GTFS), and elevation data are exploited to infer trip information. Mode of transport can be inferred with Random Forest models, ensemble CNN models, and RNN approaches with an accuracy of 87%, 91%, and 86%, respectively. The Random Forest and multi-task RNN models to infer trip purpose achieve an accuracy of 71% and 78%, respectively. Also, the Random Forest transit itinerary inference model can predict used transit itineraries with an accuracy of 81%. While further improvement is required to enhance the performance of the developed artificial intelligence models on smartphone data, the results of the research indicate the capability of smartphone-based travel surveys as a complementary (and potentially replacement) surveying tool to household travel surveys