Spaten: A spatio-temporal and textual big data generator

Doudali, Thaleia Dimitra; Konstantinou, Ioannis; Koziris, Nectarios

doi:10.1109/bigdata.2017.8258327

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…These methods, as well as OntoIdea [32], were evaluated on several sets of geometries: Achichi et al [33] apply them to discover topological relations between LineStrings, constructed of trajectories from the TomTom [34] dataset. Saveta et al [35] apply these methods to find relations between LineStrings to LineStrings and between LineStrings to Polygons, from the TomTom dataset and Spaten dataset [36], respectively. All datasets included at most 2000 instances.…”

Section: Related Workmentioning

confidence: 99%

Geo-L: Topological Link Discovery for Geospatial Linked Data Made Easy

Zinke-Wehlmann

Kirschenbaum

2021

IJGI

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Geospatial linked data are an emerging domain, with growing interest in research and the industry. There is an increasing number of publicly available geospatial linked data resources, which can also be interlinked and easily integrated with private and industrial linked data on the web. The present paper introduces Geo-L, a system for the discovery of RDF spatial links based on topological relations. Experiments show that the proposed system improves state-of-the-art spatial linking processes in terms of mapping time and accuracy, as well as concerning resources retrieval efficiency and robustness.

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Section: Related Workmentioning

confidence: 99%

Geo-L: Topological Link Discovery for Geospatial Linked Data Made Easy

Zinke-Wehlmann

Kirschenbaum

2021

IJGI

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“…Three of them are Twitter [9] datasets with different sizes of 200k, 2 million, and 20 million. Another dataset, Spaten [4] has about 1.5 million restaurant reviews across the US. It also contains a geo-location with each review.…”

Section: Datasetsmentioning

confidence: 99%

NUMA-Aware Spatio-Textual Similarity Join

Gautam

Ray

Nickerson

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Spatio-textual similarity join is an operation for finding documents, which are both spatially close and textually relevant. Joins in databases are considered to be the most expensive operation; similarly spatiotextual similarity join is a resource intensive operation. Therefore, it is natural to consider approaches to parallelize this operation. Many modern multi-core systems adopt a NUMA-based memory architecture. NUMA systems entail varying memory access latencies across nodes, which may adversely affect overall query latency. Recent work on spatio-textual similarity join have not addressed the effects of non-uniform access latencies in multi-node NUMA systems. In this paper, we propose a NUMA-aware parallel spatiotextual similarity join algorithm NA-STSJ-WS. It exploits topologyaware work-stealing with adaptive data placement. Experimental evaluation demonstrates that NA-STSJ-WS performs significantly better than existing approaches that are not NUMA-aware, and in the best case we observe 82× speedup over the sequential baseline.

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Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging Challenges

Ahmed,

Raihan

2024

Springer Optimization and Its Applications

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Spaten: A spatio-temporal and textual big data generator

Cited by 6 publications

References 9 publications

Geo-L: Topological Link Discovery for Geospatial Linked Data Made Easy

Geo-L: Topological Link Discovery for Geospatial Linked Data Made Easy

NUMA-Aware Spatio-Textual Similarity Join

Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging Challenges

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