Abstract
Link discovery (LD) is the process of identifying relations (links) between entities that originate from different data sources, thereby facilitating several tasks, such as data deduplication, record linkage, and data integration. Existing LD frameworks facilitate data integration tasks over multidimensional data. However, limited work has focused on spatial or spatiotemporal LD, which is typically much more processing-intensive due to the complexity of spatial relations. This chapter targets spatiotemporal link discovery, focusing on topological and proximity relations, proposing a framework with several salient features: support both for streaming and archival data, support of spatial relations in 2D and 3D, flexibility in terms of input consumption, improved filtering techniques, use of blocking techniques, proximity-based LD instead of merely topological LD, and a data-parallel design and implementation. The efficiency of the proposed spatiotemporal LD framework is demonstrated by means of experiments on real-life data from the maritime and aviation domains.
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Acknowledgements
The research work has been supported by the datAcron project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687591 and by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: HFRI-FM17-81).
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Santipantakis, G.M., Doulkeridis, C., Vlachou, A., Vouros, G.A. (2020). Integrating Data by Discovering Topological and Proximity Relations Among Spatiotemporal Entities. In: Vouros, G., et al. Big Data Analytics for Time-Critical Mobility Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-030-45164-6_6
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DOI: https://doi.org/10.1007/978-3-030-45164-6_6
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