| Autor: |
Kristian Torp, Magnus N. Hansen |
| Přispěvatelé: |
Renz, Matthias, Sarwat, Mohamed, Nascimento, Mario A., Shekhar, Shashi, Xie, Xing |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Torp, K & Hansen, M N 2022, Efficient network-constrained trajectory queries . in M Renz, M Sarwat, M A Nascimento, S Shekhar & X Xie (eds), 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022 ., 92, Association for Computing Machinery, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pp. 1-4, 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022, Seattle, United States, 01/11/2022 . https://doi.org/10.1145/3557915.3561028 |
| Popis: |
The large search companies have very clearly shown that full-text search on very large datasets can be executed efficiently. In this paper, we show how querying spatio-temporal trajectory data can be converted to a full-text search problem. This allows for the reuse of efficient data and index structures from the full-text domain. The core idea is to convert a trajectory into a document consisting of spatial and temporal terms. For example, spatial terms are municipality names, zip codes, or road-network segment numbers. Temporal terms are, for example, morning, weekday, spring, and 2020. Using a dataset consisting of +62 million trajectories (24.9 billion GPS points) we show how to query this dataset efficiently. These queries cover spatial, temporal, and spatio-temporal queries. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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