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In recent years, new wildlife tracking and telemetry technologies have become available, leading to substantial growth in the volume of wildlife tracking data. In the future, one can expect an almost exponential increase in collected data as new sensors are integrated into current tracking systems. A crucial limitation for efficient use of telemetry data is a lack of infrastructure to collect, store and efficiently share the information. Large data sets generated by wildlife tracking equipment pose a number of challenges: to cope with this amount of data, a specific data management approach is needed, one designed to deal with data scalability, automatic data acquisition, long-term storage, efficient data retrieval, management of spatial and temporal information, multi-user support and data sharing and dissemination. The state-of-the-art technology to meet these challenges are relational database management systems (DBMSs), with their dedicated spatial extension. DBMSs are efficient, industry-standard tools for storage, fast retrieval and manipulation of large data sets, as well as data dissemination to client programs or Web interfaces. In the future, we expect the development of tools able to deal at the same time with both spatial and temporal dimensions of animal movement data, such as spatiotemporal databases

Dettki, H.; Urbano, F.; Basille, M.; Cagnacci, F. (2014). Wildlife tracking data management: chances come from difficulties. In: Spatial database for GPS wildlife tracking data: a practical guide to creating a data management system with PostgreSQL/PostGIS and R (editor(s) Urbano, F.; Cagnacci, F.): Springer: 1-7. ISBN: 978-3-319-03742-4 handle: http://hdl.handle.net/10449/24216

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