TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets
The rise of GPS and broadband-speed wireless devices has led to tremendous excitement about a range of applications broadly characterized as "location based services". Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatio-temporal data...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2011
|
| Online Access: | http://hdl.handle.net/1721.1/62803 https://orcid.org/0000-0002-7470-3265 |
| _version_ | 1811090237303029760 |
|---|---|
| author | Cudre-Mauroux, Philippe Wu, Eugene Madden, Samuel R. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Cudre-Mauroux, Philippe Wu, Eugene Madden, Samuel R. |
| author_sort | Cudre-Mauroux, Philippe |
| collection | MIT |
| description | The rise of GPS and broadband-speed wireless devices has led to tremendous excitement about a range of applications broadly characterized as "location based services". Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatio-temporal data sets required to support such services. Proposals in the literature either present new indices without discussing how to cluster data, potentially resulting in many disk seeks for lookups of densely packed objects, or use static quadtrees or other partitioning structures, which become rapidly suboptimal as the data or queries evolve. As a result of these performance limitations, we built TrajStore, a dynamic storage system optimized for efficiently retrieving all data in a particular spatiotemporal region. TrajStore maintains an optimal index on the data and dynamically co-locates and compresses spatially and temporally adjacent segments on disk. By letting the storage layer evolve with the index, the system adapts to incoming queries and data and is able to answer most queries via a very limited number of I/Os, even when the queries target regions containing hundreds or thousands of different trajectories. |
| first_indexed | 2024-09-23T14:39:34Z |
| format | Article |
| id | mit-1721.1/62803 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:39:34Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/628032022-10-01T21:56:37Z TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets Cudre-Mauroux, Philippe Wu, Eugene Madden, Samuel R. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Madden, Samuel R. Cudre-Mauroux, Philippe Wu, Eugene Madden, Samuel R. The rise of GPS and broadband-speed wireless devices has led to tremendous excitement about a range of applications broadly characterized as "location based services". Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatio-temporal data sets required to support such services. Proposals in the literature either present new indices without discussing how to cluster data, potentially resulting in many disk seeks for lookups of densely packed objects, or use static quadtrees or other partitioning structures, which become rapidly suboptimal as the data or queries evolve. As a result of these performance limitations, we built TrajStore, a dynamic storage system optimized for efficiently retrieving all data in a particular spatiotemporal region. TrajStore maintains an optimal index on the data and dynamically co-locates and compresses spatially and temporally adjacent segments on disk. By letting the storage layer evolve with the index, the system adapts to incoming queries and data and is able to answer most queries via a very limited number of I/Os, even when the queries target regions containing hundreds or thousands of different trajectories. National Science Foundation (U.S.) (IIS-0704424) Microsoft Research (Jim Gray Seed Grant) 2011-05-10T18:09:42Z 2011-05-10T18:09:42Z 2010-04 2010-03 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-5446-4 978-1-4244-5445-7 INSPEC Accession Number: 11258782 http://hdl.handle.net/1721.1/62803 Cudre-Mauroux, P., E. Wu, and S. Madden. “TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets.” Data Engineering (ICDE), 2010 IEEE 26th International Conference On. 2010. 109-120. https://orcid.org/0000-0002-7470-3265 en_US http://dx.doi.org/10.1109/ICDE.2010.5447829 IEEE 26th International Conference on Data Engineering (ICDE), 2010 Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute of Electrical and Electronics Engineers MIT web domain |
| spellingShingle | Cudre-Mauroux, Philippe Wu, Eugene Madden, Samuel R. TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets |
| title | TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets |
| title_full | TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets |
| title_fullStr | TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets |
| title_full_unstemmed | TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets |
| title_short | TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets |
| title_sort | trajstore an adaptive storage system for very large trajectory data sets |
| url | http://hdl.handle.net/1721.1/62803 https://orcid.org/0000-0002-7470-3265 |
| work_keys_str_mv | AT cudremaurouxphilippe trajstoreanadaptivestoragesystemforverylargetrajectorydatasets AT wueugene trajstoreanadaptivestoragesystemforverylargetrajectorydatasets AT maddensamuelr trajstoreanadaptivestoragesystemforverylargetrajectorydatasets |