The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Evaluation of Access Structures for Discretely Moving Points
STDBM '99 Proceedings of the International Workshop on Spatio-Temporal Database Management
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Smart Identification Frameworks for Ubiquitous Computing Applications
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Efficient storage scheme and query processing for supply chain management using RFID
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Reordering of Location Identifiers for Indexing an RFID Tag Object Database
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Incremental aggregation of RFID data
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
An improved data warehouse model for RFID data in supply chain
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
An approximate duplicate elimination in RFID data streams
Data & Knowledge Engineering
| Hi-index | 0.00 |
For tracing tag locations, the trajectories should be modeled and indexed in a radio frequency identification (RFID) system. The trajectory of a tag is represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as time-parameterized intervals and new index scheme called the Time Parameterized Interval R-tree. We also propose new insert and split algorithms to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with the R-tree and the R*-tree. Our experiments show that the new index scheme outperforms the other two in processing queries of tags on various datasets.