PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing Animated Objects Using Spatiotemporal Access Methods
IEEE Transactions on Knowledge and Data Engineering
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Moving Objects Databases: Issues and Solutions
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Developments in Spatio-Temporal Query Languages
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
On location models for ubiquitous computing
Personal and Ubiquitous Computing
Efficient indexing of the historical, present, and future positions of moving objects
Proceedings of the 6th international conference on Mobile data management
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The Bdual-Tree: indexing moving objects by space filling curves in the dual space
The VLDB Journal — The International Journal on Very Large Data Bases
ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Generating semantic-based trajectories for indoor moving objects
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
GMOBench: a benchmark for generic moving objects
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A generic data model for moving objects
Geoinformatica
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
Finding traffic-aware fastest paths in spatial networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
A connectivity index for moving objects in an indoor cellular space
Personal and Ubiquitous Computing
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Indoor spaces accommodate large populations of individuals. With appropriate indoor positioning, e.g., Bluetooth and RFID, in place, large amounts of trajectory data result that may serve as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. This scenario calls for the indexing of indoor trajectories. Based on an appropriate notion of indoor trajectory and definitions of pertinent types of queries, the paper proposes two R-tree based structures for indexing object trajectories in symbolic indoor space. The RTR-tree represents a trajectory as a set of line segments in a space spanned by positioning readers and time. The TP2R-tree applies a data transformation that yields a representation of trajectories as points with extension along the time dimension. The paper details the structure, node organization strategies, and query processing algorithms for each index. An empirical performance study suggests that the two indexes are effective, efficient, and robust. The study also elicits the circumstances under which our proposals perform the best.