The TSQL2 Temporal Query Language
The TSQL2 Temporal Query Language
A Framework for Generating Network-Based Moving Objects
Geoinformatica
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Aggregate Processing of Planar Points
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Management of Dynamic Location Information in DOMINO
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Cost and Imprecision in Modeling the Position of Moving Objects
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Incremental Computation and Maintenance of Temporal Aggregates
Proceedings of the 17th International Conference on Data Engineering
Coalescing in Temporal Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Plug and Play with Query Algebras: SECONDO-A Generic DBMS Development Environment
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An asymptotically optimal multiversion B-tree
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient Algorithms for Large-Scale Temporal Aggregation
IEEE Transactions on Knowledge and Data Engineering
Indexing Spatio-Temporal Data Warehouses
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Querying about the Past, the Present, and the Future in Spatio-Temporal Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Spatio-Temporal Aggregation Using Sketches
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
Modeling and querying moving objects in networks
The VLDB Journal — The International Journal on Very Large Data Bases
Integrated data management for mobile services in the real world
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
On computing temporal aggregates with range predicates
ACM Transactions on Database Systems (TODS)
Multi-dimensional aggregation for temporal data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Online pattern aggregation over RFID data streams
WAIM'10 Proceedings of the 11th international conference on Web-age information management
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Many applications of spatio-temporal databases require support for sequenced spatio-temporal (SST) aggregation, e. g., when analyzing traffic density in a city. Conceptually, an SST aggregation produces one aggregate value for each point in time and space. This paper is the first to propose a method to efficiently evaluate SST aggregation queries for the COUNT, SUM, and AVG aggregation functions. Based on a discrete time model and a discrete, 1.5 dimensional space model that represents a road network, we generalize the concept of (temporal) constant intervals towards constant rectangles that represent maximal rectangles in the space-time domain over which the aggregation result is constant. We propose a new data structure, termed SST-tree, which extends the Balanced Tree for one-dimensional temporal aggregation towards the support for two-dimensional, spatio-temporal aggregation. The main feature of the Balanced Tree to store constant intervals in a compact way by using two counters is extended towards a compact representation of constant rectangles in the space-time domain. We propose and evaluate two variants of the SST-tree. The SSTT-tree and SSTH-tree use trees and hashmaps to manage spacestamps, respectively. Our experiments show that both solutions outperform a brute force approach in terms of memory and time. The SSTH-tree is more efficient in terms of memory, whereas the SSTT-tree is more efficient in terms of time.