Hierarchical representations of collections of small rectangles
ACM Computing Surveys (CSUR)
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient computation of temporal aggregates with range predicates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Improving min/max aggregation over spatial objects
Proceedings of the 9th ACM international symposium on Advances in geographic information systems
Efficient aggregation over objects with extent
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Algorithms
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
Incremental Computation and Maintenance of Temporal Aggregates
Proceedings of the 17th International Conference on Data Engineering
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
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Indexing Spatio-Temporal Data Warehouses
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Incremental computation and maintenance of temporal aggregates
The VLDB Journal — The International Journal on Very Large Data Bases
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
On computing temporal aggregates with range predicates
ACM Transactions on Database Systems (TODS)
Binary B-trees for virtual memory
SIGFIDET '71 Proceedings of the 1971 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control
Multi-dimensional aggregation for temporal data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
| Hi-index | 0.00 |
Sequenced spatiotemporal aggregation (SSTA) is an important query for many applications of spatiotemporal databases, such as traffic analysis. Conceptually, an SSTA query returns one aggregate value for each individual spatiotemporal granule. While the data is typically recorded at a fine granularity, at query time a coarser granularity is common. This calls for efficient evaluation strategies that are granularity aware. In this paper, we formally define an SSTA operator that includes a data-to-query granularity conversion. Based on a discrete time model and a discrete 1.5 dimensional space model, we generalize the concept of time constant intervals to constant rectangles, which represent maximal rectangles in the spatiotemporal domain over which an aggregation result is constant. We propose an efficient evaluation algorithm for SSTA queries that takes advantage of a coarse query granularity. The algorithm is based on the plane sweep paradigm, and we propose a granularity aware event point schedule, termed gaEPS, and a granularity aware sweep line status, termed gaSLS. These data structures store space and time points from the input relation in a compressed form using a minimal set of counters. In extensive experiments, we show that for coarse query granularities gaEPS significantly outperforms a basic EPS that is based on an extension of previous work, both in terms of memory usage and runtime.