The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Guidelines for presentation and comparison of indexing techniques
ACM SIGMOD Record
Advanced database indexing
Towards data mining benchmarking: a test bed for performance study of frequent pattern mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating spatiotemporal datasets on the WWW
ACM SIGMOD Record
Overlapping linear quadtrees and spatio-temporal query processing
The Computer Journal
Benchmark Handbook: For Database and Transaction Processing Systems
Benchmark Handbook: For Database and Transaction Processing Systems
Survey of Spatio-Temporal Databases
Geoinformatica
Oporto: A Realistic Scenario Generator for Moving Objects
Geoinformatica
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Benchmarking Database Systems A Systematic Approach
VLDB '83 Proceedings of the 9th International Conference on Very Large Data Bases
Specifications for Efficient Indexing in Spatiotemporal Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Generating Network-Based Moving Objects
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Benchmarking access methods for time-evolving regional data
Data & Knowledge Engineering
Synthetic generation of cellular network positioning data
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Spatio-temporal discretization for sequential pattern mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Efficient MaxCount and threshold operators of moving objects
Geoinformatica
BerlinMOD: a benchmark for moving object databases
The VLDB Journal — The International Journal on Very Large Data Bases
Generating synthetic meta-data for georeferenced video management
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
SemGen: towards a semantic data generator for benchmarking duplicate detectors
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
A stochastic viewpoint on the generation of spatiotemporal datasets
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
MNTG: an extensible web-based traffic generator
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
An experimental analysis of iterated spatial joins in main memory
Proceedings of the VLDB Endowment
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Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model.