On the Generation of Spatiotemporal Datasets

Theodoridis, Yannis; Silva, Jefferson R. O.; Nascimento, Mário A.

doi:10.1007/3-540-48482-5_11

Cited by 210 publications

(112 citation statements)

References 24 publications

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“…This can be joined with visualinteractive means, for instance, Bach et al [14] developed an evolutionary graph generation algorithm. Another data type -geographic data can be generated using spatio-temporal patterns [95,[90][91][92].…”

Section: Data Source: Real Versus Controlled Datamentioning

confidence: 99%

Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

Archambault

Purchase

Hoßfeld

2017

Lecture Notes in Computer Science

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Section: Data Source: Real Versus Controlled Datamentioning

confidence: 99%

Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

Archambault

Purchase

Hoßfeld

2017

Lecture Notes in Computer Science

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“…The GSTD algorithm (Generate Spatio Temporal Data) [11] is one of the first contributions. It generates a set of points or rectangular regions according to a predefined distribution (e.g., Gaussian).…”

Section: Spatio-temporal Data Generationmentioning

confidence: 99%

Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics

Bösche

Sellam

Pirk

et al. 2013

Selected Topics in Performance Evaluation and Benchmarking

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Abstract. Database benchmarking is most valuable if real-life data and workloads are available. However, real-life data (and workloads) are often not publicly available due to IPR constraints or privacy concerns. And even if available, they are often limited regarding scalability and variability of data characteristics. On the other hand, while easily scalable, synthetically generated data often fail to adequately reflect real-life data characteristics. While there are well established synthetic benchmarks and data generators for, e.g., business data (TPC-C, TPC-H), there is no such up-to-date data generator, let alone benchmark, for spatiotemporal and/or moving objects data. In this work, we present a data generator for spatiotemporal data. More specifically, our data generator produces synthetic GPS traces, mimicking the GPS traces that GPS navigation devices generate. To this end, our generator is fed with real-life statistical profiles derived from the user base and uses real-world road network information. Spatial scalability is achieved by choosing statistics from different regions. The data volume can be scaled by tuning the number and length of the generated trajectories. We compare the generated data to real-life data to demonstrate how well the synthetically generated data reflects real-life data characteristics.

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“…We generate experimental data sets using the Generate Spatiotemporal Data (GSTD) tool [8]. The GSTD tool is a data generator used broadly in the performance evaluations on moving objects and is able to generate moving objects with various distributions [3].…”

Section: Experimental Setupsmentioning

confidence: 99%

Partitioned-Tree Nested Loop Join: An Efficient Join for Spatio-Temporal Interval Join

Lee

Han

Kim

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYA predictive spatio-temporal interval join finds all pairs of moving objects satisfying a join condition on future time interval and space. In this paper, we propose a method called PTJoin. PTJoin partitions the inner index into small sub-trees and performs the join process for each subtree to reduce the number of disk page accesses for each window search. Furthermore, to reduce the number of pages accessed by consecutive window searches, we partition the index so that overlapping index pages do not belong to the same partition. Our experiments show that PTJoin reduces the number of page accesses by up to an order of magnitude compared to Interval STJoin [9], which is the state-of-the-art solution, when the buffer size is small.

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On the Generation of Spatiotemporal Datasets

Cited by 210 publications

References 24 publications

Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics

Partitioned-Tree Nested Loop Join: An Efficient Join for Spatio-Temporal Interval Join

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