Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics
- Cite this paper as:
- Bösche K., Sellam T., Pirk H., Beier R., Mieth P., Manegold S. (2013) Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics. In: Nambiar R., Poess M. (eds) Selected Topics in Performance Evaluation and Benchmarking. TPCTC 2012. Lecture Notes in Computer Science, vol 7755. Springer, Berlin, Heidelberg
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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