Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Database benchmarking is most valuable if real-life data and workloads are#N#available.  Hover, real-life data (and workloads) are often not publicly#N#available due to IPR constraints or privacy concerns.  And even if#N#available, they are often limited regarding scalability and variability of#N#data characteristics.  On the other hand, while easily scalable,#N#synthetically generated data often fail to adequately reflect real-life data#N#characteristics.#N##N#While there are ll established synthetic benchmarks and data generators#N#for, e.g., business data (TPC-C, TPC-H), there is no such up-to-date data#N#generator, let alone benchmark, for spatiotemporal and/or moving objects#N#data.#N##N#In this work,  present a data generator for spatiotemporal data. More#N#specifically, our data generator produces synthetic GPS traces, mimicking the#N#GPS traces that GPS navigation devices generate. To this end, our generator#N#is fed with real-life statistical profiles derived from the user base and#N#uses real-world road network information. Spatial scalability is achieved by#N#choosing statistics from different regions. The data volume can be scaled by#N#tuning the number and length of the generates trajectories.  compare the#N#generated data to real-life data to demonstrate how ll the synthetically#N#generated data reflects real-life data characteristics.
['Konrad Bösche', 'Thibault Sellam', 'Holger Pirk', 'Rene Beier', 'Peter Mieth', 'Stefan Manegold']
I am writing a research paper on a idea Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics
[{"from": "human", "value": "I am writing a research paper on a idea Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Database benchmarking is most valuable if real-life data and workloads are#N#available.  Hover, real-life data (and workloads) are often not publicly#N#available due to IPR constraints or privacy concerns.  And even if#N#available, they are often limited regarding scalability and variability of#N#data characteristics.  On the other hand, while easily scalable,#N#synthetically generated data often fail to adequately reflect real-life data#N#characteristics.#N##N#While there are ll established synthetic benchmarks and data generators#N#for, e.g., business data (TPC-C, TPC-H), there is no such up-to-date data#N#generator, let alone benchmark, for spatiotemporal and/or moving objects#N#data.#N##N#In this work,  present a data generator for spatiotemporal data. More#N#specifically, our data generator produces synthetic GPS traces, mimicking the#N#GPS traces that GPS navigation devices generate. To this end, our generator#N#is fed with real-life statistical profiles derived from the user base and#N#uses real-world road network information. Spatial scalability is achieved by#N#choosing statistics from different regions. The data volume can be scaled by#N#tuning the number and length of the generates trajectories.  compare the#N#generated data to real-life data to demonstrate how ll the synthetically#N#generated data reflects real-life data characteristics."}]