BerlinMOD: a benchmark for moving object databases

Düntgen, Christian; Behr, Thomas; Güting, Ralf Hartmut

doi:10.1007/s00778-009-0142-5

Cited by 127 publications

(65 citation statements)

References 44 publications

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“…(2) Extending Hadoop with spatiotemporal operators. (3) Demonstrating the utility of the proposed algebra using the BerlinMOD benchmark (Düntgen, Behr, and Güting 2009). …”

Section: Open Accessmentioning

confidence: 99%

“…BerlinMOD (Düntgen, Behr, and Güting 2009) is a benchmark for MOD systems. It is used in many research works.…”

Section: Berlinmod Benchmarkmentioning

confidence: 99%

“…This section applies the proposed algebra to implement the widely accepted BerlinMOD benchmark queries (Düntgen, Behr, and Güting 2009). This benchmark was originally proposed to compare MOD systems.…”

Section: Berlinmod Queriesmentioning

confidence: 99%

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A spatiotemporal algebra in Hadoop for moving objects

Bakli

Sakr

Soliman

2018

Geo-spatial Information Science

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Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.;

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“…(2) Extending Hadoop with spatiotemporal operators. (3) Demonstrating the utility of the proposed algebra using the BerlinMOD benchmark (Düntgen, Behr, and Güting 2009). …”

Section: Open Accessmentioning

confidence: 99%

“…BerlinMOD (Düntgen, Behr, and Güting 2009) is a benchmark for MOD systems. It is used in many research works.…”

Section: Berlinmod Benchmarkmentioning

confidence: 99%

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A spatiotemporal algebra in Hadoop for moving objects

Bakli

Sakr

Soliman

2018

Geo-spatial Information Science

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“…These solutions target short term vehicle movements at a local scale. The intentions of the travelers (where they come from, where they are going, when the next trip will be) may not be accurate [3].…”

Section: Spatio-temporal Data Generationmentioning

confidence: 99%

“…Second, we maintain consistency between the trips of a same vehicle. The benchmark Berlin-MOD [3] contains a realistic data generation algorithm. Nevertheless, it relies on several rules fine-tuned for the benchmark use-case (the traffic of Berlin).…”

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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Moving Objects Databases and Tracking

Güting¹

2018

Encyclopedia of Database Systems

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BerlinMOD: a benchmark for moving object databases

Cited by 127 publications

References 44 publications

A spatiotemporal algebra in Hadoop for moving objects

A spatiotemporal algebra in Hadoop for moving objects

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

Moving Objects Databases and Tracking

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