A Parallel General-Purpose Synthetic Data Generator1

Hoag, Joseph E.; Thompson, Craig W.

doi:10.1007/978-1-4419-0176-7_6

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Benchmarks typically offer tools to generate datasets of varying size to test systems in different situations [29,43,31,44,30,18]. SmartBench's data generation tool 2 uses seed data from a real IoT deployment (our University building) including sensor data and metadata about the building and sensors, which is able to scale up/down to create a synthetic dataset.…”

Section: Data and Query Generatormentioning

confidence: 99%

SmartBench

et al. 2020

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This paper proposes SmartBench, a benchmark focusing on queries resulting from (near) real-time applications and longer-term analysis of IoT data. SmartBench, derived from a deployed smart building monitoring system, is comprised of: 1) An extensible schema that captures the fundamentals of an IoT smart space; 2) A set of representative queries focusing on analytical tasks; and 3) A data generation tool that generates large amounts of synthetic sensor and semantic data based on seed data collected from a real system. We present an evaluation of seven representative database systems and highlight some interesting findings that can be considered when deciding what database technologies to use under different types of IoT query workloads.

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Section: Data and Query Generatormentioning

confidence: 99%

SmartBench

et al. 2020

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A Regular Expression-based DGL for Meaningful Synthetic Data Generation

Cheng

2020

2020 IEEE International Conference on Big Data and Smart Computing (BigComp)

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Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data

Choi

Jung

2020

Sustainability

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Forecasting hydraulic data such as pressure and demand in water distribution system (WDS) is an important task that helps ensure efficient and accurate operations. Despite high-performance data prediction, missing data can still occur, making it difficult to effectively operate WDS. Though the pressure data are directly related to the rules of operation for pumps or valves, few studies have been conducted on pressure data forecasting. This study proposes a new missing and incomplete data control approach based on real pressure data for reliable and efficient WDS operation and maintenance. The proposed approach is: (1) application of source data from high-resolution, real-world pressure data; (2) development of a cross-domain artificial neural network (CDANN), combining the standard artificial neural networks (ANNs) and the cross-domain training approach for missing data control; and (3) analysis of standard data mining according to external factors to improve prediction accuracy. To verify the proposed approach, a real-world network located in South Korea was used, and the forecasting results were evaluated through performance indicators (i.e., overall, special points, and percentage errors). The performance of the CDANN is compared with that of standard ANNs, and CDANN was found to provide better predictions than traditional ANNs.

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A Parallel General-Purpose Synthetic Data Generator1

Abstract: PSDG is a parallel synthetic data generator designed to generate "industrial sized" data sets quickly using cluster computing. PSDG depends on SDDL, a synthetic data description language that provides flexibility in the types of data we can generate.

Cited by 3 publications

References 3 publications

SmartBench

SmartBench

A Regular Expression-based DGL for Meaningful Synthetic Data Generation

Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data

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