Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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BST-IGT Model: Synthetic Benchmark Generation Technique Maintaining Trend of Time Series Data

  • Received : 2020.01.28
  • Accepted : 2020.02.11
  • Published : 2020.02.28
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Abstract

In this paper, we introduce a technique for generating synthetic benchmarks based on time series data. Many of the data measured on IoT devices have a time series characteristic that measures numerical changes over time. However, there is a problem that it is difficult to model the data measured over a long period as generalized time series data. To solve this problem, this paper introduces the BST-IGT model. The BST-IGT model separates the entire data into sections that can be easily time-series modeled, collects the generated data into templates, and produces new synthetic benchmarks that share or modify characteristics based on them. As a result of making a new benchmark using the proposed modeling method, we could create a benchmark with multiple aspects by mixing the composite benchmark with the statistical features of the existing data and other benchmarks.

본 논문에서는 시계열 데이터를 기반으로 합성 벤치마크를 생성하는 기법을 소개한다. IoT 기기에서 측정되는 많은 데이터는 시간에 따른 수치 변화를 측정하는 시계열적 특성이 있다. 하지만 긴 기간 동안 측정되는 데이터를 일반화된 시계열 데이터로 모델링하기 힘든 문제점이 존재한다. 이런 문제를 개선하기 위해 본 논문에서는 BST-IGT 모델을 소개한다. BST-IGT 모델은 전체 데이터를 시계열 모델링이 쉬운 구간으로 분리하여 생성 데이터를 템플릿으로 수집하고 이를 기반으로 특성을 공유하거나 변형되는 새로운 합성 벤치마크를 생성한다. 제안된 모델링 기법을 이용하여 신규 벤치마크를 생성한 결과, 기존 데이터의 통계적 특성을 유지하는 합성 벤치마크와 다른 벤치마크와의 혼합으로 여러 특성을 가지는 벤치마크의 생성을 수행할 수 있었다.

Keywords

References

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