Specification Mining over Temporal Data

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers Pub Date : 2023-09-14 DOI:10.3390/computers12090185
Giacomo Bergami, Samuel Appleby, Graham Morgan
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引用次数: 0

Abstract

Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution.
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规范挖掘时态数据
当前规范挖掘算法的时间数据依赖于穷举搜索方法,这在真实的数据设置中是有害的,因为在长时间的观察中记录了大量不同的时间行为。本文提出了一种新的算法,Bolt2,它是在我们之前的算法Bolt的基础上改进的启发式搜索算法。我们的实验表明,所提出的方法不仅在运行时间方面优于穷举搜索方法,而且还保证了捕获整体时间行为的最小描述。这是通过利用支持度量的假设格搜索实现的。我们的新规范挖掘算法也优于我们之前的贡献所取得的结果。
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来源期刊
Computers
Computers COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.40
自引率
3.60%
发文量
153
审稿时长
11 weeks
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