Pattern analysis is responsible for a significant role in data extraction as we enter the era of big data, providing valuable information. Regular patterns, which are temporally consistent patterns in transactional data, offer significant and intelligent insights in various areas. Temporal regularity in a regular pattern allows analyzing and recognizing noteworthy knowledge that appear recurrently from sensor data of the Internet of Things (IoT), such as medical sensors. Regular pattern analysis has been studied to find regular patterns using the temporal regularity of pattern occurrences in huge amounts of data. Recently, the development of tree-based regular pattern analysis algorithms has progressed. However, this tree-based data structure takes long time and require huge memory. For this reason, we suggest a novel list-based data structure and present an algorithm using the proposed structure to discover regular patterns on transactional data efficiently. Our algorithm extracts the exact regular pattern results while spending less time and memory than the existing methods because the proposed structure stores information from the data in minimal structure expression. In performance evaluation, we compare our method to existing methods. These performance tests demonstrate our algorithm outperforms other methods in runtime, memory efficiency, and scalability. Moreover, the accuracy test shows that our method discovers regular patterns accurately. The significance test demonstrates the advantage of regularity, which the proposed algorithm considers. Furthermore, the implications of the proposed algorithm are discussed with concrete applications. In addition, we discuss methods for handle huge amounts of data in the real world.

中文翻译：

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通过预修剪技术实现规律驱动的模式提取和分析方法，无模式损失


随着我们进入大数据时代，模式分析在数据提取中发挥着重要作用，提供有价值的信息。规则模式是事务数据中时间一致的模式，在各个领域提供重要且智能的见解。规则模式中的时间规律允许分析和识别从物联网 （IoT） 的传感器数据中反复出现的值得注意的知识，例如医疗传感器。已经研究了规则模式分析，以使用大量数据中模式出现的时间规律来查找规则模式。最近，基于树的规则模式分析算法的开发取得了进展。但是，这种基于树的数据结构需要很长时间并且需要巨大的内存。出于这个原因，我们提出了一种新颖的基于列表的数据结构，并提出了一种使用所提出的结构来有效地发现交易数据上的规则模式的算法。我们的算法提取精确的规则模式结果，同时比现有方法花费更少的时间和内存，因为提出的结构以最小结构表达式存储数据中的信息。在性能评估中，我们将我们的方法与现有方法进行比较。这些性能测试表明，我们的算法在运行时、内存效率和可扩展性方面优于其他方法。此外，准确率检验表明，我们的方法可以准确地发现规则模式。显著性检验证明了规律性的优势，所提出的算法考虑了这一点。此外，还通过具体应用讨论了所提出的算法的含义。此外，我们还讨论了在现实世界中处理大量数据的方法。