基于序列数据集的集体异常检测方法综述

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xiaodi Huang, Po Yun, Zhongfeng Hu
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引用次数: 0

摘要

序列数据集上的异常检测通常侧重于集体异常的检测,旨在发现由具有特定关系的数据序列组成的异常模式,而不是单个观测。在本次调查中,总结了现有的研究,以与时间序列数据集和空间序列数据集相一致。对于第一类,检测可以细分为基于符号数据集和基于时间序列数据集,包括相似性、概率性和趋势性方法。对于第二类,它可以细分为基于同质数据集的异构数据集,其中包括多数据集融合和联合方法。与最先进的调查论文相比,本文的贡献在于深入分析了不同应用领域中集体异常的各种表现形式及其相应的检测方法和代表性技术。因此,从业者可以获得一些指导,为他们的特定案例选择最合适的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey of Collective Anomaly Detection on Sequence Dataset
Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather than individual observations. In this survey, existing studies are summarized to align with temporal sequence dataset and spatial sequence dataset. For the first category, the detection can be subdivided into symbolic dataset based and time series dataset based, which include similarity, probabilistic, and trend approaches. For the second category, it can be subdivided into homogeneous datasets based heterogeneous datasets based, which include multi-dataset fusion and joint approaches. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of various representations of collective anomaly in different application field and their corresponding detection methods, representative techniques. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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