Parallel graph-based anomaly detection technique for sequential data
In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier...
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| Format: | Article |
| Language: | English |
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Springer
2022-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915781930847X |
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| author | Ahmed Farag Hatem Abdelkader Rashed Salem |
| author_facet | Ahmed Farag Hatem Abdelkader Rashed Salem |
| author_sort | Ahmed Farag |
| collection | DOAJ |
| description | In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable. |
| format | Article |
| id | doaj-art-6b61b39e8a2f4f0c857aaf8117f1c8c6 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-6b61b39e8a2f4f0c857aaf8117f1c8c62025-08-20T03:52:00ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-01-013411446145410.1016/j.jksuci.2019.09.009Parallel graph-based anomaly detection technique for sequential dataAhmed Farag0Hatem Abdelkader1Rashed Salem2Corresponding author.; Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin Elkom, EgyptDepartment of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin Elkom, EgyptDepartment of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin Elkom, EgyptIn data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.http://www.sciencedirect.com/science/article/pii/S131915781930847XData miningOutlier detectionRegion outliersParallel graph-based algorithm |
| spellingShingle | Ahmed Farag Hatem Abdelkader Rashed Salem Parallel graph-based anomaly detection technique for sequential data Journal of King Saud University: Computer and Information Sciences Data mining Outlier detection Region outliers Parallel graph-based algorithm |
| title | Parallel graph-based anomaly detection technique for sequential data |
| title_full | Parallel graph-based anomaly detection technique for sequential data |
| title_fullStr | Parallel graph-based anomaly detection technique for sequential data |
| title_full_unstemmed | Parallel graph-based anomaly detection technique for sequential data |
| title_short | Parallel graph-based anomaly detection technique for sequential data |
| title_sort | parallel graph based anomaly detection technique for sequential data |
| topic | Data mining Outlier detection Region outliers Parallel graph-based algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S131915781930847X |
| work_keys_str_mv | AT ahmedfarag parallelgraphbasedanomalydetectiontechniqueforsequentialdata AT hatemabdelkader parallelgraphbasedanomalydetectiontechniqueforsequentialdata AT rashedsalem parallelgraphbasedanomalydetectiontechniqueforsequentialdata |