00428nas a2200133   4500008004100000245007900041210006900120260001200189300001200201490000700213100001900220700001800239856003700257       2018                        eng d00aA Novel Framework for Context-aware Outlier Detection in Big Data Streams-0 aNovel Framework for Contextaware Outlier Detection in Big Data S  c10/2018  a213-2220 v161 aAhmad, Hussien1 aDowaji, Salah  uhttps://dline.info/newl/node/69602165nas a2200145   4500008004100000245007900041210006900120260001200189300001200201490000700213520170600220100001901926700001801945856005601963       2018                        eng d00aA Novel Framework for Context-aware Outlier Detection in Big Data Streams-0 aNovel Framework for Contextaware Outlier Detection in Big Data S  c10/2018  a213-2220 v163 a<p>Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy.</p>
1 aAhmad, Hussien1 aDowaji, Salah  uhttp://dline.info/fpaper/jdim/v16i5/jdimv16i5_1.pdf00375nas a2200133   4500008004100000245004600041210004600087260001200133300001000145490000700155100002400162700001800186856003700204       2018                        eng d00aValue Driven Management of Cloud Services0 aValue Driven Management of Cloud Services  c03/2018  a22-370 v101 aMurad, Salah, Eddin1 aDowaji, Salah  uhttps://dline.info/newl/node/647