Experience from an operational Map-Reduce cluster reveals that outliers signi cantly prolong job completion. ˆe causes for outliers include run-time contention for processor, memory and other resources, disk failures, varying bandwidth and congestion along network paths and, imbalance in task workload. We present Mantri, a system that monitors tasks and culls outliers using causeand resource-aw...

As said in signal processing, \One person's noise is another person's signal." For many applications , such as the exploration of satellite or medical images, and the monitoring of criminal activities in electronic commerce, identifying exceptions can often lead to the discovery of truly unexpected knowledge. In this paper, we study an intuitive notion of outliers. A key contribution of this pa...

This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We pro...

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...

 In this paper an applied problem, where the response of interest is the number of success in a specific experiment, is considered and by various visions is studied. The effects of outlier values of response on results of a regression analysis are so important to be studied. For this reason, using diagnostic methods, outlier response values are recognized. It is shown that use of arc-sine ...

Several algorithms have been proposed for discovering patterns from trajectories of moving objects, but only a few have concentrated on outlier detection. Existing approaches, in general, discover spatial outliers, and do not provide any further analysis of the patterns. In this paper we introduce semantic spatial and spatio-temporal outliers and propose a new algorithm for trajectory outlier d...

The objective of this research is detection of outliers in multivariate data employing various distance measure, particularly using robust regression diagnosis technique. Several classical outlier identification methods are based on the sample mean and covariance matrix in general. But they do not always yield better result, as they themselves are affected by the outliers. Sometimes one outlier...

In this paper we describe an interactive approach for finding outliers in big sets of records, such as collected by banks, insurance companies, web shops. The key idea behind our approach is the usage of an easy-to-compute and easy-to-interpret outlier score function. This function is used to identify a set of potential outliers. The outliers, organized in clusters, are then presented to a doma...

In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special...