Internet services access networked storage many times while processing a request. Just a few slow storage accesses per request can raise response times a lot, making the whole service less usable and hurting profits. This paper presents Zoolander, a key value store that meets strict, low latency service level objectives (SLOs). Zoolander scales out using replication for predictability, an old b...

This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vector domain description (SVDD). This data description can be used for novelty or outlier detection. A spherically shaped decision boundary around a set of objects is constructed by a set of support vectors describing the sphere boundary. It has the possibility of ...

Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The se...

Outlier detection is a substantial research problem in the domain of data mining that aims to uncover objects which exhibit significantly different, exceptional and inconsistent from rest of the data. Outlier detection has been widely researched and finds use within various application domains including tax fraud detection, network robustness analysis, network intrusion and medical diagnosis. I...

— Outlier detection is an important task in many applications; it can lead to the discovery of unexpected, useful or interesting objects in data analysis. Many outlier detection methods are available. However, they are limited by assumptions in distribution or rely on many patterns to detect one outlier. Often, a distribution is not known, or experimental results may not provide enough informat...

Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The se...

Despite computation becomes much complex on data with unprecedented large-scale, we argue computers or smart devices should and will consistently provide information and knowledge to human being in the order of a few tens milliseconds. We coin a new term 10-millisecond computing to call attention to this class of workloads. Public reports indicate that internet service users are sensitive to th...

The U.S. Census Bureau has enhanced the X-12-ARIMA seasonal adjustment program by incorporating an improved automatic regARIMA model (regression model with ARIMA errors) selection procedure. Currently this procedure is available only in test version 0.3 of X-12ARIMA, but it will be released in a future version of the program. It is based on the automatic model selection procedure of TRAMO , an ...

In the context of computer aided mammography, many standard algorithms (e.g. SVM and neural networks) have been used for detecting lesions. However, these general purpose learning methods make implicit assumptions like sample independence that are commonly violated. A new ensemble algorithm is proposed to explicitly account for the small fraction of outlier images which tend to produce a large ...