Journal ArticleDOI
Failure prediction based on log files using Random Indexing and Support Vector Machines
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TLDR
This work describes an approach to predict failures based on log files using Random Indexing and Support Vector Machines that is very reliable in predicting both failures and non-failures.About:
This article is published in Journal of Systems and Software.The article was published on 2013-01-01. It has received 108 citations till now. The article focuses on the topics: Random indexing.read more
Citations
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Proceedings ArticleDOI
Task Failure Prediction in Cloud Data Centers Using Deep Learning
TL;DR: A failure prediction algorithm based on multi-layer Bidirectional Long Short Term Memory (Bi-LSTM) to identify task and job failures in the cloud and shows that the algorithm outperforms other state-of-art prediction methods with 93% accuracy and 87% for task failure and job failure respectively.
Journal ArticleDOI
A multivariate classification of open source developers
TL;DR: The main result of this study is the identification of a recurrent pattern of four kinds of contributors with the same characteristics in all the projects analyzed even if the projects are very different in domain, size, language, etc.
Book ChapterDOI
Diagnosing Performance Variations in HPC Applications Using Machine Learning
Ozan Tuncer,Emre Ates,Yijia Zhang,Ata Turk,Jim Brandt,Vitus J. Leung,Manuel Egele,Ayse K. Coskun +7 more
TL;DR: Diagnosing anomalies is often a difficult task given the vast amount of noisy and high-dimensional data being collected via a variety of system monitoring infrastructures.
Journal ArticleDOI
Pair Programming and Software Defects--A Large, Industrial Case Study
E Di Bella,Ilenia Fronza,Nattakarn Phaphoom,Alberto Sillitti,Giancarlo Succi,Jelena Vlasenko +5 more
TL;DR: Investigating the effects of PP on software quality in five different scenarios shows that PP appears to provide a perceivable but small effect on the reduction of defects in these settings.
A method for characterizing energy consumption in Android smartphones
TL;DR: An approach to relate the energy consumption of smartphones with the operational status of the device is investigated, surveying parameters exposed by the operating system using an Android application to expand the information that may help to produce more reliable measurements.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Introduction to Information Retrieval
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Book ChapterDOI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
Journal ArticleDOI
Cross-Validatory Choice and Assessment of Statistical Predictions
TL;DR: In this article, a generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription, and examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.