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Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and(More)
Feather selection is a process that extracts a number of feature subsets which are the most representative of the original meaning from original feature set. It greatly reduces the text processing time and increases the accuracy because of removing some data outliers. With the rapid development of Web 2.0 and the further evolution of the Internet, short(More)
In this work we develop and test a novel hierarchical framework for modeling and learning multivariate clinical time series data. Our framework combines two modeling approaches: Linear Dynamical Systems (LDS) and Gaussian Processes (GP), and is capable to model and work with time series of varied length and with irregularly sampled observations. We test our(More)
—Feature selection is a process which chooses a subset from the original feature set according to some rules. The selected feature retains original physical meaning and provides a better understanding for the data and learning process. However, few modern feature selection approaches take the advantage of features' context information. Based on this(More)
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to(More)
Development of accurate models of complex clinical time series data is critical for understanding the disease, its dynamics , and subsequently patient management and clinical decision making. Clinical time series differ from other time series applications mainly in that observations are often missing and made at irregular time intervals. In this work, we(More)
OBJECTIVE Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for(More)
Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the(More)
Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics (1) What user information is useful for personalization; (2) Importance of(More)
This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent and one cannot decompose the problem into a set of independent classification problems. To address the problem and properly represent label(More)