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A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks

A Sampling-Based Approach to Optimizing Top-k Queries in Sen...

引用

international conference on data engineering

作者： A.S. Silberstein R. Braynard C. Ellis K. Munagala Jun Yang Department of Computer Science Duke University Durham NC USA

Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered sensors. When approximation is acceptable, a model-driven approach to query processing is effective in saving energy by avoiding contacting nodes whose values can be predicted or are unlikely to be in the result set. To optimize queries such as top-k, however, reasoning directly with models of joint probability distributions can be prohibitively expensive. Instead of using models explicitly, we propose to use samples of past sensor readings. Not only are such samples simple to maintain, but they are also computationally efficient to use in query optimization. With these samples, we can formulate the problem of optimizing approximate top-k queries under an energy constraint as a linear program. We demonstrate the power and flexibility of our sampling-based approach by developing a series of topk query planning algorithms with linear programming, which are capable of efficiently producing plans with better performance and novel features. We show that our approach is both theoretically sound and practically effective on simulated and real-world datasets.

关键词： intelligent networks Birds Predictive models Network topology Wireless sensor networks Query processing Constraint optimization data acquisition Gaussian processes Computer science

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Loosely Coupling Java Algorithms and XML Parsers: a Performance-Oriented Study

Loosely Coupling Java Algorithms and XML Parsers: a Performa...

引用

international conference on data engineering Workshops

作者： G. Psaila Facoltà di Ingegneria Università degli Studi di Bergamo Bergamo Italy

The adoption of XML to represent any kind of data and documents, even complex and huge, is becoming a matter of fact. However, interfacing algorithms and applications with XML Parsers requires to adapt algorithms and applications: event-based SAX Parsers need algorithms that react to events generated by the parser. But parsing/loading XML documents provides poor performance (if compared to reading flat files): therefore, several researches are trying to address this problem by improving the parsing phase, e.g., by adopting condensed or binary representations of XML documents. This paper deals with the other side of the coin, i.e., the problem of coupling algorithms with XML Parsers, in a way that does not require to change the active (polling-based) nature of many algorithms and provides acceptable performance during execution; this problem becomes even more important when we consider Java algorithms, that usually are less efficient than C or C++ algorithms. This paper presents a study about the problem of loosely coupling Java algorithms with XML Parsers. The coupling is loose because the algorithm should be unaware of the particular interface provided by parsers. We consider several coupling techniques, and we compare them by analyzing their performance. The evaluation leads us to identify the coupling techniques that perform better, depending on the specific algorithm’s needs and application scenario.

关键词： Java XML Machine learning algorithms Performance analysis Performance evaluation Web services Memory management Virtual machining data mining Machine learning

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Automatic Sales Lead Generation from Web data

Automatic Sales Lead Generation from Web Data

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international conference on data engineering

作者： G. Ramakrishnan S. Joshi S. Negi R. Krishnapuram S. Balakrishnan IBM India Research Laboratory India

Speed to market is critical to companies that are driven by sales in a competitive market. The earlier a potential customer can be approached in the decision making process of a purchase, the higher are the chances of converting that prospect into a customer. Traditional methods to identify sales leads such as company surveys and direct marketing are manual, expensive and not scalable. Over the past decade the World Wide Web has grown into an information-mesh, with most important facts being reported through Web sites. Several news papers, press releases, trade journals, business magazines and other related sources are on-line. These sources could be used to identify prospective buyers automatically. In this paper, we present a system called ETAP (Electronic Trigger Alert Program) that extracts trigger events from Web data that help in identifying prospective buyers. Trigger events are events of corporate relevance and indicative of the propensity of companies to purchase new products associated with these events. Examples of trigger events are change in management, revenue growth and mergers & acquisitions. The unstructured nature of information makes the extraction task of trigger events difficult. We pose the problem of trigger events extraction as a classification problem and develop methods for learning trigger event classifiers using existing classification methods. We present methods to automatically generate the training data required to learn the classifiers. We also propose a method of feature abstraction that uses named entity recognition to solve the problem of data sparsity. We score and rank the trigger events extracted from ETAP for easy browsing. Our experiments show the effectiveness of the method and thus establish the feasibility of automatic sales lead generation using the Web data.

关键词： Marketing and sales data mining Companies Decision making Web sites Consumer electronics Corporate acquisitions Training data Seminars Advertising

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learning Gaussian processes from multiple tasks

Learning Gaussian processes from multiple tasks

引用

ICML 2005: 22nd international conference on Machine learning

作者： Yu, Kai Tresp, Volker Schwaighofer, Anton Information and Communication Corporate Technology Siemens AG Munich Germany Intelligent Data Analysis Group Praunhofer FIRST Berlin

ISBN: (纸本)1595931805

We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.

关键词： learning systems

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learning Predictive Representations from a history

Learning Predictive Representations from a history

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ICML 2005: 22nd international conference on Machine learning

作者： Wiewiora, Eric Computer Science and Engineering University of California San Diego

ISBN: (纸本)1595931805

Predictive State Representations (PSRs) have shown a great deal of promise as an alternative to Markov models. However, learning a PSR from a single stream of data generated from an environment remains a challenge. In this work, we present a formalism of PSRs and the domains they model. This formalization suggests an algorithm for learning PSRs that will (almost surely) converge to a globally optimal model given sufficient training data.

关键词： learning systems

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data-dependencies and learning in artificial systems

Data-dependencies and learning in artificial systems

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Workshop on Approaches and Applications of Inductive Programming, AAIP 2005 - In Conjunction with the 22nd international conference on Machine learning, ICML 2005

作者： Gopalakrishna, Palem Department of Computer Science and Engineering Indian Institute of Technology - Bombay Mumbai India

data-dependencies play an important role in the performance of learning algorithms. In this paper we analyze the concepts of data dependencies in the context of artificial systems. When a problem and its solution are viewed as points in a system configuration, variations in the problem configurations can be used to study the variations in the solution configurations and vice versa. These variations could be used to infer solutions to unknown instances of problems based on the solutions to known instances, thus reducing the problem of learning to that of identifying the relations among problems and their solutions. We use this concept in constructing a formal framework for a learning mechanism based on the relations among data attributes. As part of the framework we provide metrics - quality and quantity - for data samples and establish a knowledge conservation theorem. We explain how these concepts can be used in practice by considering an example problem and discuss the limitations.

关键词： learning algorithms

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Beyond the point cloud: From transductive to semi-supervised learning

Beyond the point cloud: From transductive to semi-supervised...

引用

ICML 2005: 22nd international conference on Machine learning

作者： Sindhwani, Vikas Niyogi, Partha Belkin, Mikhail Department of Computer Science University of Chicago Chicago IL 60637

ISBN: (纸本)1595931805

Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning. A large body of recent literature has focussed on the transductive setting where labels of unlabeled examples are estimated by learning a function denned only over the point cloud data. In a truly semi-supervised setting however, a learning machine has access to labeled and unlabeled examples and must make predictions on data points never encountered before. In this paper, we show how to turn transductive and standard supervised learning algorithms into semi-supervised learners. We construct a family of data-dependent norms on Reproducing Kernel Hilbert Spaces (RKHS). These norms allow us to warp the structure of the RKHS to reflect the underlying geometry of the data. We derive explicit formulas for the corresponding new kernels. Our approach demonstrates state of the art performance on a variety of classification tasks.

关键词： learning systems

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Incomplete-data classification using logistic regression

Incomplete-data classification using logistic regression

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ICML 2005: 22nd international conference on Machine learning

作者： Williams, David Liao, Xuejun Xue, Ya Carin, Lawrence Department of Electrical and Computer Engineering Duke University Durham NC 27708 United States

ISBN: (纸本)1595931805

A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the non-missing data). Conditional density functions are estimated using a Gaussian mixture model (GMM), with parameter estimation performed using both expectation maximization (EM) and Variational Bayesian EM (VB-EM). Using widely available real data, we demonstrate the general advantage of the VB-EM GMM estimation for handling incomplete data, vis-à-vis the EM algorithm. Moreover, it is demonstrated that the approach proposed here is generally superior to standard imputation procedures.

关键词： Regression analysis

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Robust one-class clustering Using hybrid global and local search 05

Robust one-class clustering Using hybrid global and local se...

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ICML 2005: 22nd international conference on Machine learning

作者： Gupta, Gunjan Ghosh, Joydeep Department of Electrical and Computer Engineering University of Texas at Austin Austin TX 78712 United States

ISBN: (纸本)1595931805

Unsupervised learning methods often involve summarizing the data using a small number of parameters. In certain domains, only a small subset of the available data is relevant for the problem. One-Class Classification or One-Class Clustering attempts to find a useful subset by locating a dense region in the data. In particular, a recently proposed algorithm called One-Class Information Ball (OC-IB) shows the advantage of modeling a small set of highly coherent points as opposed to pruning outliers. We present several modifications to OC-IB and integrate it with a global search that results in several improvements such as deterministic results, optimality guarantees, control over cluster size and extension to other cost functions. Empirical studies yield significantly better results on various real and artificial data.

关键词： Robustness (control systems)

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Logistic regression with an auxiliary data source 05

Logistic regression with an auxiliary data source

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ICML 2005: 22nd international conference on Machine learning

作者： Liao, Xuejun Xue, Ya Carin, Lawrence Department of Electrical and Computer Engineering Duke University Durham NC 27708 United States

ISBN: (纸本)1595931805

To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples drawn from two mismatched distributions, where D a are fully labeled and Dp partially labeled, our objective is to complete the labels of Dp. We introduce an auxiliary variable μ for each example in Da to reflect its mismatch with Dp. Under an appropriate constraint the μ's are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in Dp. The proposed algorithm, called "Migratory-Logit" or M-Logit, is demonstrated successfully on simulated as well as real data sets.

关键词： learning systems

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