IEEE Transactions on Knowledge and Data Engineering

Issue 3 • March 2006

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Displaying Results 1 - 17 of 17
  • [Front cover]

    Publication Year: 2006, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2006, Page(s): c2
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  • BORDER: efficient computation of boundary points

    Publication Year: 2006, Page(s):289 - 303
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6040 KB) | HTML iconHTML

    This work addresses the problem of finding boundary points in multidimensional data sets. Boundary points are data points that are located at the margin of densely distributed data such as a cluster. We describe a novel approach called BORDER (a BOundaRy points DEtectoR) to detect such points. BORDER employs the state-of-the-art database technique - the Gorder kNN join and makes use of the special... View full abstract»

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  • Enhancing data analysis with noise removal

    Publication Year: 2006, Page(s):304 - 319
    Cited by:  Papers (47)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3792 KB) | HTML iconHTML

    Removing objects that are noisy is an important goal of data cleaning as noise hinders most types of data analysis. Most existing data cleaning methods focus on removing noise that is the product of low-level data errors that result from an imperfect data collection process, but data objects that are irrelevant or only weakly relevant can also significantly hinder data analysis. Thus, if the goal ... View full abstract»

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  • Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing

    Publication Year: 2006, Page(s):320 - 333
    Cited by:  Papers (50)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1800 KB) | HTML iconHTML

    Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approaches fall into two categories: feature extraction and feature selection. Techniques in the feature extraction category are typically more effectiv... View full abstract»

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  • Learning object models from semistructured Web documents

    Publication Year: 2006, Page(s):334 - 349
    Cited by:  Papers (15)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3320 KB) | HTML iconHTML

    This paper presents an automated approach to learning object models by means of useful object data extracted from data-intensive semistructured Web documents such as product descriptions. Modeling intensive data on the Web involves the following three phrases: first, we identify the object region covering the descriptions of object data when irrelevant contents from the Web documents are excluded.... View full abstract»

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  • Toward efficient multifeature query processing

    Publication Year: 2006, Page(s):350 - 362
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2232 KB) | HTML iconHTML

    In many advanced applications, data are described by multiple high-dimensional features. Moreover, different queries may weight these features differently; some may not even specify all the features. In this paper, we propose our solution to support efficient query processing in these applications. We devise a novel representation that compactly captures f features into two components. The first c... View full abstract»

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  • Optimizing cyclic join view maintenance over distributed data sources

    Publication Year: 2006, Page(s):363 - 376
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2640 KB) | HTML iconHTML

    Materialized views defined over distributed data sources are critical for many applications to ensure efficient access, reliable performance, and high availability. Materialized views need to be maintained upon source updates since stale view extents may not serve well or may even mislead user applications. Thus, view maintenance performance is one of the keys to the success of these applications.... View full abstract»

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  • Maintaining sliding window skylines on data streams

    Publication Year: 2006, Page(s):377 - 391
    Cited by:  Papers (65)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4480 KB) | HTML iconHTML

    The skyline of a multidimensional data set contains the "best" tuples according to any preference function that is monotonic on each dimension. Although skyline computation has received considerable attention in conventional databases, the existing algorithms are inapplicable to stream applications because 1) they assume static data that are stored in the disk (rather than continuously arriving/ex... View full abstract»

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  • An error-resilient and tunable distributed indexing scheme for wireless data broadcast

    Publication Year: 2006, Page(s):392 - 404
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2448 KB) | HTML iconHTML

    Access efficiency and energy conservation are two critical performance concerns in a wireless data broadcast system. We propose in this paper a novel parameterized index called the exponential index that has a linear yet distributed structure for wireless data broadcast. Based on two tuning knobs, index base and chunk size, the exponential index can be tuned to optimize the access latency with the... View full abstract»

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  • Multicampaign assignment problem

    Publication Year: 2006, Page(s):405 - 414
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3040 KB) | HTML iconHTML

    It is crucial to maximize targeting efficiency and customer satisfaction in personalized marketing. State-of-the-art techniques for targeting focus on the optimization of individual campaigns. Our motivation is the belief that the effectiveness of a campaign with respect to a customer is affected by how many precedent campaigns have been recently delivered to the customer. We raise the multiple re... View full abstract»

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  • Combining feature reduction and case selection in building CBR classifiers

    Publication Year: 2006, Page(s):415 - 429
    Cited by:  Papers (41)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (5440 KB) | HTML iconHTML

    CBR systems that are built for the classification problems are called CBR classifiers. This paper presents a novel and fast approach to building efficient and competent CBR classifiers that combines both feature reduction (FR) and case selection (CS). It has three central contributions: 1) it develops a fast rough-set method based on relative attribute dependency among features to compute the appr... View full abstract»

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  • Call for Papers for Special Issue on Knowledge and Data Management and Engineering in Intelligence and Security informatics

    Publication Year: 2006, Page(s): 430
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  • [Advertisement]

    Publication Year: 2006, Page(s): 431
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  • [Advertisement]

    Publication Year: 2006, Page(s): 432
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  • TKDE Information for authors

    Publication Year: 2006, Page(s): c3
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  • [Back cover]

    Publication Year: 2006, Page(s): c4
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Aims & Scope

IEEE Transactions on Knowledge and Data Engineering (TKDE) informs researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area.

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Meet Our Editors

Editor-in-Chief
Xuemin Lin
University of New South Wales

Associate Editor-in-Chief
Lei Chen
Hong Kong University of Science and Technology