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IEEE Transactions on Knowledge and Data Engineering

Issue 1 • Date Jan. 2010

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Displaying Results 1 - 22 of 22
  • [Front cover]

    Publication Year: 2010, Page(s): c1
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  • [Inside front cover]

    Publication Year: 2010, Page(s): c2
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  • State of the Transactions Editorial

    Publication Year: 2010, Page(s): 1
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  • Anonymous Query Processing in Road Networks

    Publication Year: 2010, Page(s):2 - 15
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2753 KB) | HTML iconHTML

    The increasing availability of location-aware mobile devices has given rise to a flurry of location-based services (LBSs). Due to the nature of spatial queries, an LBS needs the user position in order to process her requests. On the other hand, revealing exact user locations to a (potentially untrusted) LBS may pinpoint their identities and breach their privacy. To address this issue, spatial anon... View full abstract»

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  • Density Conscious Subspace Clustering for High-Dimensional Data

    Publication Year: 2010, Page(s):16 - 30
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2946 KB) | HTML iconHTML

    Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, the identification of dense regions in previous works lacks of considering a critical problem, called... View full abstract»

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  • Development of a Bayesian Framework for Determining Uncertainty in Receiver Operating Characteristic Curve Estimates

    Publication Year: 2010, Page(s):31 - 45
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1789 KB) | HTML iconHTML

    This research uses a Bayesian framework to develop probability densities for the receiver operating characteristic (ROC) curve. The ROC curve is a discrimination metric that may be used to quantify how well a detection system classifies targets and nontargets. The degree of uncertainty in ROC curve formulation is a concern that previous research has not adequately addressed. This research formulat... View full abstract»

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  • Learning with Positive and Unlabeled Examples Using Topic-Sensitive PLSA

    Publication Year: 2010, Page(s):46 - 58
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2671 KB) | HTML iconHTML

    It is often difficult and time-consuming to provide a large amount of positive and negative examples for training a classification system in many applications such as information retrieval. Instead, users often find it easier to indicate just a few positive examples of what he or she likes, and thus, these are the only labeled examples available for the learning system. A large amount of unlabeled... View full abstract»

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  • LIGHT: A Query-Efficient Yet Low-Maintenance Indexing Scheme over DHTs

    Publication Year: 2010, Page(s):59 - 75
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2779 KB) | HTML iconHTML

    DHT is a widely used building block for scalable P2P systems. However, as uniform hashing employed in DHTs destroys data locality, it is not a trivial task to support complex queries (e.g., range queries and k-nearest-neighbor queries) in DHT-based P2P systems. In order to support efficient processing of such complex queries, a popular solution is to build indexes on top of the DHT. Unfortunately,... View full abstract»

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  • MILD: Multiple-Instance Learning via Disambiguation

    Publication Year: 2010, Page(s):76 - 89
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2301 KB) | HTML iconHTML

    In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set are associated with bags rather than instances. A bag is labeled positive if at least one of its instances is positive; otherwise, the bag is labeled negative. Since a positive bag may contain some negative instances in a... View full abstract»

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  • Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach

    Publication Year: 2010, Page(s):90 - 104
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1852 KB) | HTML iconHTML

    Massive radio frequency identification (RFID) data sets are expected to become commonplace in supply chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are... View full abstract»

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  • Object and Combination Shedding Schemes for Adaptive Media Workflow Execution

    Publication Year: 2010, Page(s):105 - 119
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3171 KB) | HTML iconHTML

    Complex media fusion operations can be costly in terms of the time they need to process input objects. If data arrive faster to fusion nodes than the speed with which they can consume the inputs, this will result in some input objects not being processed. In this paper, we develop load shedding mechanisms which take into consideration both data quality and expensive nature of media fusion operator... View full abstract»

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  • The Dynamic Bloom Filters

    Publication Year: 2010, Page(s):120 - 133
    Cited by:  Papers (34)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2173 KB) | HTML iconHTML

    A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic... View full abstract»

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  • Aging Bloom Filter with Two Active Buffers for Dynamic Sets

    Publication Year: 2010, Page(s):134 - 138
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1514 KB) | HTML iconHTML

    A Bloom filter is a simple but powerful data structure that can check membership to a static set. As Bloom filters become more popular for network applications, a membership query for a dynamic set is also required. Some network applications require high-speed processing of packets. For this purpose, Bloom filters should reside in a fast and small memory, SRAM. In this case, due to the limited mem... View full abstract»

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  • Bayesian Classifiers Programmed in SQL

    Publication Year: 2010, Page(s):139 - 144
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (807 KB) | HTML iconHTML

    The Bayesian classifier is a fundamental classification technique. In this work, we focus on programming Bayesian classifiers in SQL. We introduce two classifiers: naive Bayes and a classifier based on class decomposition using K-means clustering. We consider two complementary tasks: model computation and scoring a data set. We study several layouts for tables and several indexing alternatives. We... View full abstract»

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  • Deterministic Column-Based Matrix Decomposition

    Publication Year: 2010, Page(s):145 - 149
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (733 KB) | HTML iconHTML

    In this paper, we propose a deterministic column-based matrix decomposition method. Conventional column-based matrix decomposition (CX) computes the columns by randomly sampling columns of the data matrix. Instead, the newly proposed method (termed as CX_D) selects columns in a deterministic manner, which well approximates singular value decomposition. The experimental results well demonstrate the... View full abstract»

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  • 2009 Reviewers List

    Publication Year: 2010, Page(s):150 - 156
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  • Call for Papers for New IEEE Transactions on Affective Computing

    Publication Year: 2010, Page(s): 157
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  • 7 Great Reasons for Joining the IEEE Computer Society [advertisement]

    Publication Year: 2010, Page(s): 158
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  • IEEE Computer Society Computing Now [advertisement]

    Publication Year: 2010, Page(s): 159
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  • IEEE and IEEE Computer Society 2010 Student Member Package

    Publication Year: 2010, Page(s): 160
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  • TKDE Information for authors

    Publication Year: 2010, Page(s): c3
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  • [Back cover]

    Publication Year: 2010, 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.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Jian Pei
Simon Fraser University

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

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