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Knowledge and Data Engineering, IEEE Transactions on

Issue 4 • Date April 2014

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Displaying Results 1 - 19 of 19
  • A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks

    Publication Year: 2014 , Page(s): 780 - 794
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    Request Permissions | Click to expandAbstract | PDF file iconPDF (2135 KB) |  | HTML iconHTML  

    Knowledge discovery from scientific articles has received increasing attention recently since huge repositories are made available by the development of the Internet and digital databases. In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. In ... View full abstract»

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  • Accuracy-Constrained Privacy-Preserving Access Control Mechanism for Relational Data

    Publication Year: 2014 , Page(s): 795 - 807
    Cited by:  Papers (1)
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    Request Permissions | Click to expandAbstract | PDF file iconPDF (4141 KB) |  | HTML iconHTML  

    Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized user can still compromise the privacy of a person leading to identity disclosure. A PPM can use suppression and generalization of relational data to anonymize and satisfy privacy requirements, e.g., k-... View full abstract»

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  • Active Learning without Knowing Individual Instance Labels: A Pairwise Label Homogeneity Query Approach

    Publication Year: 2014 , Page(s): 808 - 822
    Request Permissions | Click to expandAbstract | PDF file iconPDF (3586 KB) |  | HTML iconHTML  

    Traditional active learning methods require the labeler to provide a class label for each queried instance. The labelers are normally highly skilled domain experts to ensure the correctness of the provided labels, which in turn results in expensive labeling cost. To reduce labeling cost, an alternative solution is to allow nonexpert labelers to carry out the labeling task without explicitly tellin... View full abstract»

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  • An Automated Framework for Incorporating News into Stock Trading Strategies

    Publication Year: 2014 , Page(s): 823 - 835
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2491 KB) |  | HTML iconHTML  

    In this paper we present a framework for automatic exploitation of news in stock trading strategies. Events are extracted from news messages presented in free text without annotations. We test the introduced framework by deriving trading strategies based on technical indicators and impacts of the extracted events. The strategies take the form of rules that combine technical trading indicators with... View full abstract»

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  • CoRE: A Context-Aware Relation Extraction Method for Relation Completion

    Publication Year: 2014 , Page(s): 836 - 849
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1569 KB) |  | HTML iconHTML  

    We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation ℜ, RC attempts at linking entity pairs between two entity lists under the relation ℜ. To accomplish the RC goals, we propose to formulate search queries for each query entity α b... View full abstract»

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  • Efficient Ranking on Entity Graphs with Personalized Relationships

    Publication Year: 2014 , Page(s): 850 - 863
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1449 KB) |  | HTML iconHTML  

    Authority flow techniques like PageRank and ObjectRank can provide personalized ranking of typed entity-relationship graphs. There are two main ways to personalize authority flow ranking: Node-based personalization, where authority originates from a set of user-specific nodes; edge-based personalization, where the importance of different edge types is user-specific. We propose the first approach t... View full abstract»

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  • Extended Subtree: A New Similarity Function for Tree Structured Data

    Publication Year: 2014 , Page(s): 864 - 877
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1810 KB) |  | HTML iconHTML  

    Although several distance or similarity functions for trees have been introduced, their performance is not always satisfactory in many applications, ranging from document clustering to natural language processing. This research proposes a new similarity function for trees, namely Extended Subtree (EST), where a new subtree mapping is proposed. EST generalizes the edit base distances by providing n... View full abstract»

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  • Fast Nearest Neighbor Search with Keywords

    Publication Year: 2014 , Page(s): 878 - 888
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1028 KB) |  | HTML iconHTML  

    Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects' geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would in... View full abstract»

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  • Improving Activity Recognition by Segmental Pattern Mining

    Publication Year: 2014 , Page(s): 889 - 902
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2783 KB) |  | HTML iconHTML  

    Activity recognition is a key task for the development of advanced and effective ubiquitous applications in fields like ambient assisted living. A major problem in designing effective recognition algorithms is the difficulty of incorporating long-range dependencies between distant time instants without incurring substantial increase in computational complexity of inference. In this paper we presen... View full abstract»

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  • Infrequent Weighted Itemset Mining Using Frequent Pattern Growth

    Publication Year: 2014 , Page(s): 903 - 915
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2831 KB) |  | HTML iconHTML  

    Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. However, in some contexts, e.g., when the need is to minimize a certain cost function, discovering rare data correlations is more interesting than mining frequent ones. This paper tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted itemset (IWI)... View full abstract»

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  • Local Thresholding in General Network Graphs

    Publication Year: 2014 , Page(s): 916 - 928
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1385 KB) |  | HTML iconHTML  

    Local thresholding algorithms were first presented more than a decade ago and have since been applied to a variety of data mining tasks in peer-to-peer systems, wireless sensor networks, and in grid systems. One critical assumption made by those algorithms has always been cycle-free routing. The existence of even one cycle may lead all peers to the wrong outcome. Outside the lab, unfortunately, cy... View full abstract»

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  • MultiComm: Finding Community Structure in Multi-Dimensional Networks

    Publication Year: 2014 , Page(s): 929 - 941
    Multimedia
    Request Permissions | Click to expandAbstract | PDF file iconPDF (4174 KB) |  | HTML iconHTML  

    The main aim of this paper is to develop a community discovery scheme in a multi-dimensional network for data mining applications. In online social media, networked data consists of multiple dimensions/entities such as users, tags, photos, comments, and stories. We are interested in finding a group of users who interact significantly on these media entities. In a co-citation network, we are intere... View full abstract»

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  • On Skyline Groups

    Publication Year: 2014 , Page(s): 942 - 956
    Request Permissions | Click to expandAbstract | PDF file iconPDF (3784 KB) |  | HTML iconHTML  

    We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple data set-i.e., groups of k tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we first s... View full abstract»

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  • Quasi-SLCA Based Keyword Query Processing over Probabilistic XML Data

    Publication Year: 2014 , Page(s): 957 - 969
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1663 KB) |  | HTML iconHTML  

    The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ)over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTK... View full abstract»

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  • Secure Mining of Association Rules in Horizontally Distributed Databases

    Publication Year: 2014 , Page(s): 970 - 983
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1575 KB) |  | HTML iconHTML  

    We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton . Our protocol, like theirs, is based on the Fast Distributed Mining (FDM)algorithm of Cheung et al. , which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-... View full abstract»

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  • Security Evaluation of Pattern Classifiers under Attack

    Publication Year: 2014 , Page(s): 984 - 996
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1076 KB) |  | HTML iconHTML  

    Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitat... View full abstract»

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  • Shortest Path Computing in Relational DBMSs

    Publication Year: 2014 , Page(s): 997 - 1011
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (3236 KB) |  | HTML iconHTML  

    This paper takes the shortest path discovery to study efficient relational approaches to graph search queries. We first abstract three enhanced relational operators, based on which we introduce an FEM framework to bridge the gap between relational operations and graph operations. We show new features introduced by recent SQL standards, such as window function and merge statement, can improve the p... View full abstract»

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  • Towards Online Shortest Path Computation

    Publication Year: 2014 , Page(s): 1012 - 1025
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2709 KB) |  | HTML iconHTML  

    The online shortest path problem aims at computing the shortest path based on live traffic circumstances. This is very important in modern car navigation systems as it helps drivers to make sensible decisions. To our best knowledge, there is no efficient system/solution that can offer affordable costs at both client and server sides for online shortest path computation. Unfortunately, the conventi... View full abstract»

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  • Versatile Size- l Object Summaries for Relational Keyword Search

    Publication Year: 2014 , Page(s): 1026 - 1038
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2534 KB) |  | HTML iconHTML  

    The Object Summary (OS)is a recently proposed tree structure, which summarizes all data held in a relational database about a data subject. An OS can potentially be very large in size and therefore unfriendly for users who wish to view synoptic information about the data subject. In this paper, we investigate the effective and efficient retrieval of concise and informative OS snippets (denoted as ... View full abstract»

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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