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  • Efficient Answering of Why-Not Questions in Similar Graph Matching

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    Accepted Manuscript (AM)
    Author(s)
    Islam, Md Saiful
    Liu, Chengfei
    Li, Jianxin
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2015
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    Abstract
    Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph q and a set of missing (why-not) graphs, we aim to modify q into a new query graph q* such that the missing graphs are included in the new answer set of q*. We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our ...
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    Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph q and a set of missing (why-not) graphs, we aim to modify q into a new query graph q* such that the missing graphs are included in the new answer set of q*. We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for q*. Then, we present a two-phase algorithm to find the new query q*. In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query q within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for q*. We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.
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    Journal Title
    IEEE Transactions on Knowledge and Data Engineering
    Volume
    27
    Issue
    10
    DOI
    https://doi.org/10.1109/TKDE.2015.2432798
    Copyright Statement
    © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Information and computing sciences
    Data structures and algorithms
    Publication URI
    http://hdl.handle.net/10072/368827
    Collection
    • Journal articles

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