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    Fast Approximate Score Computation on Large-Scale Distributed Data for Learning Multinomial Bayesian Networks

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    Author
    Katib, Anas
    Rao, Praveen
    Barnard, Kobus
    Kamhoua, Charles
    Affiliation
    Univ Arizona, Dept Comp Sci
    Issue Date
    2019-06
    Keywords
    Approximate score computation
    bayesian networks
    structure learning
    distributed data
    gossip algorithms
    
    Metadata
    Show full item record
    Publisher
    ASSOC COMPUTING MACHINERY
    Citation
    Anas Katib, Praveen Rao, Kobus Barnard, and Charles Kamhoua. 2019. Fast Approximate Score Computation on Large-Scale Distributed Data for Learning Multinomial Bayesian Networks. ACM Trans. Knowl. Discov. Data 13, 2, Article 14 (March 2019), 40 pages. https://doi.org/10.1145/3301304
    Journal
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
    Rights
    © 2019 Association for Computing Machinery.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    In this article, we focus on the problem of learning a Bayesian network over distributed data stored in a commodity cluster. Specifically, we address the challenge of computing the scoring function over distributed data in an efficient and scalable manner, which is a fundamental task during learning. While exact score computation can be done using the MapReduce-style computation, our goal is to compute approximate scores much faster with probabilistic error bounds and in a scalable manner. We propose a novel approach, which is designed to achieve the following: (a) decentralized score computation using the principle of gossiping; (b) lower resource consumption via a probabilistic approach for maintaining scores using the properties of a Markov chain; and (c) effective distribution of tasks during score computation (on large datasets) by synergistically combining well-known hashing techniques. We conduct theoretical analysis of our approach in terms of convergence speed of the statistics required for score computation, and memory and network bandwidth consumption. We also discuss how our approach is capable of efficiently recomputing scores when new data are available. We conducted a comprehensive evaluation of our approach and compared with the MapReduce-style computation using datasets of different characteristics on a 16-node cluster. When theMapReduce-style computation provided exact statistics for score computation, it was nearly 10 times slower than our approach. Although it ran faster on randomly sampled datasets than on the entire datasets, it performed worse than our approach in terms of accuracy. Our approach achieved high accuracy (below 6% average relative error) in estimating the statistics for approximate score computation on all the tested datasets. In conclusion, it provides a feasible tradeoff between computation time and accuracy for fast approximate score computation on large-scale distributed data.
    ISSN
    1556-4681
    DOI
    10.1145/3301304
    Version
    Final accepted manuscript
    Sponsors
    U.S. Air Force Summer Faculty Fellowship Program; University of Missouri Research Board; National Science FoundationNational Science Foundation (NSF) [1747751]; King Abdullah Scholarship Program (Saudi Arabia)
    ae974a485f413a2113503eed53cd6c53
    10.1145/3301304
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