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Filtering by: Committee Member Huang, Liang Remove constraint Committee Member: Huang, Liang Advisor Termehchy, Arash Remove constraint Advisor: Termehchy, Arash

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  • Relational Learning over Dirty Data Using Data Constraints

    Creator:
    Lee, Ga Young
    Abstract:
    Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Applying machine learning on dirty databases may lead to inaccurate results. Users have to spend a lot of time and effort repairing data errors...
    Resource Type:
    Masters Thesis
    Full Text:
    my committee members, Professor Liang Huang, Professor Weng- Keen Wong, and Professor Ren Guo

  • Redesigning the Structure and Access Paths of Databases for Effective and Efficient Query Processing

    Creator:
    Ghadakchi, Vahid
    Abstract:
    Many database users are not familiar with formal query languages, the concept of schema, or the exact content of their database. Thus, it is challenging for these users to formulate their information needs over semi-structured and structured databases. To address this problem, researchers have proposed usable query interfaces over which...
    Resource Type:
    Dissertation
    Full Text:
    the help of my committee members, Alan Fern, Prasad Tadepalli, Liang Huang and Yelda Turkan for

  • Usable and Scalable Querying of Scientific Datasets

    Creator:
    McCamish, Benjamin J.
    Abstract:
    Scientists and engineers have to analyze and query multiple large databases. Analysis over databases created by phasor measurement units can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data efficiently, which...
    Resource Type:
    Dissertation
    Full Text:
    -Sanchez, Arash Termehchy, Vahid Ghadakchi, Liang Huang, and Behrouz Touri. Their ideas and dedication to