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Filtering by: Creator Vergara, Hector A. Remove constraint Creator: Vergara, Hector A. Contributor advisor sim Termehchy, Arash Remove constraint Contributor advisor sim: Termehchy, Arash Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Representationally Robust and Scalable Learning over Relational Databases

    Creator:
    Picado Leiva, Jose Manuel
    Abstract:
    Learning novel concepts from relational databases is an important problem with applications in several disciplines, such as data management, natural language processing, and bioinformatics. For a learning algorithm to be effective, the input data should be clean and in some desired representation. However, real-world data is usually heterogeneous – the...
    Resource Type:
    Dissertation
    Full Text:
    : Alan Fern, David Maier, Prasad Tadepalli, and Hector Vergara. Their insightful comments helped this

  • Principles of Variational Databases

    Creator:
    Ataei, Parisa S.
    Abstract:
    Data variations are prevalent in real-world applications. For example, software vendors have to handle numerous variations in the business requirements, conventions, and environmental settings of a software product. In database-backed software, the database of each version may have a different schema and content. As another example, data scientists often need...
    Resource Type:
    Masters Thesis
    Full Text:
    , conventions, and en- vironmental settings of a software product. In database-backed software, the database of

  • Collaborative Online Learning For Join Processing

    Creator:
    Xie, Mian
    Abstract:
    Relational binary operators, such as join, are arguably the most costly and frequently used operations in relational data systems. In many join algorithms, the majority of the process time is spent on scanning and attempting to join the parts of the relations that do not satisfy the join condition and...
    Resource Type:
    Capstone Project
    Full Text:
    A PROJECT submitted to Oregon State University in partial fulfillment of the requirements for the

  • Learning to Estimate Multi-Relation Aggregation Functions

    Creator:
    Jadhav, Rashmi Suresh
    Abstract:
    Multi-relation aggregation queries process the join operator before computing the aggregation function. This join is arguably the most costly operation since traditional join algorithms spend majority of their time trying to join the parts of the relations that do not generate any output tuples. This causes slow response times with...
    Resource Type:
    Masters Thesis
    Full Text:
    -Relation Aggregation Functions by Rashmi Suresh Jadhav A THESIS submitted to Oregon State University