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Filtering by: Creator Letchworth, Christopher A. Remove constraint Creator: Letchworth, Christopher A. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Contributor Termehchy, Arash Remove constraint Contributor: 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:
    databases may lead to inaccurate results. Users have to spend a great deal of time and effort repairing

  • Improving the Effectiveness of Answering Keyword Queries Using Effective Subsets

    Creator:
    Khodadadi, Abtin
    Abstract:
    Most database users do not know formal query languages, such as SQL, and prefer to express their information needs using usable query languages, such as keyword queries. Keyword queries, however, are inherently ambiguous and challenging for the database systems to understand and answer effectively. We propose a novel approach to...
    Resource Type:
    Capstone Project
    Full Text:
    propose a novel approach to improving the effectiveness of answering keyword queries by processing them

  • 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