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Filtering by: Creator Shaw, Wesley D. Remove constraint Creator: Shaw, Wesley D. Contributor Termehchy, Arash Remove constraint Contributor: Termehchy, Arash

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  • 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:
    contains. Let D be a countably infinite set of constants. An instance IS of S assigns to each relation

  • 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:
    of different learning lengths in m-run (z = 3) (d) Averaged(4 Queries) Response Time z = 3 Figure

  • 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:
    ▷◁partkey lineitem (b) Q12 : orders ▷◁orderkey lineitem (c) Q10 : customer ▷◁custkey orders (d) Q15

  • 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:
    Dj , respectively, 1 ≤ i ≤ n, and 1 ≤ j ≤ m. Operation ≈d is a similarity operator defined over