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Filtering by: Creator Hernandez-Rocha, Cristian Degree Field Computer Science

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
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