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Filtering by: Alternative title ssim Report no. 1794 Contributor Termehchy, Arash Degree Field Computer Science

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:

exploratory workloads. They may support only for a restricted types of joins. In this report, we outline a
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:

which a director has worked and other entities related to the movies until there are no more values to
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:

will send only the learned tuple to the join operator until there is no hope of producing any joint