Cardinality Estimation in Streaming Graph Data Management Systems
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Date
2024-02-23
Authors
Akillioglu, Kerem
Advisor
Özsu, M. Tamer
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Graph processing has become an increasingly popular paradigm for data management
systems. Concurrently, there is a pronounced demand for specialized systems dedicated
to streaming processing that are essential to address the continual flow of data and the
inherent dynamism in streaming data. Yet, the lack of a standardized, general-purpose
query framework specifically for streaming graphs is a notable gap in existing technologies.
This shortfall emphasizes the necessity for a more comprehensive solution for processing
and analyzing streaming graph data efficiently in real time. Enhancing this solution is
crucially dependent on improving the query processing pipeline, especially on cardinality
estimation and query optimization, both of which are key factors in ensuring optimal
system performance.
In this thesis, a novel cardinality estimation technique, called GraphSketch, that
is tailored for streaming graph database management systems (GDBMS) is proposed.
GraphSketch is a sketch-based framework designed to concisely summarize streaming
graphs, enabling both accurate and efficient cardinality estimations. The thesis delves
into the theoretical foundations of GraphSketch, outlining its conceptual design and the
specific methodologies employed in its construction. Additionally, the thesis elaborates
on the suitability of GraphSketch for streaming systems, highlighting its capability for
incremental updates, which are pivotal in maintaining efficiency in the rapidly evolving
environment of streaming data.
Description
Keywords
databases, query optimization, cardinality estimation, streaming systems, graph database management systems