Document Type
Dissertation
Date of Award
12-31-2022
Degree Name
Doctor of Philosophy in Computing Sciences - (Ph.D.)
Department
Computer Science
First Advisor
Dimitri Theodoratos
Second Advisor
James Geller
Third Advisor
Vincent Oria
Fourth Advisor
Usman W. Roshan
Fifth Advisor
Yi Chen
Abstract
Discovering patterns in graphs by evaluating graph pattern queries involving direct (edge-to-edge mapping) and reachability (edge-to-path mapping) relationships under homomorphisms on data graphs has been extensively studied. Previous studies have aimed to reduce the evaluation time of graph pattern queries due to the potentially numerous matches on large data graphs.
In this work, the concept of the summary graph is developed to improve the evaluation of tree pattern queries and graph pattern queries. The summary graph first filters out candidate matches which violate certain reachability constraints, and then finds local matches of query edges. This reduces redundancy in the representation of the query results and allows for computation sharing during the generation of these results. Methods using materialized graph pattern views are developed to improve the efficiency of graph pattern query evaluation. A view is materialized as a summary graph, which compactly records all the homomorphisms of the view to the data graph. View usability is characterized in terms of query edge coverage to provide necessary and sufficient conditions for answering queries using views, and algorithms are developed for determining view usability and for summary graph construction.
Experimental evaluation shows that the methods using summary graphs and its related concepts outperform previous state-of-the-art approaches. It also demonstrates that the view materialization method outperforms, by several orders of magnitude, a state-of-the-art approach which does not use materialized views, and substantially improves upon its scalability.
Recommended Citation
Lan, Michael, "Using materialized views for answering graph pattern queries" (2022). Dissertations. 1637.
https://digitalcommons.njit.edu/dissertations/1637
