Robust and Scalable Content-and-Structure Indexing

Frequent queries on semi-structured hierarchical data are Content-and-Structure (CAS) queries that filter data items based on their location in the hierarchical structure and their value for some attribute. We propose the Robust and Scalable Content-and-Structure (RSCAS) index to efficiently answer...

Full description

Saved in:
Bibliographic Details
Published inThe VLDB journal
Main Authors Wellenzohn, Kevin, Böhlen, Michael H., Helmer, Sven, Pietri, Antoine, Zacchiroli, Stefano
Format Journal Article
LanguageEnglish
Published Springer 2022
Subjects
Computer Science
Software Engineering
Online AccessGet full text

Cover

More Information
Summary:Frequent queries on semi-structured hierarchical data are Content-and-Structure (CAS) queries that filter data items based on their location in the hierarchical structure and their value for some attribute. We propose the Robust and Scalable Content-and-Structure (RSCAS) index to efficiently answer CAS queries on big semi-structured data. To get an index that is robust against queries with varying selectivities we introduce a novel dynamic interleaving that merges the path and value dimensions of composite keys in a balanced manner. We store interleaved keys in our triebased RSCAS index, which efficiently supports a wide range of CAS queries, including queries with wildcards and descendant axes. We implement RSCAS as a log-structured merge (LSM) tree to scale it to data-intensive applications with a high insertion rate. We illustrate RSCAS's robustness and scalability by indexing data from the Software Heritage (SWH) archive, which is the world's largest, publiclyavailable source code archive.
ISSN:1066-8888
0949-877X