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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Robust and Scalable Content-and-Structure Indexing (Extended Version) |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | CoRR |
| Geographical Reach | international |
| Volume | abs/2209.05126 |
| Page Range | 1 - 28 |
| Date | 2022 |
| Abstract Text | 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 trie-based 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, publicly-available source code archive. |
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