Document - Compressed indexes for fast search of semantic data

2021

Contribution to conference Open Access

Compressed indexes for fast search of semantic data

Perego R., Pibiri G. E., Venturini R.

Triple indexing RDF Search Efficiency

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. We propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81 times.

Source: ICDE 2021 - 37th IEEE International Conference on Data Engineering, pp. 2325–2326, Online conference, 19-22/04/2021

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@inproceedings{oai:it.cnr:prodotti:446387,
	title = {Compressed indexes for fast search of semantic data},
	author = {Perego R. and Pibiri G. E. and Venturini R.},
	doi = {10.1109/icde51399.2021.00248},
	booktitle = {ICDE 2021 - 37th IEEE International Conference on Data Engineering, pp. 2325–2326, Online conference, 19-22/04/2021},
	year = {2021}
}