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Fast In-Memory SQL Analytics on Typed Graphs

Summary: GQ-Fast targets typed-graph relationship queries, combining compressed annotated adjacency lists with bottom-up pipelining and compiled C++ execution. Its dense, non-random-access compression avoids row-ID intermediates, delivering up to orders-of-magnitude faster in-memory OLAP. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11713
Venue
VLDB
Year
2017
Pagerank
6.4574091e-05
Overall Rank
4,892 | 66.44%
DOI
10.14778/3021924.3021928

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lin_vldb17,
        title = {{Fast In-Memory SQL Analytics on Typed Graphs}},
        author = {Lin, Chunbin and Mandel, Benjamin and Papakonstantinou, Yannis and Springer, Matthias},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {3},
        pages = {265--276},
        doi = {10.14778/3021924.3021928},
        url = {https://doi.org/10.14778/3021924.3021928},
        year = {2017}
}

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