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Evaluating Query Languages and Systems for High-Energy Physics Data

Summary: Six general-purpose data platforms evaluated on an HEP benchmark show wide variance in expressing HEP queries; SQL-like languages are often awkward for HEP patterns. Most platforms are 10–100× slower than the domain-specific HEP system, signaling substantial work to achieve relevance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12659
Venue
VLDB
Year
2022
Pagerank
4.3008468e-05
Overall Rank
9,702 | 32.51%
DOI
10.14778/3489496.3489498

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,760 Adaptive data transformations for QaaS 2025 CIDR 4.2856106e-05
9,901 AnyBlox: A Framework for Self-Decoding Datasets 2025 VLDB 4.258022e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
44 The Design Of Postgres 1986 SIGMOD 0.00071838587
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
109 Dremel: Interactive Analysis of Web-Scale Datasets 2010 VLDB 0.00048186983
156 Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases 2017 SIGMOD 0.00040504295
167 The Snowflake Elastic Data Warehouse 2016 SIGMOD 0.00039180521
1,438 AsterixDB: A Scalable, Open Source BDMS 2014 VLDB 0.00011973592
3,548 Adaptive Query Processing on RAW Data 2014 VLDB 6.9859242e-05
4,016 Critical Database Technologies for High Energy Physics 1997 VLDB 6.5270853e-05
8,271 Rumble: Data Independence for Large Messy Data Sets 2021 VLDB 4.5453618e-05
8,863 Petabyte Databases 1999 SIGMOD 4.4331117e-05
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