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REVIEW 1 major objections 6 minor 91 references

This paper shows that materialized-view query rewriting cannot be judged stage by stage: the best enumerator or selector flips depending on the other pipeline stages.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-01 12:00 UTC pith:3EAFG6GU

load-bearing objection First full-pipeline MV benchmark; the interaction story is plausible but the headline numbers depend on unverified re-implementations of BigSubs and GnnMV. the 1 major comments →

arxiv 2607.19679 v1 pith:3EAFG6GU submitted 2026-07-22 cs.DB

Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting

Xinjie Hu , Zhengjie Miao This is my paper
classification cs.DB
keywords materialized viewsquery rewritingview selectioncandidate enumerationbenchmarkOLAP workloadscross-engine evaluationpipeline ablation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that materialized-view (MV) based query acceleration is a three-stage pipeline—candidate enumeration, budget-constrained view selection, and optimizer rewriting—and that these stages interact so strongly that single-stage rankings are misleading. Using a modular framework that swaps components independently on four workloads and several engines, it shows that the same enumerator or selector can be a bottleneck or a winner depending on its partners. It also finds that portable SQL rewriting can outperform engine-native optimizer rewriting on complex workloads, while a commercial integrated system's aggressive column pruning wins only when the storage budget is tight relative to database size. The paper identifies a concrete failure mechanism: a cost-only selection utility—creation cost minus scan cost, times query count—systematically under-ranks high-coverage 'heavy tail' views, causing large gaps between selectors.

Core claim

The central claim is that pipeline outcomes depend on how candidate generation, selection under budget, and rewriting interact, so no stage can be evaluated or optimized in isolation. Concretely, on the JOB workload at a 1 GB budget, replacing the join-graph enumerator ECSE with the plan-subtree baseline reduces end-to-end time saving by 43.85 percentage points when the selector and rewriter are fixed, while switching the selector from BigSubs to GnnMV under the weaker enumerator increases saving by 47.53 points; both resulting pipelines are high-performing. The same interaction holds across workloads and engines. The paper attributes it to structural properties: enumerators differ in join-o

What carries the argument

The modular evaluation framework that decomposes the pipeline into enumeration, selection, and rewriting and allows controlled ablations (fix two stages, vary the third). The cross-engine protocol compares an engine's native optimizer-level rewriting against portable SQL rewrites executed on the same engine. The mechanism identified for the selector failure is a cost-only utility model: u = (creation_cost - scan_cost) × count, which penalizes moderate-cost, high-coverage views and explains why a learned utility model can outperform it under tight budgets.

Load-bearing premise

The paper re-implements BigSubs and GnnMV from published descriptions because the original implementations are not public; if either re-implementation deviates from the original in benefit estimation or ILP modeling, the selector rankings it reports may describe the re-implementations rather than the methods themselves.

What would settle it

Re-run the controlled ablations with the original author-provided implementations (or a verified third-party port) of BigSubs and GnnMV; if the 42.4 percentage-point gap on the STATS/Basic/102 MB setting disappears, or BigSubs no longer under-ranks the high-coverage views, the paper's central mechanism is an artifact of re-implementation rather than a property of the methods.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Single-stage benchmarking of MV enumerators or selectors is insufficient: a ranking from one pipeline context does not transfer to another, so published comparisons that fix one context can be globally misleading.
  • The bottleneck stage shifts with workload: enumeration caps possible savings when high-coverage views are missing, selection dominates under tight budgets, and rewriting caps realized savings when selected views are not exploited.
  • Portable SQL rewriting provides a practical cross-engine baseline: on complex workloads it often beats engine-native rewriting by a large margin, so plan-transparent systems can be evaluated fairly against it.
  • Cost-only selection utilities can systematically omit the very views that matter most, so selectors should weight query coverage or use learned cost models when budgets are tight.
  • Commercial integrated MV systems do not dominate modular pipelines everywhere; they win mainly when their column-pruned views fit a tight budget and lose where coverage is the limiting factor.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A cheap heuristic approximating the learned selector—for example, ranking views by coverage times per-query saving rather than creation cost times count—could be tested against the reported 42.4 percentage-point gap; if it closes the gap, expensive training may be unnecessary.
  • The 'Inverse Query Reconstruction' failure mode suggests a static structural checker that flags rewrites introducing UNION ALL with inverse disjunctive predicates could prevent catastrophic regressions; such a checker could be built and validated on the paper's workloads.
  • Because the paper finds that high-coverage views are not necessarily large, one could hypothesize that join-order-exploring enumerators are more drift-resistant than predicate-specialized ones; a direct test would measure drift robustness on workloads where both enumerator types produce identical candidate counts.
  • The paper's evidence implies that future MV systems should co-design enumeration and selection around coverage-aware utility rather than optimize them independently; a system that builds workload-level join graphs and feeds coverage-weighted benefits into a budgeted ILP could capture much of the learned-selector gain without training.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 6 minor

Summary. The paper presents a modular benchmark for materialized-view (MV) based query rewriting, jointly evaluating candidate enumeration, budget-constrained view selection, and query rewriting across multiple open-source and commercial engines. It introduces a cross-engine protocol for plan-transparent systems, a set of stage-wise and end-to-end metrics, and controlled ablations over a 4-workload, 7-engine matrix. The central empirical claim is that pipeline stages interact strongly: single-stage rankings of enumerators or selectors are locally valid but globally misleading, with headline examples on JOB (ECSE→Basic costing 43.85 pp under BigSubs, and BigSubs→GnnMV gaining 47.53 pp under Basic) and a STATS+102 MB case showing a 42.4 pp gap between BigSubs and GnnMV. The paper also reports cross-engine variability, rewrite failure modes, and robustness checks under workload drift, data skew, and memory pressure. The artifact is publicly available.

Significance. If the results hold, the paper makes a valuable contribution: it provides the first end-to-end, cross-engine benchmark of MV-based rewriting across all three pipeline stages, and it demonstrates with controlled ablations that isolated stage evaluation can mislead. The modular framework and cross-engine protocol are reusable, the coverage of industrial rewriters is impressive, and the robustness analyses (workload drift, DSB skew, hardware pressure) add useful evidence. The paper ships code and data, which strengthens reproducibility. However, the headline quantitative claims rest on author re-implementations of BigSubs and GnnMV that are not fidelity-validated, and all reported latency savings are point estimates without variance or confidence intervals. The precise magnitudes of the interaction effects are therefore not yet established, even though the qualitative interaction phenomenon is plausible and well supported by the ablations and case studies.

major comments (1)
  1. [§7.2, §8, §9] GnnMV is trained using utility labels generated by HIV rewriting on PostgreSQL (Section 7.2), and the evaluation that shows GnnMV beating BigSubs is performed on the same HIV/PostgreSQL pipeline. This alignment gives the learned selector direct access to the exact cost model and rewriter behavior used at test time, which may explain part of the observed gap. The paper calls this a 'fair comparison' but it is a controlled comparison only in the sense that all selectors share the same candidate pool and budget; it does not control for train/test pipeline alignment. Please add an ablation where GnnMV (or any learned selector) is trained on a different rewriter/engine and evaluated on HIV/PG, or report the sensitivity of the selector rankings to the training pipeline. Without this, the claim that GnnMV is systematically better than BigSubs is not fully supported.
minor comments (6)
  1. [Figures 3 and 10] The main text and appendix use inconsistent labels: appendix Figure 10 uses 'COM-ii' and 'Hawc' where the main text uses 'Sys-B' and 'Basic' (or 'UniView'). Please harmonize names across all figures and tables.
  2. [§9.3, Figure 4] The text states 'switching from HIV to Sys-A's native rewriter reduces workload time saving from ~40% to ~5%' but Figure 4 does not clearly identify the exact pipeline or view set behind this number. Specify the enumerator–selector configuration and provide the precise values in the text.
  3. [§7.3] The end-to-end metric treats a slower rewritten query as 'no rewrite' (i.e., uses original latency). This is a reasonable choice, but it should be stated more prominently and its effect on the reported savings discussed, since it makes the metric more optimistic than one that counts regressions as negative savings.
  4. [Table 8] For the views ranked by BigSubs (MV 121, 263, 7340), the 'Est. benefit (GnnMV)' column is populated even though these views were not selected by GnnMV. It is unclear whether these are GnnMV's estimated utilities for those views; please clarify the interpretation.
  5. [References] References [34] and [35] are duplicates, as are [46] and [47]. Please deduplicate the bibliography.
  6. [§4.2, §5.2] Sys-B is treated both as an enumerator and as a selector in different parts of the evaluation, but the interface through which its enumerated views are separated from its internal selection is not described. Clarify how the modular framework obtains Sys-B's enumerated candidate set without its selection.

Circularity Check

0 steps flagged

No significant circularity: empirical benchmark with independent measurements; design alignments do not reduce to inputs.

full rationale

This paper is an empirical benchmark/evaluation study, not a derivation chain. Its central claims—stage interactions, bottleneck shifts, modular pipelines beating commercial systems—are summaries of measured workload time savings under controlled ablations. No predicted quantity is defined in terms of a fitted input, and no equation-level reduction (Eq. X = Eq. Y by construction) is present. The paper does not rely on a self-citation chain or an imported uniqueness theorem. The closest potential concerns are (i) using HIV-generated SQL as the portable baseline while HIV is itself an evaluated rewriter, and (ii) GnnMV training labels being produced by the same HIV/PostgreSQL pipeline used in evaluation. Both are comparison-design/fairness considerations, not circular reasoning: the HIV baseline is a reference point for cross-engine comparison, and GnnMV is still evaluated on a held-out test split measuring generalization to unseen queries. The re-implementations of BigSubs and GnnMV are reproducibility/external-validity risks but do not make any reported finding equivalent to its own input. The Section 11 limitation about opaque optimizer decisions likewise concerns interpretation, not circularity. Overall, no circular step is exhibited, so the score is 0.

Axiom & Free-Parameter Ledger

2 free parameters · 6 axioms · 0 invented entities

No fitted mathematical parameters are used; the central claims are empirical. The ledger lists hand-chosen experimental settings and domain assumptions that the headline percentages depend on. No new physical or conceptual entities are introduced; 'Inverse Query Reconstruction' is a descriptive label for an observed rewrite pattern, not a postulated mechanism.

free parameters (2)
  • Storage budget settings = 1 GB primary; 0.1/0.2/0.5/1 GB sweep; 102 MB case study; 8 GB DSB robustness check
    Controlled independent variables rather than fitted constants, but headline percentages (e.g., 43.85 pp, 47.53 pp) are conditioned on the chosen 1 GB budget, and the 102 MB instance in Section 10.3 is a hand-picked extreme used to illustrate the BigSubs/GnnMV gap.
  • Workload train/validation/test split ratio = 3:1:6
    Chosen by hand following prior work [40]; learned-selector rankings depend on this split and on the utility labels generated by HIV-on-PostgreSQL, so it is a hand-set numerical choice that affects the results.
axioms (6)
  • domain assumption Latency measurements are stable on a single AWS instance without repeated runs
    Section 7.2 and the metric definitions report no repetition or variance; the benchmark assumes single-shot latency is a reliable estimator of workload time saving.
  • domain assumption The four workloads (JOB, SCALE, STATS, TPC-DS) are representative of analytical MV workloads
    Section 7.1 justifies them by prior usage; the generality of the bottleneck-shift findings rests on this representativeness.
  • domain assumption pg_total_relation_size is an appropriate storage-cost proxy for materialized views
    Section 7.2 uses pg_total_relation_size to enforce the storage budget; this includes heap plus indexes and is PostgreSQL-specific, so budget-feasibility conclusions may not transfer to engines with different storage accounting.
  • domain assumption Author re-implementations of BigSubs and GnnMV faithfully reproduce the original methods
    Section 5.2 states the originals are not publicly available and the authors implemented them; all selector-ranking conclusions depend on this fidelity.
  • domain assumption Commercial systems Sys-A, Sys-B, and Sys-C are configured comparably and their black-box behavior is representative
    Section 6.2 treats these systems as plan-transparent black boxes; the paper does not disclose configuration knobs, so comparability is assumed.
  • domain assumption Rewrites produced by the evaluated production rewriters are semantically equivalent
    Section 6.2 excludes UniView because it produced non-equivalent rewrites, but for HIV, CAL, and the commercial optimizers equivalence is assumed from production validation rather than independently checked for every query-view pair.

reviewed 2026-08-01 · how reviews work

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Cite this review

Pith. "Pith review of Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting." pith.science (2026). https://pith.science/paper/3EAFG6GU

@misc{pith2026260719679,
  author       = {Pith},
  title        = {Pith review of: Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3EAFG6GU}},
  note         = {Machine review of arXiv:2607.19679}
}
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read the original abstract

Materialized views (MVs) accelerate OLAP and data-warehouse workloads by precomputing reusable subexpressions, but practical MV-based query acceleration is a multi-stage pipeline: candidate enumeration, view selection under storage budgets, and query rewriting inside the optimizer. Existing evaluations typically study only parts of this pipeline and within a single system, leaving end-to-end trade-offs and cross-system behavior unclear. In this paper, we benchmark MV-based query rewriting by jointly evaluating enumeration, selection, and rewriting with a modular evaluation framework and by using controlled ablations. We also introduce a cross-engine protocol allowing us to compare systems that expose only execution plans by contrasting native optimizer-level rewriting with portable SQL rewriting baselines when available. Across representative academic methods and modern open-source and commercial systems, we find strong interaction effects across stages and large variability in MV usage and realized savings. We identify recurring failure modes that explain performance regressions after rewriting. Our results highlight which pipeline stages most often limit performance and provide evidence to guide future MV enumeration, selection, and rewriting designs.

Figures

Figures reproduced from arXiv: 2607.19679 by Xinjie Hu, Zhengjie Miao.

Figure 1
Figure 1. Figure 1: The modular evaluation framework for MV-based query rewriting pipeline. Blocks at the bottom show the details of [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Behavior Comparison of Enumerators. We take 33a, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Workload time Saving (%) of all (Enumerator,Selector,Rewriter) pipeline combinations on PostgreSQL. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cross-engine time saving under two rewrite modes. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Cumulative Original Latency and Time Saving Dis [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Query coverage vs. materialized size for the top 10% [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Per-query runtime impact of each rewriter on [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Rewriting Failure Case of Sys-B time saving, higher than 9.86% from SCALE construction. This sug￾gests that a richer historical workload can expose reusable join structures that benefit a simpler future workload. 9.4.2 Data-distribution skew and hardware resource pressure. The other two robustness checks affect different stages. Under DSB skew, realized time saving and native rewrite success both vary subs… view at source ↗
Figure 10
Figure 10. Figure 10: Time Saving (%) Main Result (a) JOB BigSubs COM-ii GnnMV UniView Recommender 0 20 40 60 Time Saving (%) A: rewriter=HIV & engine=PG BigSubs COM-ii GnnMV UniView Recommender 0 5 10 Time Saving (%) F: rewriter=CAL & engine=PG BigSubs COM-ii GnnMV UniView Recommender 0 10 20 Time Saving (%) B: rewriter=HIV & engine=STR BigSubs COM-ii GnnMV UniView Recommender 0 5 10 15 Time Saving (%) G: rewriter=STR & engin… view at source ↗
Figure 11
Figure 11. Figure 11: MV-driven time saving split by query latency quan [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Queries and views for which HIV and DRS exhibit different rewriting behavior. view structures and summarize the dominant failure patterns into two advantages of HIV (A1 and A2). Drilling down via high-impact views. We group the 2,408 fail￾ures by view and find that a small number of views accounts for a large fraction of failures: the top 5 (out of 53) cover 56% of all failures. We select the most frequen… view at source ↗
Figure 14
Figure 14. Figure 14: Example of view definitions by Sys-B and ECSE [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗

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Reference graph

Works this paper leans on

91 extracted references · 8 canonical work pages

  1. [1]

    Apache Doris

    2025. Apache Doris. https://doris.apache.org/

    2025

  2. [2]

    Apache Hive

    2025. Apache Hive. https://hive.apache.org/

    2025

  3. [3]

    AWS Redshift

    2025. AWS Redshift. https://aws.amazon.com/redshift/

    2025

  4. [4]

    CelerData

    2025. CelerData. https://celerdata.com/

    2025

  5. [5]

    Google Cloud BigQuery

    2025. Google Cloud BigQuery. https://cloud.google.com/bigquery?hl=en

    2025

  6. [6]

    Oracle Database

    2025. Oracle Database. https://www.oracle.com/ca-en/database/

    2025

  7. [7]

    PostgreSQL

    2025. PostgreSQL. https://www.postgresql.org/

    2025

  8. [8]

    Snowflake

    2025. Snowflake. https://www.snowflake.com/en/

    2025

  9. [9]

    StarRocks

    2025. StarRocks. https://www.starrocks.io/

    2025

  10. [10]

    1995.Foundations of databases

    Serge Abiteboul, Richard Hull, and Victor Vianu. 1995.Foundations of databases. Vol. 8. Addison-Wesley Reading

    1995

  11. [11]

    Foto Afrati and Rada Chirkova. 2005. Selecting and using views to compute aggregate queries. InInternational Conference on Database Theory. Springer, 383–397

    2005

  12. [12]

    Foto Afrati, Chen Li, and Prasenjit Mitra. 2002. Answering queries using views with arithmetic comparisons. InProceedings of the Twenty-First ACM SIGMOD- SIGACT-SIGART Symposium on Principles of Database Systems(Madison, Wis- consin)(PODS ’02). Association for Computing Machinery, New York, NY, USA, 209–220. https://doi.org/10.1145/543613.543641

    arXiv 2002

  13. [13]

    Narasayya

    Sanjay Agrawal, Surajit Chaudhuri, and Vivek R. Narasayya. 2000. Automated Selection of Materialized Views and Indexes in SQL Databases. InProceedings of the 26th International Conference on Very Large Data Bases (VLDB ’00). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 496–505

    2000

  14. [14]

    Rafi Ahmed, Randall Bello, Andrew Witkowski, and Praveen Kumar. 2020. Au- tomated generation of materialized views in Oracle.Proc. VLDB Endow.13, 12 (Aug. 2020), 3046–3058. https://doi.org/10.14778/3415478.3415533

    arXiv 2020

  15. [15]

    Kamel Aouiche, Pierre-Emmanuel Jouve, and Jérôme Darmont. 2006. Clustering- based materialized view selection in data warehouses. InAdvances in Databases and Information Systems. Springer Berlin Heidelberg, 81–95

    2006

  16. [16]

    Xavier Baril and Zohra Bellahsène. 2003. Selection of materialized views: a cost-based approach. InProceedings of the 15th International Conference on Advanced Information Systems Engineering(Klagenfurt, Austria)(CAiSE’03). Springer-Verlag, Berlin, Heidelberg, 665–680

    2003

  17. [17]

    Bauer and W

    A. Bauer and W. Lehner. 2003. On solving the view selection problem in dis- tributed data warehouse architectures. In15th International Conference on Scien- tific and Statistical Database Management, 2003.43–51. https://doi.org/10.1109/ SSDM.2003.1214953

    Pith/arXiv arXiv 2003

  18. [18]

    Mior, and Daniel Lemire

    Edmon Begoli, Jesús Camacho-Rodríguez, Julian Hyde, Michael J. Mior, and Daniel Lemire. 2018. Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources. InProceedings of the 2018 International Conference on Management of Data(Houston, TX, USA)(SIGMOD ’18). Association for Computing Machinery, New York, NY, USA...

    arXiv 2018

  19. [19]

    Bello, Karl Dias, Alan Downing, James J

    Randall G. Bello, Karl Dias, Alan Downing, James J. Feenan, James L. Finnerty, William D. Norcott, Harry Sun, Andrew Witkowski, and Mohamed Ziauddin

  20. [20]

    Chandra and Philip M

    Ashok K. Chandra and Philip M. Merlin. 1977. Optimal implementation of conjunctive queries in relational data bases. InProceedings of the Ninth Annual ACM Symposium on Theory of Computing(Boulder, Colorado, USA)(STOC ’77). Association for Computing Machinery, New York, NY, USA, 77–90. https: //doi.org/10.1145/800105.803397

    arXiv 1977

  21. [21]

    Surajit Chaudhuri, Ravi Krishnamurthy, Spyros Potamianos, and Kyuseok Shim

  22. [22]

    Chaves, Erik Buchmann, Fabian Hueske, and Klemens Böhm

    Leonardo Weiss F. Chaves, Erik Buchmann, Fabian Hueske, and Klemens Böhm

  23. [23]

    Sara Cohen, Werner Nutt, and Yehoshua Sagiv. 2006. Rewriting queries with arbitrary aggregation functions using views.ACM Trans. Database Syst.31, 2 (June 2006), 672–715

    2006

  24. [25]

    Transaction Processing Performance Council(TPC). 2025. TPC-DS Vesion 2 and Version 3.http://www.tpc.org/tpcds/(2025)

    2025

  25. [26]

    Roozbeh Derakhshan, Frank Dehne, Othmar Korn, and Bela Stantic. 2006. Simu- lated annealing for materialized view selection in data warehousing environment. InProceedings of the 24th IASTED International Conference on Database and Ap- plications(Innsbruck, Austria)(DBA’06). ACTA Press, USA, 89–94

    2006

  26. [27]

    Roozbeh Derakhshan, Bela Stantic, Othmar Korn, and Frank Dehne. 2008. Parallel Simulated Annealing for Materialized View Selection in Data Warehousing En- vironments. InProceedings of the 8th International Conference on Algorithms and Architectures for Parallel Processing(Agia Napa, Cyprus)(ICA3PP ’08). Springer- Verlag, Berlin, Heidelberg, 121–132. https...

    work page doi:10.1007/978-3-540-69501- 2008

  27. [28]

    Bailu Ding, Surajit Chaudhuri, Johannes Gehrke, and Vivek Narasayya. 2021. DSB: a decision support benchmark for workload-driven and traditional database systems.Proc. VLDB Endow.14, 13 (Sept. 2021), 3376–3388. https://doi.org/10. 14778/3484224.3484234

    arXiv 2021

  28. [29]

    Duschka and Michael R

    Oliver M. Duschka and Michael R. Genesereth. 1997. Query planning in info- master. InProceedings of the 1997 ACM Symposium on Applied Computing(San Jose, California, USA)(SAC ’97). Association for Computing Machinery, New York, NY, USA, 109–111. https://doi.org/10.1145/331697.331719

    arXiv 1997

  29. [30]

    Levy, Dan Suciu, and Khaled Yagoub

    Daniela Florescu, Alon Y. Levy, Dan Suciu, and Khaled Yagoub. 1999. Optimiza- tion of Run-time Management of Data Intensive Web-sites. InProceedings of the 25th International Conference on Very Large Data Bases (VLDB ’99). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 627–638

    1999

  30. [31]

    Georgios Giannikis, Darko Makreshanski, Gustavo Alonso, and Donald Koss- mann. 2014. Shared workload optimization.Proceedings of the VLDB Endowment 7, 6 (2014), 429–440

    2014

  31. [32]

    Jonathan Goldstein and Per-Åke Larson. 2001. Optimizing queries using ma- terialized views: a practical, scalable solution. InProceedings of the 2001 ACM SIGMOD International Conference on Management of Data(Santa Barbara, Cali- fornia, USA)(SIGMOD ’01). Association for Computing Machinery, New York, NY, USA, 331–342. https://doi.org/10.1145/375663.375706

    arXiv 2001

  32. [33]

    Stéphane Grumbach, Maurizio Rafanelli, and Leonardo Tininini. 1999. Querying aggregate data. InProceedings of the Eighteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems(Philadelphia, Pennsylvania, USA) (PODS ’99). Association for Computing Machinery, New York, NY, USA, 174–184. https://doi.org/10.1145/303976.303994

    arXiv 1999

  33. [34]

    Ashish Gupta, Venky Harinarayan, and Dallan Quass. 1995. Aggregate-Query Processing in Data Warehousing Environments. InProceedings of the 21th In- ternational Conference on Very Large Data Bases (VLDB ’95). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 358–369

    1995

  34. [35]

    Ashish Gupta, Venky Harinarayan, and Dallan Quass. 1995. Aggregate-query processing in data warehousing environments. (1995)

    1995

  35. [36]

    Himanshu Gupta. 1997. Selection of views to materialize in a data warehouse. In International Conference on Database Theory. Springer, 98–112

    1997

  36. [37]

    Himanshu Gupta and Inderpal Singh Mumick. 1999. Selection of Views to Materialize Under a Maintenance Cost Constraint. InProceedings of the 7th International Conference on Database Theory (ICDT ’99). Springer-Verlag, Berlin, Heidelberg, 453–470

    1999

  37. [38]

    H Gupta and I S Mumick. 2005. Selection of views to materialize in a data warehouse.IEEE Trans. Knowl. Data Eng.17, 1 (Jan. 2005), 24–43

    2005

  38. [39]

    Alon Y. Halevy. 2001. Answering queries using views: A survey.The VLDB Journal10, 4 (Dec. 2001), 270–294. https://doi.org/10.1007/s007780100054

    doi:10.1007/s007780100054 2001

  39. [40]

    Yue Han, Chengliang Chai, Jiabin Liu, Guoliang Li, Chuangxian Wei, and Chao- qun Zhan. 2023. Dynamic materialized view management using graph neural network. InICDE

    2023

  40. [41]

    Yue Han, Guoliang Li, Haitao Yuan, and Ji Sun. 2021. An Autonomous Ma- terialized View Management System with Deep Reinforcement Learning. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). 2159–2164. https://doi.org/10.1109/ICDE51399.2021.00217

    arXiv 2021

  41. [42]

    Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, and Bin Cui. 2021. Cardinality estimation in DBMS: a comprehensive benchmark evaluation.Proc. VLDB Endow.15, 4 (Dec. 2021), 752–765. https://doi.org/10.14778/3503585.3503586

    arXiv 2021

  42. [43]

    Venky Harinarayan, Anand Rajaraman, and Jeffrey D. Ullman. 1996. Imple- menting data cubes efficiently. InProceedings of the 1996 ACM SIGMOD Inter- national Conference on Management of Data(Montreal, Quebec, Canada)(SIG- MOD ’96). Association for Computing Machinery, New York, NY, USA, 205–216. https://doi.org/10.1145/233269.233333

    arXiv 1996

  43. [44]

    Ching-Tien Ho, Rakesh Agrawal, Nimrod Megiddo, and Ramakrishnan Srikant

  44. [45]

    J. T. Horng, Y. J. Chang, and B. J. Liu. 2003. Applying evolutionary algorithms to materialized view selection in a data warehouse.Soft Computing7, 8 (July 2003), 574–581. https://doi.org/10.1007/s00500-002-0243-1

    work page doi:10.1007/s00500-002-0243-1 2003

  45. [46]

    Alekh Jindal, Konstantinos Karanasos, Sriram Rao, and Hiren Patel. 2018. Select- ing subexpressions to materialize at datacenter scale.Proc. VLDB Endow.11, 7 (March 2018), 800–812. https://doi.org/10.14778/3192965.3192971

    arXiv 2018

  46. [47]

    Alekh Jindal, Konstantinos Karanasos, Sriram Rao, and Hiren Patel. 2018. Se- lecting subexpressions to materialize at datacenter scale.Proceedings VLDB Endowment11, 7 (March 2018), 800–812

    2018

  47. [48]

    Alekh Jindal, Shi Qiao, Hiren Patel, Zhicheng Yin, Jieming Di, Malay Bag, Marc Friedman, Yifung Lin, Konstantinos Karanasos, and Sriram Rao. 2018. Compu- tation Reuse in Analytics Job Service at Microsoft. InProceedings of the 2018 International Conference on Management of Data. ACM

    2018

  48. [49]

    Panos Kalnis, Nikos Mamoulis, and Dimitris Papadias. 2002. View selection using randomized search.Data Knowl. Eng.42, 1 (July 2002), 89–111. https: //doi.org/10.1016/S0169-023X(02)00045-9

    work page doi:10.1016/s0169-023x(02)00045-9 2002

  49. [50]

    Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, and Alfons Kemper. 2019. Learned Cardinalities: Estimating Correlated Joins with Deep Learning. InProceedings of the Conference on Innovative Data Systems Research (CIDR)

    2019

  50. [51]

    Anthony Klug. 1988. On conjunctive queries containing inequalities.J. ACM35, 1 (Jan. 1988), 146–160. https://doi.org/10.1145/42267.42273

    arXiv 1988

  51. [52]

    Kolaitis, David L

    Phokion G. Kolaitis, David L. Martin, and Madhukar N. Thakur. 1998. On the complexity of the containment problem for conjunctive queries with built-in predicates. InProceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems(Seattle, Washington, USA)(PODS ’98). Association for Computing Machinery, New York, NY, USA,...

    arXiv 1998

  52. [53]

    Eric Lambrecht, Subbarao Kambhampati, and Senthil Gnanaprakasam. 1999. Optimizing recursive information gathering plans. InProceedings of the 16th International Joint Conference on Artificial Intelligence - Volume 2(Stockholm, Sweden)(IJCAI’99). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1204–1210

    1999

  53. [54]

    MINSOO LEE and JOACHIM HAMMER. 2001. SPEEDING UP MATERI- ALIZED VIEW SELECTION IN DATA WAREHOUSES USING A RANDOM- IZED ALGORITHM.International Journal of Cooperative Information Sys- tems10, 03 (2001), 327–353. https://doi.org/10.1142/S0218843001000370 arXiv:https://doi.org/10.1142/S0218843001000370

    work page doi:10.1142/s0218843001000370 2001

  54. [55]

    Viktor Leis, Andrey Gubichev, Atanas Mirchev, Peter Boncz, Alfons Kemper, and Thomas Neumann. 2015. How good are query optimizers, really?Proc. VLDB Endow.9, 3 (Nov. 2015), 204–215. https://doi.org/10.14778/2850583.2850594

    arXiv 2015

  55. [56]

    Levy, Alberto O

    Alon Y. Levy, Alberto O. Mendelzon, and Yehoshua Sagiv. 1995. Answering queries using views (extended abstract). InProceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems(San Jose, California, USA)(PODS ’95). Association for Computing Machinery, New York, NY, USA, 95–104. https://doi.org/10.1145/212433.220198

    arXiv 1995

  56. [57]

    Levy, Anand Rajaraman, and Joann J

    Alon Y. Levy, Anand Rajaraman, and Joann J. Ordille. 1996. Querying Heteroge- neous Information Sources Using Source Descriptions. InProceedings of the 22th International Conference on Very Large Data Bases (VLDB ’96). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 251–262

    1996

  57. [58]

    Levy and Yehoshua Sagiv

    Alon Y. Levy and Yehoshua Sagiv. 1993. Queries Independent of Updates. In Proceedings of the 19th International Conference on Very Large Data Bases (VLDB ’93). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 171–181

    1993

  58. [59]

    Runzhong Li, Qilong Li, Haotian Liu, Rui Mao, Qing Li, and Bo Tang. 2025. Athena: An Effective Learning-based Framework for Query Optimizer Perfor- mance Improvement.Proc. ACM Manag. Data3, 3, Article 132 (June 2025), 24 pages. https://doi.org/10.1145/3725395

    work page doi:10.1145/3725395 2025

  59. [60]

    Elmore, and Sanjay Krishnan

    Xi Liang, Aaron J. Elmore, and Sanjay Krishnan. 2019. Opportunistic View Materialization with Deep Reinforcement Learning. arXiv:1903.01363 [cs.DB] https://arxiv.org/abs/1903.01363

    Pith/arXiv arXiv 2019

  60. [61]

    Spyros Ligoudistianos, Dimitri Theodoratos, and Timos Sellis. 1998. Experimental Evaluation of Data Warehouse Configuration Algorithms. InProceedings of the 9th International Workshop on Database and Expert Systems Applications (DEXA ’98). IEEE Computer Society, USA, 218

    1998

  61. [62]

    Imene Mami, Remi Coletta, and Zohra Bellahsene. 2011. Modeling view selection as a constraint satisfaction problem. InProceedings of the 22nd International Conference on Database and Expert Systems Applications - Volume Part II(Toulouse, France)(DEXA’11). Springer-Verlag, Berlin, Heidelberg, 396–410

    2011

  62. [63]

    Sudarshan, and Krithi Ramamritham

    Hoshi Mistry, Prasan Roy, S. Sudarshan, and Krithi Ramamritham. 2001. Materi- alized view selection and maintenance using multi-query optimization.SIGMOD Rec.30, 2 (May 2001), 307–318. https://doi.org/10.1145/376284.375703

    arXiv 2001

  63. [64]

    Chang-Sup Park, Myoung Ho Kim, and Yoon-Joon Lee. 2001. Rewriting OLAP queries using materialized views and dimension hierarchies in data warehouses. InProceedings 17th International Conference on Data Engineering. IEEE, 515–523

    2001

  64. [65]

    Perez and Christopher M

    Luis L. Perez and Christopher M. Jermaine. 2014. History-aware query optimiza- tion with materialized intermediate views. In2014 IEEE 30th International Confer- ence on Data Engineering. 520–531. https://doi.org/10.1109/ICDE.2014.6816678

    arXiv 2014

  65. [66]

    Seshadri, S

    Prasan Roy, S. Seshadri, S. Sudarshan, and Siddhesh Bhobe. 2000. Efficient and extensible algorithms for multi query optimization.SIGMOD Rec.29, 2 (May 2000), 249–260. https://doi.org/10.1145/335191.335419

    arXiv 2000

  66. [67]

    Yehoshua Sagiv and Mihalis Yannakakis. 1980. Equivalences Among Relational Expressions with the Union and Difference Operators.J. ACM27, 4 (Oct. 1980), 633–655. https://doi.org/10.1145/322217.322221

    arXiv 1980

  67. [68]

    Griffiths Selinger, M

    P. Griffiths Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. G. Price. 1979. Access path selection in a relational database management system. InProceedings of the 1979 ACM SIGMOD International Conference on Manage- ment of Data(Boston, Massachusetts)(SIGMOD ’79). Association for Computing Machinery, New York, NY, USA, 23–34. https://doi.o...

    arXiv 1979

  68. [69]

    Dimitri Theodoratos, Spyros Ligoudistianos, and Timos Sellis. 1999. Designing the Global Data Warehouse with SPJ Views. InAdvanced Information Systems Engineering, Matthias Jarke and Andreas Oberweis (Eds.). Springer Berlin Hei- delberg, Berlin, Heidelberg, 180–194

    1999

  69. [70]

    Dimitri Theodoratos, Spyros Ligoudistianos, and Timos Sellis. 2001. View selec- tion for designing the global data warehouse.Data Knowl. Eng.39, 3 (Dec. 2001), 219–240. https://doi.org/10.1016/S0169-023X(01)00041-6

    work page doi:10.1016/s0169-023x(01)00041-6 2001

  70. [71]

    Dimitri Theodoratos and Timos K. Sellis. 1997. Data Warehouse Configuration. In Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB ’97). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 126–135

    1997

  71. [72]

    Dimitri Theodoratos and Timos K. Sellis. 1998. Data Warehouse Schema and Instance Design. InProceedings of the 17th International Conference on Conceptual Modeling (ER ’98). Springer-Verlag, Berlin, Heidelberg, 363–376

    1998

  72. [73]

    Tsatalos, Marvin H

    Odysseas G. Tsatalos, Marvin H. Solomon, and Yannis E. Ioannidis. 1996. The GMAP: a versatile tool for physical data independence.The VLDB Journal5, 2 (April 1996), 101–118. https://doi.org/10.1007/s007780050018

    work page doi:10.1007/s007780050018 1996

  73. [74]

    Hidetoshi Uchiyama, Kanda Runapongsa, and Toby J. Teorey. 1999. A progressive view materialization algorithm. InProceedings of the 2nd ACM International Workshop on Data Warehousing and OLAP(Kansas City, Missouri, USA)(DOLAP ’99). Association for Computing Machinery, New York, NY, USA, 36–41. https: //doi.org/10.1145/319757.319786

    arXiv 1999

  74. [75]

    Satyanarayana R Valluri, Soujanya Vadapalli, and Kamalakar Karlapalem

  75. [76]

    Zhenrong Xu, Pengfei Wang, Guoze Xue, Qitong Yan, Shenghao Gong, Yelan Jiang, Yuren Mao, Yunjun Gao, Shu Shen, Wei Zhang, Dan Luo, and Lu Chen

  76. [77]

    Jian Yang, Kamalakar Karlapalem, and Qing Li. 1997. Algorithms for Materialized View Design in Data Warehousing Environment. InProceedings of the 23rd International Conference on Very Large Data Bases (VLDB ’97). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 136–145

    1997

  77. [78]

    Jian Yang, Kamalakar Karlapalem, and Qing Li. 1997. A Framework for Designing Materialized Views in Data Warehousing Environment. InProceedings of the 17th International Conference on Distributed Computing Systems (ICDCS ’97) (ICDCS ’97). IEEE Computer Society, USA, 458

    1997

  78. [79]

    Wei Ye, Ning Gu, Genxing Yang, and Zhenyu Liu. 2005. Extended derivation cube based view materialization selection in distributed data warehouse. InPro- ceedings of the 6th International Conference on Advances in Web-Age Information Management(Hangzhou, China)(W AIM’05). Springer-Verlag, Berlin, Heidelberg, 245–256. https://doi.org/10.1007/11563952_22

    work page doi:10.1007/11563952_22 2005

  79. [80]

    Jeffrey Xu Yu, Xin Yao, Chi-Hon Choi, and Gang Gou. 2003. Materialized view selection as constrained evolutionary optimization.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)33, 4 (2003), 458–467. https://doi.org/10.1109/TSMCC.2003.818494

    arXiv 2003

  80. [81]

    Chuan Zhang and Jian Yang. 1999. Genetic Algorithm for Materialized View Selection in Data Warehouse Environments. InProceedings of the First Interna- tional Conference on Data Warehousing and Knowledge Discovery (DaWaK ’99). Springer-Verlag, Berlin, Heidelberg, 116–125

    1999

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.