Pith. sign in

REVIEW 3 major objections 6 minor 45 references

OneDB: A Distributed Multi-Metric Data Similarity Search System

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that OneDB is the first general distributed system to support exact similarity search in multi-metric space, delivering range and k-nearest-neighbor queries over text, images, locations, and numeric attributes with…

desk verdict Solid systems idea, but the printed pruning lemma and loss function don't support the exactness and learning claims; needs a correctness fix before it's ready. read the letter →

arxiv 2507.04256 v1 pith:T6UUXCJ6 submitted 2025-07-06 cs.DB

Tang Qian , Yifan Zhu , Lu Chen , Xiangyu Ke , Jingwen Zhao , Tianyi Li , Yunjun Gao , Christian S. Jensen This is my paper · ORCID
classification cs.DB
keywords multi-metricsimilaritysearchdistributedindexingmulti-modaldatak-nearestneighborqueriesrangemetricspacepruningmodalityweightlearningreinforcementtuning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

OneDB sets out to prove that similarity search over multi-modal data, records that mix text, images, locations, and numeric attributes, can be both exact and distributed. The central claim is that each modality can keep its own metric space and distance function, unified by a linear multi-metric distance, and that a dual-layer index can answer exact range and k-nearest-neighbor queries over that distance at scale. It further claims that modality weights need not be supplied by users: a lightweight contrastive learner can recover useful weights from about thirty example query cases, and a reinforcement-learning tuner can adjust system parameters end-to-end. If true, this matters because current systems either handle modalities in isolation, missing cross-modal relevance, or approximate the search, risking missed records in settings like healthcare. The experiments report 2.5 to 5.75 times speedups over single- or multi-metric baselines, recall gains of 12.63 to 30.75 percent over a state-of-the-art vector database, and 15 to 17 percent end-to-end gains from autotuning.

What carries the argument

The central object is the multi-metric distance $\delta_W(q_M, o_M) = \sum_{i=1}^{m} \omega_i \, \delta_i(q_i, o_i)$, defined over $m$ metric spaces with a weight vector $W = (\omega_1, \dots, \omega_m)$ in $[0,1]^m$. This linear weighted sum carries the entire argument: the learned weights express user intent, the global pivot mapping embeds each modality into one dimension for the RR*-tree partition, the local index forest of R-trees, MVP-trees, and inverted indexes supports per-modality pruning, and the exact range and kNN semantics are defined by this distance. The two load-bearing inequalities are the triangle inequality, which justifies global pivot-bound pruning, and a pigeonhole bound that lets a single close modality certify a candidate object for a range query.

What would settle it

Collect human relevance judgments for a set of multi-modal queries and check whether any learned single weight vector ranks the judged-relevant items ahead of judged-irrelevant ones; if even one user's judgments cannot be reproduced by any linear weighting, OneDB's accuracy claims are optimizing a proxy rather than user intent. A concrete two-modality counterexample would make the relevant item far from the query in both metrics, so that no convex combination of distances can rank it first.

Watch

Extended reading notes

Core claim

The authors' discovery is that exact multi-metric similarity search can be made distributed and practical by separating coarse global partitioning from fine-grained per-modality indexing. A global RR*-tree maps each metric space to one dimension through distances to a chosen pivot, so that every multi-modal object becomes a point in an m-dimensional vector space partitioned evenly across workers. Each worker then keeps a local forest of per-modality indexes, and query processing alternates between a global pruning lemma based on the triangle inequality and a local pigeonhole lemma. For k-nearest-neighbor queries, a two-phase strategy finds an upper bound on the k-th neighbor distance and converts the problem into a range query, keeping results exact under the weighted distance. The paper claims this is the first general distributed system for similarity search in multi-metric space, and that the combination of weight learning, dual-layer indexing, and reinforcement-learning tuning is what makes the approach accurate and efficient.

Load-bearing premise

The load-bearing premise is that a user's notion of similarity across modalities can be captured by one fixed set of importance weights applied to per-modality distances, and that ground-truth example results exist to learn those weights; if user intent cannot be expressed as any single weight vector, the learned weights and the exact result set optimize the wrong objective.

Editorial extensions

If this is right

  • Exact multi-modal range and kNN queries become available over arbitrary metric modalities, so one distributed store can query images by L1 distance, locations by L2 distance, and text by edit distance through a single SQL statement.
  • Users can express retrieval intent by example instead of by numeric weights, since less than one hundred seconds of training on roughly thirty query cases reportedly reaches about ninety percent recall.
  • The global/local index split means adding a new modality or changing weights does not require rebuilding the whole index; a new local index is added for the new modality at each worker.
  • The reinforcement-learning tuning module removes the need for an administrator to hand-tune Spark and index parameters, claiming 15 to 17 percent end-to-end latency improvement.
  • Against vector database systems that fuse or re-rank vectors, exact multi-metric search offers high recall without a query-ratio knob that trades accuracy for speed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The linear weighted-sum definition is the key constraint; a natural testable extension would compare user preference satisfaction against nonlinear fusion or per-user weight distributions, since no single weight vector may capture a heterogeneous user population.
  • The pruning lemmas depend only on the triangle inequality in each modality, so the same dual-layer architecture should accept any new metric index that provides distance bounds, suggesting a plug-in index-selection layer could be added without changing query semantics.
  • The weight learner's reliance on ground-truth kNN examples could be replaced by revealed preferences from click or interaction logs, turning the system's own query history into training data.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper presents OneDB, a Spark-based distributed system for exact multi-metric similarity search over multi-modal data. It combines a global RR*-tree built on pivot-mapped coordinates, per-modality local indexes (R-tree, MVP-tree, inverted index), a contrastive weight-learning module, a two-phase multi-metric kNN algorithm, and an RL-based end-to-end parameter tuner. Experiments on Air, Food, Rental, Synthetic, and Synthetic II compare against DIMS-M, DESIRE-D, and Milvus, reporting 2.5-5.75x speedups, 12.63%-30.75% recall improvements over Milvus at comparable efficiency, scalability up to 10M objects, and 15%-17% gains from parameter tuning.

Significance. The system-level contribution is potentially significant: OneDB is a plausible first distributed multi-metric similarity-search system, and the experimental comparison against external baselines (DIMS-M, DESIRE-D, Milvus) is appropriate and welcome. The dual-layer indexing design, the breadth of datasets, and the 10M-object scalability study are strengths. However, the exactness argument is impaired by a concrete pruning error in Lemma VI.1, the contrastive loss in Eq. (1) has the wrong sign direction, and Lemma VI.2 is stated with a condition that is necessary but not sufficient. The weight-learning evaluation only recovers hidden weight vectors and does not validate the learned notion of similarity against human relevance judgments. No code or reproducible artifacts are provided. The central claims are defensible only after the correctness issues are repaired and the experiments re-run.

major comments (3)
  1. [Section VI-B, Lemma VI.1 (used in Section VI-C)] The pruning box is defined with radius r, but the query ball for the weighted distance delta_W has per-metric half-width r/omega_i. Since Definition III.1 allows omega_i in [0,1] and does not require the weights to sum to 1, the inference 'delta_W >= delta_i > r' is false: delta_W can be <= r even when delta_i(q,o) > r. Concretely, take r=1, W=(0.1,0.1), pivot distances delta_1(q1,p1)=delta_2(q2,p2)=0.5, and an object with delta_1(o1,p1)=delta_2(o2,p2)=1.7 and delta_1(q1,o1)=delta_2(q2,o2)=1.2 (valid on a line with p between q and o). Then R_q=[-0.5,1.5] does not intersect the entry MBR [1.7,1.7], so Lemma VI.1 prunes the entry, yet delta_W=0.24<1. This produces false negatives in exact MMRQ and, through the range-query conversion, breaks the MMkNN correctness guarantee. The lemma should use r/omega_i for every metric with omega_i>0, and no pruning should be applied on metrics with omega_i=0.
  2. [Section V-B, Eq. (1)] The contrastive loss is L = -log( sum e^{+delta(q,o+)} / (sum e^{+delta(q,o+)} + sum e^{+delta(q,o-)}) ). Minimizing this expression drives e^{+delta(q,o+)} upward, i.e., it maximizes the distance from the query to the positive examples and minimizes the distance to the negative examples, the opposite of the stated goal. The sentence in Section V.B saying that the model should 'increase the similarity distances of positive samples' also reverses the direction: a larger delta means lower similarity. If the implementation actually used e^{-delta}, then Eq. (1) misstates the method and the reported 90% recall cannot be verified from the paper; if the implementation used e^{+delta}, the learned weights are optimized to push positives away. Either way, the weight-learning claim in Section VIII-E needs to be re-derived and the experiments re-run with the corrected loss.
  3. [Section VI-B, Lemma VI.2] The lemma states that an object is included in the MMRQ result if for some i with omega_i>0 we have delta_i(q,o) <= r / sum_j omega_j. This condition is not sufficient: even if one per-modality distance is small, the other modalities can contribute enough to make delta_W exceed r. The condition is, however, necessary: if delta_i(q,o) > r/sum_j omega_j for every i, then delta_W > r, so an object in the result must satisfy the condition for at least one i. If Lemma VI.2 is used only to generate candidates that are later checked by exact verification, the statement should be revised to 'not prunable' rather than 'included'; if it is used as an inclusion test, it causes false positives. The proof is currently omitted, so the correctness argument for the local candidate loop is incomplete.
minor comments (6)
  1. [Abstract and Section I] 'Spart SQL' should be 'Spark SQL'.
  2. [Section VI-B, Lemma VI.1 proof] In case (2) of the proof, the displayed triangle-inequality step says delta_i(oi,qi) >= delta_i(qi,pi)-delta_i(oi,pi)>r, but from Rq.max < MBR.min one obtains delta_i(oi,pi)-delta_i(qi,pi)>r; the algebra should be corrected.
  3. [Figures 5-9] In the supplied version, many axis labels, legends, and even caption text appear as escape sequences such as '/s32/s68 ...', making the quantitative plots unreadable; the figures should be regenerated with proper labels and legends.
  4. [Algorithm 1, line 4] The notation delta_i(p_i^i, o_i) should be delta_i(p_i, o_i); the superscript is inconsistent with the rest of the paper.
  5. [Section VII-B, Eqs. (2)-(5)] The notation alternates between Delta Q_t^0 / Delta Q_t^{t-1} and Delta Q_{t->0} / Delta Q_{t->t-1}, and Eq. (3) contains 'esign' where an exponent appears intended; please unify the notation and fix the typography.
  6. [Section VIII-E] The weight-learning experiments hide known weight vectors and measure recall against those synthetic ground truths; the paper should state explicitly that this does not validate the learned similarity against human relevance judgments, and that the abstract's accuracy improvements are relative to the fixed linear delta_W objective.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the system's main performance claims are benchmarked against external baselines, and the weight-learning and RL-tuning evaluations are experimental recovery/optimization studies rather than by-construction derivations.

full rationale

OneDB's central claims — distributed exact multi-metric range and kNN search, speedups versus DIMS-M and DESIRE-D, and recall/efficiency trade-offs versus Milvus — are evaluated on three real and two synthetic datasets against external systems, so they are not derived from the paper's own inputs by construction. The global RR*-tree and local index forest are described algorithmically and benchmarked independently. The weight-learning module is tested by hiding a known weight vector, generating ground-truth kNNs under that vector, and checking whether the contrastive loss recovers those results; this is an inversion/recovery experiment, not a tautology. The RL-tuning module optimizes measured query latency and reports the resulting improvement, which is an optimization outcome rather than a predicted consequence of the model equations. The paper does cite prior work by overlapping authors, notably the DESIRE paper for the pigeonhole argument behind local candidate pruning in Lemma VI.2 and DIMS for a baseline; these citations point to concrete published algorithms and do not smuggle in an unverified uniqueness result or ansatz. Two non-circularity concerns remain: the weight-learning recall appears to be measured on the same 30 query cases used for training, which is an evaluation-design weakness rather than a circular reduction, and Lemma VI.1's global pruning bound uses radius r rather than r divided by the modality weight, which looks unsound for fractional weights and is a correctness issue outside the scope of circularity. Neither concern makes the reported results equivalent to the paper's inputs by definition, so the circularity score remains low.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim depends on the representability of user intent as a weighted linear sum of per-modality metric distances, on the availability of ground-truth query cases, and on standard metric-space properties. No code or data artifacts are shipped to test these assumptions in practice; the only hand-chosen numeric parameters disclosed are the d_hidden threshold and the unspecified penalty coefficient lambda.

free parameters (2)
  • Hidden dimension threshold for index selection (d_hidden > 5) = 5
    Algorithm 2, lines 8-12: data modalities are assigned to MVP-tree versus R-tree based on a hand-chosen threshold of 5; no sensitivity analysis is reported in the paper.
  • Penalty coefficient lambda in reward function variant (iii) = unspecified
    Section VII-B, Eq. (5): the penalty term for performance drops has no reported value or tuning procedure, yet the variant is experimentally compared.
assumptions (5)
  • domain assumption Each data modality admits a metric distance delta_i with symmetry, non-negativity, identity, and triangle inequality that captures semantic similarity.
    Section III defines metric spaces and Section IV applies the model to images, text, strings, and numeric attributes; this is assumed rather than validated against user perception.
  • ad hoc to paper User retrieval intent over multi-modal data is representable as a fixed linear weighted sum delta_W = sum omega_i * delta_i.
    Definition III.1 and the weight-learning objective in Section V assume linear separability of modality importance; any non-linear user similarity cannot be represented.
  • domain assumption Ground-truth k-nearest neighbor sets G+ for a small number of query points are available from users.
    Section V-A requires N query cases with corresponding true kNNs; the paper's weight-learning evaluation and real-world utility depend on this availability.
  • domain assumption Distance normalization by twice the observed median makes different metric spaces comparable.
    Section III, immediately after Definition III.1; the normalization is dataset-dependent and no update strategy is given for data drift.
  • standard math Triangle inequality holds for each metric space.
    Used in pruning lemmas VI.1 and VI.2 to guarantee that pruned objects cannot be query results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of OneDB: A Distributed Multi-Metric Data Similarity Search System." pith.science (2026). https://pith.science/paper/T6UUXCJ6

@misc{pith2026250704256,
  author       = {Pith},
  title        = {Pith review of: OneDB: A Distributed Multi-Metric Data Similarity Search System},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T6UUXCJ6}},
  note         = {Machine review of arXiv:2507.04256}
}
read the original abstract

Increasingly massive volumes of multi-modal data are being accumulated in many {real world} settings, including in health care and e-commerce. This development calls for effective general-purpose data management solutions for multi-modal data. Such a solution must facilitate user-friendly and accurate retrieval of any multi-modal data according to diverse application requirements. Further, such a solution must be capable of efficient and scalable retrieval. To address this need, we present OneDB, a distributed multi-metric data similarity retrieval system. This system exploits the fact that data of diverse modalities, such as text, images, and video, can be represented as metric data. The system thus affords each data modality its own metric space with its own distance function and then uses a multi-metric model to unify multi-modal data. The system features several innovations: (i) an extended Spart SQL query interface; (ii) lightweight means of learning appropriate weights of different modalities when retrieving multi-modal data to enable accurate retrieval; (iii) smart search-space pruning strategies that improve efficiency; (iv) two-layered indexing of data to ensure load-balancing during distributed processing; and (v) end-to-end system parameter autotuning. Experiments on three real-life datasets and two synthetic datasets offer evidence that the system is capable of state-of-the-art performance: (i) efficient and effective weight learning; (ii) retrieval accuracy improvements of 12.63\%--30.75\% over the state-of-the-art vector similarity search system at comparable efficiency; (iii) accelerated search by 2.5--5.75x over state-of-the-art single- or multi-metric solutions; (iv) demonstrated high scalability; and (v) parameter tuning that enables performance improvements of 15+%.

Figures

Figures reproduced from arXiv: 2507.04256 by the authors.

Figure 1
Figure 1. Example of similarity search on multi-modal data [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A multi-metric space example metric space, the set M contains data objects of a single type; hence, all objects are compared using the same metric δ. Next, a multi-metric space (M, ∆) integrates multiple dis￾tinct metric spaces. In this context, M is a collection of domains Mi (where 1 ≤ i ≤ m), and ∆ is a corresponding set of distance metrics δi , each associated with its respective domain Mi . This structure enabl… view at source ↗
Figure 4
Figure 4. An example of dual-layer index Algorithm 2: Local Index Construction Input: Dataset S with N points, the number m of metric spacers Output: Indexes {I1, I2, . . . , Im} for each metric space 1: foreach o M j ∈ S, j ∈ {1, 2, . . . , N} do 2: {o 1 j , o2 j , . . . , oM j } ← Decompose(o S j ) // Decompose into separated metric spaces 3: foreach i ∈ {1, 2, . . . , m} do 4: Ci ← {o i 1, oi 2, . . . , oi N } // Dataset i… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: MMRQ performance vs. r To evaluate the update performance of OneDB, we perform 0.1%–1% random updates (object deletions and insertions). As shown in Table IV, OneDB achieves low update costs, and stable query performance with only minor increases in latency. C. Similar…
Figure 6
Figure 6. Figure 6: MMkNNQ performance vs. k recall(%) Milvus OneDB 1 2 3 4 5 60 70 80 90 100 51.6 54.2 59.9 62.9 64.2 55.2 55.2 55.2 55.2 55.2 0 30 60 90 running time(ms) recall (%) rate (a) Air 1 2 3 4 5 70 80 90 52.7 54.4 61.0 64.1 65.6 59.1 59.1 59.1 59.1 59.1 0 30 60 90 running time(…
Figure 7
Figure 7. Figure 7: Comparison with vector database Milvus Comparison with Vector Database. We choose the state-of￾the-art vector database Milvus for comparision, as it supports distributed multi-vector kNN search. It is important to note that Milvus cannot support multi-vector range sear…
Figure 11
Figure 11. Figure 11: Case Study Exp. Log. Penalty-based 125 145 165 0 10 20 30 40 50 550 500 450 400 running time (ms) throughput (queries/min) steps (a) Air 100 120 140 0 10 20 30 40 50 600 550 500 450 running time (ms) throughput (queries/min) steps (b) Synthesis [PITH_FULL_IMAGE:figur…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 42 canonical work pages

  1. [1]

    Multimodal image-text matching improves retrieval-based chest x-ray report generation,

    J. Jeong, K. Tian, A. Li, S. Hartung, S. Adithan, F. Behzadi, J. Calle, D. Osayande, M. Pohlen, and P. Rajpurkar, “Multimodal image-text matching improves retrieval-based chest x-ray report generation,” in Medical Imaging with Deep Learning . PMLR, 2024, pp. 978–990

    work page 2024

  2. [2]

    Wip-skod: A framework for situational knowledge on demand,

    S. Palacios, K. Solaiman, P. Angin, A. Nesen, B. Bhargava, Z. Collins, A. Sipser, M. Stonebraker, and J. Macdonald, “Wip-skod: A framework for situational knowledge on demand,” in Heterogeneous Data Manage- ment, Polystores, and Analytics for Healthcare: VLDB 2019 Workshops, Poly and DMAH, Los Angeles, CA, USA, August 30, 2019, Revised Selected Papers 5. ...

    work page 2019

  3. [3]

    Multi-feature, multi- modal, and multi-source social event detection: A comprehensive sur- vey,

    I. Afyouni, Z. Al Aghbari, and R. A. Razack, “Multi-feature, multi- modal, and multi-source social event detection: A comprehensive sur- vey,” Information Fusion, vol. 79, pp. 279–308, 2022

    work page 2022

  4. [4]

    Similarity search in the blink of an eye with compressed indices,

    C. Aguerrebere, I. Bhati, M. Hildebrand, M. Tepper, and T. Willke, “Similarity search in the blink of an eye with compressed indices,” arXiv preprint arXiv:2304.04759, 2023

    arXiv 2023

  5. [5]

    A one-size-fits- three representation learning framework for patient similarity search,

    Y . Huang, F. Luo, X. Wang, Z. Di, B. Li, and B. Luo, “A one-size-fits- three representation learning framework for patient similarity search,” Data Science and Engineering , vol. 8, no. 3, pp. 306–317, 2023

    work page 2023

  6. [6]

    Dita: Distributed in-memory trajectory analytics,

    Z. Shang, G. Li, and Z. Bao, “Dita: Distributed in-memory trajectory analytics,” in Proceedings of the 2018 International Conference on Management of Data , 2018, pp. 725–740

    work page 2018

  7. [7]

    REPOSE: distributed top-k trajectory similarity search with local reference point tries,

    B. Zheng, L. Weng, X. Zhao, K. Zeng, X. Zhou, and C. S. Jensen, “REPOSE: distributed top-k trajectory similarity search with local reference point tries,” in ICDE, 2021, pp. 708–719

    work page 2021

  8. [8]

    An adaptive distributed index for similarity queries in metric spaces,

    M. Zhu, D. Shen, Y . Kou, T. Nie, and G. Yu, “An adaptive distributed index for similarity queries in metric spaces,” in WAIM, vol. 7418. Springer, 2012, pp. 222–227

    work page 2012

Show all 45 references
  1. [9]

    Efficient metric indexing for similarity search,

    L. Chen, Y . Gao, X. Li, C. S. Jensen, and G. Chen, “Efficient metric indexing for similarity search,” in ICDE, 2015, pp. 591–602

    2015

  2. [10]

    Dims: Distributed index for similarity search in metric spaces,

    Y . Zhu, C. Luo, T. Qian, L. Chen, Y . Gao, and B. Zheng, “Dims: Distributed index for similarity search in metric spaces,” 2024. [Online]. Available: https://arxiv.org/abs/2410.05091

    2024 arXiv

  3. [11]

    Indexing multi-metric data,

    M. Franzke, T. Emrich, A. Z ¨ufle, and M. Renz, “Indexing multi-metric data,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016, pp. 1122–1133

    2016

  4. [12]

    Analyticdb- v: a hybrid analytical engine towards query fusion for structured and unstructured data,

    C. Wei, B. Wu, S. Wang, R. Lou, C. Zhan, F. Li, and Y . Cai, “Analyticdb- v: a hybrid analytical engine towards query fusion for structured and unstructured data,” Proceedings of the VLDB Endowment , vol. 13, no. 12, pp. 3152–3165, 2020

    2020

  5. [13]

    Pase: Postgresql ultra-high- dimensional approximate nearest neighbor search extension,

    W. Yang, T. Li, G. Fang, and H. Wei, “Pase: Postgresql ultra-high- dimensional approximate nearest neighbor search extension,” in Pro- ceedings of the 2020 ACM SIGMOD international conference on man- agement of data , 2020, pp. 2241–2253

    2020

  6. [14]

    Elasticsearch,

    B. Elasticsearch, “Elasticsearch,” software], version, vol. 6, no. 1, 2018

    2018

  7. [15]

    pgvector,

    “pgvector,” https://github.com/pgvector/pgvector, accessed: 2024-10-14

    2024

  8. [16]

    {VBASE}: Unifying online vector similarity search and relational queries via relaxed monotonicity,

    Q. Zhang, S. Xu, Q. Chen, G. Sui, J. Xie, Z. Cai, Y . Chen, Y . He, Y . Yang, F. Yanget al. , “ {VBASE}: Unifying online vector similarity search and relational queries via relaxed monotonicity,” in 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23)...

    2023

  9. [17]

    Milvus: A purpose-built vector data management system,

    J. Wang, X. Yi, R. Guo, H. Jin, P. Xu, S. Li, X. Wang, X. Guo, C. Li, X. Xu et al., “Milvus: A purpose-built vector data management system,” in Proceedings of the 2021 International Conference on Management of Data, 2021, pp. 2614–2627

    2021

  10. [18]

    Desire: An efficient dynamic cluster-based forest indexing for similarity search in multi- metric spaces,

    Y . Zhu, L. Chen, Y . Gao, B. Zheng, and P. Wang, “Desire: An efficient dynamic cluster-based forest indexing for similarity search in multi- metric spaces,” Proceedings of the VLDB Endowment , vol. 15, no. 10, p. 2121, 2022

    2022

  11. [19]

    Dima: A distributed in-memory similarity-based query processing system,

    J. Sun, Z. Shang, G. Li, D. Deng, and Z. Bao, “Dima: A distributed in-memory similarity-based query processing system,” Proceedings of the VLDB Endowment , vol. 10, no. 12, pp. 1925–1928, 2017

    1925

  12. [20]

    Simba: Efficient in-memory spatial analytics,

    D. Xie, F. Li, B. Yao, G. Li, L. Zhou, and M. Guo, “Simba: Efficient in-memory spatial analytics,” in Proceedings of the 2016 international conference on management of data , 2016, pp. 1071–1085

    2016

  13. [21]

    Thesios: Synthesizing accurate counterfactual i/o traces from i/o samples,

    M. Phothilimthana, S. Kadekodi, S. Ghodrati, S. Moon, and M. Maas, “Thesios: Synthesizing accurate counterfactual i/o traces from i/o samples,” in ASPLOS 2024 , 2024. [Online]. Available: https://dl.acm.org/doi/10.1145/3620666.3651337

    2024

  14. [22]

    Indexing metric spaces for exact similarity search,

    L. Chen, Y . Gao, X. Song, Z. Li, Y . Zhu, X. Miao, and C. S. Jensen, “Indexing metric spaces for exact similarity search,” ACM Computing Surveys, vol. 55, no. 6, pp. 1–39, 2022

    2022

  15. [23]

    Satisfying general proximity/similarity queries with metric trees,

    J. K. Uhlmann, “Satisfying general proximity/similarity queries with metric trees,” Inf. Process. Lett. , vol. 40, no. 4, pp. 175–179, 1991

    1991

  16. [24]

    M-tree: An efficient access method for similarity search in metric spaces,

    P. Ciaccia, M. Patella, and P. Zezula, “M-tree: An efficient access method for similarity search in metric spaces,” in VLDB, 1997, pp. 426–435

    1997

  17. [25]

    A new version of the nearest- neighbour approximating and eliminating search algorithm (aesa) with linear preprocessing time and memory requirements,

    M. L. Mic ´o, J. Oncina, and E. Vidal, “A new version of the nearest- neighbour approximating and eliminating search algorithm (aesa) with linear preprocessing time and memory requirements,” Pattern Recogni- tion Letters, vol. 15, no. 1, pp. 9–17, 1994

    1994

  18. [26]

    Distance-based indexing for high- dimensional metric spaces,

    T. Bozkaya and Z. M. ¨Ozsoyoglu, “Distance-based indexing for high- dimensional metric spaces,” in SIGMOD, 1997, pp. 357–368

    1997

  19. [27]

    Indexing large metric spaces for similarity search queries,

    ——, “Indexing large metric spaces for similarity search queries,” ACM Trans. Database Syst. , vol. 24, no. 3, pp. 361–404, 1999

    1999

  20. [28]

    Near neighbor search in large metric spaces,

    S. Brin, “Near neighbor search in large metric spaces,” in VLDB, 1995, pp. 574–584

    1995

  21. [29]

    A data structure and an algorithm for the nearest point problem,

    I. Kalantari and G. McDonald, “A data structure and an algorithm for the nearest point problem,” IEEE Transactions on Software Engineering, no. 5, pp. 631–634, 1983

    1983

  22. [30]

    Searching in metric spaces with user-defined and approximate distances,

    P. Ciaccia and M. Patella, “Searching in metric spaces with user-defined and approximate distances,” ACM Trans. Database Syst., vol. 27, no. 4, pp. 398–437, 2002

    2002

  23. [31]

    The m2-tree: Processing complex multi-feature queries with just one index

    ——, “The m2-tree: Processing complex multi-feature queries with just one index.” in DELOS, 2000

    2000

  24. [32]

    Adapting metric indexes for searching in multi-metric spaces,

    B. Bustos, S. Kreft, and T. Skopal, “Adapting metric indexes for searching in multi-metric spaces,” Multimedia Tools and Applications , vol. 58, no. 3, pp. 467–496, 2012

    2012

  25. [33]

    Indexing multi-metric data,

    M. Franzke, T. Emrich, A. Z ¨ufle, and M. Renz, “Indexing multi-metric data,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016, pp. 1122–1133

    2016

  26. [34]

    Celik, New approaches to similarity searching in metric spaces

    C. Celik, New approaches to similarity searching in metric spaces . University of Maryland, College Park, 2006

    2006

  27. [35]

    A pivot-based index structure for combination of feature vectors,

    B. Bustos, D. Keim, and T. Schreck, “A pivot-based index structure for combination of feature vectors,” in Proceedings of the 2005 ACM Symposium on Applied Computing , ser. SAC ’05. New York, NY , USA: Association for Computing Machinery, 2005, p. 1180–1184. [Online]. Availabl...

    2005

  28. [36]

    Efficient indexing of multiple metric spaces with spectra,

    G. F. Zabot, M. T. Cazzolato, L. C. Scabora, A. J. Traina, and C. Traina, “Efficient indexing of multiple metric spaces with spectra,” in2019 IEEE International Symposium on Multimedia (ISM) . IEEE, 2019, pp. 169– 1697

    2019

  29. [37]

    Similarity grid for searching in metric spaces,

    M. Batko, C. Gennaro, and P. Zezula, “Similarity grid for searching in metric spaces,” in Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures: 6th Thematic Workshop of the EU Network of Excellence DELOS, Cagliari, Italy, June 24-25, 2004. Revised Selec...

    2004

  30. [38]

    Metric index: An efficient and scalable solution for precise and approximate similarity search,

    D. Novak, M. Batko, and P. Zezula, “Metric index: An efficient and scalable solution for precise and approximate similarity search,” Inf. Syst., vol. 36, no. 4, pp. 721–733, 2011

    2011

  31. [39]

    Efficient range query processing in metric spaces over highly distributed data,

    C. Doulkeridis, A. Vlachou, Y . Kotidis, and M. Vazirgiannis, “Efficient range query processing in metric spaces over highly distributed data,” Distributed Parallel Databases, vol. 26, no. 2-3, pp. 155–180, 2009

    2009

  32. [40]

    idistance: An adaptive b +-tree based indexing method for nearest neighbor search,

    H. V . Jagadish, B. C. Ooi, K. Tan, C. Yu, and R. Zhang, “idistance: An adaptive b +-tree based indexing method for nearest neighbor search,” ACM Trans. Database Syst. , vol. 30, no. 2, pp. 364–397, 2005

    2005

  33. [41]

    Dis- tributed similarity queries in metric spaces,

    K. Yang, X. Ding, Y . Zhang, L. Chen, B. Zheng, and Y . Gao, “Dis- tributed similarity queries in metric spaces,” Data Sci. Eng., vol. 4, no. 2, pp. 93–108, 2019

    2019

  34. [42]

    Distributed publish/subscribe query processing on the spatio-textual data stream,

    Z. Chen, G. Cong, Z. Zhang, T. Z. Fuz, and L. Chen, “Distributed publish/subscribe query processing on the spatio-textual data stream,” in 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE, 2017, pp. 1095–1106

    2017

  35. [43]

    Balance-aware distributed string similarity-based query processing system,

    J. Sun, Z. Shang, G. Li, D. Deng, and Z. Bao, “Balance-aware distributed string similarity-based query processing system,” Proceedings of the VLDB Endowment, vol. 12, no. 9, pp. 961–974, 2019

    2019

  36. [44]

    Pivot selection: Dimension reduction for distance-based indexing,

    R. Mao, W. L. Miranker, and D. P. Miranker, “Pivot selection: Dimension reduction for distance-based indexing,” Journal of Discrete Algorithms , vol. 13, pp. 32–46, 2012

    2012

  37. [45]

    Cdbtune+: An efficient deep reinforcement learning-based automatic cloud database tuning system,

    J. Zhang, K. Zhou, G. Li, Y . Liu, M. Xie, B. Cheng, and J. Xing, “Cdbtune+: An efficient deep reinforcement learning-based automatic cloud database tuning system,” The VLDB Journal , vol. 30, no. 6, pp. 959–987, 2021

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.