Pith. sign in

REVIEW 4 major objections 6 minor 2 cited by

Optimization Strategies for Parallel Computation of Skylines

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read Two orthogonal optimizations for parallel skyline computation: representative filtering and a parallel final phase.

desk verdict Sound and useful experimental paper on parallel skyline optimization, but the NoSeq correctness claim for Sliced partitioning rests on an unspecified tie-breaking rule that can break the algorithm. read the letter →

arxiv 2411.14968 v1 pith:RRAN42LN submitted 2024-11-22 cs.DB

Paolo Ciaccia , Davide Martinenghi This is my paper · ORCID
classification cs.DB
keywords skylinequeriesparallelcomputationPySparkpartitioningstrategiesrepresentativefilteringNoSeqdominancemulti-core
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

This paper tries to establish that the standard two-phase scheme for parallel skyline computation—local skylines per partition, then a final global pass—can be improved by two orthogonal optimizations, and that the supposedly sequential final phase can itself be parallelized. The key is a relative-skyline identity: if each partition's local skyline is re-checked against only its potential dominators among the other local skylines, the union of those re-checked results is exactly the global skyline. The authors propose Sliced partitioning, show that representative filtering nearly always helps, and report experiments where NoSeq, the parallel-cleanup scheme, is fastest except when the number of partitions far exceeds the number of cores. A sympathetic reader would care because skyline computation is quadratic in the worst case, so removing a sequential bottleneck is exactly the step that makes distributed skyline computation scale.

What carries the argument

The workhorse is the relative skyline $\mathrm{Sky}_c(r)$, defined as the tuples in $r$ not dominated by any tuple in $c$. Proposition 2's identity is the mechanism: it decomposes the global skyline into relative skylines computed per partition with respect to potential-dominator sets $\mathit{pd}_i$. For Sliced partitioning, tuples are assigned to partitions by sorting on one attribute, so $\mathit{pd}_i=\{u_j\mid j<i\}$; for Grid, weak grid dominance among cells gives $\mathit{pd}_i$; for Random and Angular, $\mathit{pd}_i=u\setminus u_i$. This identity is what lets the final sequential phase of Algorithm 2 be replaced by parallel relative-skyline computations.

What would settle it

Take an anticorrelated dataset with high dimensionality, set the number of partitions $p$ much larger than the number of cores (for example $p = 20c$), and compare NoSeq against Sliced with representative filtering: if NoSeq is not faster, or if the broadcast and checking time for the union $u$ exceeds the sequential final-pass time, the practical claim fails. A more direct check is to measure $|u|/|r|$: if the local skylines occupy a large fraction of the dataset, NoSeq's overhead should dominate.

Watch

Extended reading notes

Core claim

The central claim is Proposition 2: with a relation $r$ partitioned into $r_1,\dots,r_p$, local skylines $u_i=\mathrm{Sky}(r_i)$, and $u$ their union, if for each $i$ the set $\mathit{pd}_i\subseteq u\setminus u_i$ contains every tuple that can dominate a non-skyline tuple of $u_i$, then $\mathrm{Sky}(r)=\mathrm{Sky}_{\mathit{pd}_1}(u_1)\cup\dots\cup\mathrm{Sky}_{\mathit{pd}_p}(u_p)$. This identity turns the final cleanup of Algorithm 2 into another parallel phase: each node keeps only the tuples of its local skyline not dominated by $\mathit{pd}_i$. For Grid and Sliced partitioning, $\mathit{pd}_i$ is much smaller than $u$ because the partitioning induces a weak dominance order; for Random and Angular one must use $\mathit{pd}_i=u\setminus u_i$. The paper also reports experiments on synthetic and real datasets in a 120-core PySpark cluster showing that Sliced partitioning with NoSeq is the fastest configuration in most scenarios, and that representative filtering helps with every partitioning strategy.

Load-bearing premise

The load-bearing premise is that the union $u$ of local skylines is small enough that sending it to every node and running the parallel relative-skyline checks costs less than one sequential scan of $u$; the paper's own partition-count experiments show this premise fails when $p$ grows too large.

Editorial extensions

If this is right

  • The final phase of Algorithm 2 ceases to be a sequential bottleneck: each node can prune its own local skyline in parallel once it receives the potential-dominator sets.
  • For Sliced and Grid partitioning, the potential-dominator sets are much smaller than the full union of local skylines, so NoSeq's communication cost is bounded by the partition structure rather than the whole dataset.
  • Representative filtering is reported to reduce execution time for every partitioning strategy and dataset tested, with Sorted representatives preferable on anticorrelated data and Region on uniform and correlated data.
  • NoSeq on Sliced is the recommended configuration unless the number of partitions greatly exceeds the number of cores, in which case Sliced or Angular with representative filtering is the preferred alternative.
  • Because higher dimensionality yields larger skylines, NoSeq remains the most efficient option there, since the union of local skylines stays manageable enough to broadcast to all nodes.

Reading between the lines

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

  • Since Proposition 2 does not dictate how $\mathit{pd}_i$ must be chosen, an adaptive implementation could select per-partition potential dominators based on data statistics, possibly shrinking the broadcast even further than the paper's fixed choices.
  • Because representative filtering and NoSeq are described as orthogonal, combining both is a direct testable extension; the paper evaluates them separately, so stacking them should give at least the benefit of either alone.
  • The relative-skyline formulation suggests a streaming or incremental variant: as new partitions arrive, only the affected $\mathit{pd}_i$ sets need re-checking instead of recomputing the global skyline from scratch.
  • The paper does not report the Sliced sort attribute or the exact number of representatives used, so a natural test is to vary those choices and check whether the qualitative ranking among Sliced+, Angular+, and NoSeq survives.
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

4 major / 6 minor

Summary. The paper surveys partitioning strategies for parallel skyline computation (Random, Grid, Angular, and a new Sliced scheme) and proposes two orthogonal optimizations: Representative Filtering, which shares a small set of strong tuples across partitions to prune dominated tuples early, and NoSeq, which replaces the final sequential skyline pass with a second parallel phase. The authors prove two elementary propositions (Proposition 1: skyline of union equals skyline of union of local skylines; Proposition 2: the relative-skyline identity that justifies NoSeq) and report a PySpark experimental study on synthetic and real datasets, concluding that Sliced and Angular with the proposed optimizations outperform Random and Grid, and that NoSeq is usually the fastest configuration.

Significance. If the results hold, the paper makes a useful practical contribution: NoSeq removes the sequential bottleneck that limits standard two-phase parallel skyline algorithms, and Sliced is a simple, effective partitioning method. The theoretical propositions are correct and elementary, and the experimental comparison covers a wide parameter space. However, the central correctness claim for Sliced+NoSeq depends on an unstated tie-breaking assumption in the sorting step, and key experimental parameters are left unspecified, so the reproducibility and the strength of the empirical conclusions are currently limited. The paper also ships no code or data, which further reduces the immediate utility of the experimental claims.

major comments (4)
  1. [§3.4, §4.2] The correctness of Sliced Partitioning and of the NoSeq identity for Sliced depends on the sorted order being a topological sort with respect to dominance: if s ≺ t, then s must appear before t. The paper only says that the dataset is sorted by one chosen dimension 'possibly with the addition of a tie-breaking criterion to obtain a total ordering', which is not enough. An arbitrary tie-break can violate the topological property and make both SFS and the final parallel phase return dominated tuples. For example, with tuples u=(4,100), t=(5,10), s=(5,5), v=(6,1) and p=2, sorting by A1 ascending and breaking ties by A2 descending yields order u,t,s,v and partitions {u,t} and {s,v}; then t is dominated by s but is processed first, so SFS on the first partition returns {u,t,s} instead of {u,s}, and NoSeq returns {u,t,s,v} instead of the true skyline {u,s,v}. The paper never states the tie-breaking rule used in the experiments, so the text as written does not establish that Sliced and Sliced+NoSeq compute Sky(r) correctly. The fix is to require a dominance-consistent total order (e.g., full lexicographic order on all attributes, or sorting by any monotone scoring function) and to specify the choice in the experimental configuration.
  2. [§5.2, Table 1] The experimental section does not report several parameters that directly determine the results: the number of representative tuples used by Representative Filtering, the sort attribute (and tie-breaking rule) for Sliced, and the default values that Table 1 promises in bold (no bold values appear in the rendered table). Without these, the efficiency numbers cannot be reproduced, and the comparison between Sorted and Region filtering in Figure 3 cannot be independently checked. Please provide a complete configuration table with explicit default values and state the number of representatives and the Sliced sort specification.
  3. [§5 (Varying number of partitions), Conclusion] The conclusion that both optimization strategies are 'always beneficial, with any number of partitions and cores' is too strong and is contradicted by the paper's own data. In the 'Varying number of partitions' experiment, the text states that NoSeq shows degraded performance as p becomes large (e.g., 164,183 local-skyline tuples at p=3600 vs. 27,328 at p=120), and Figure 7a exhibits this degradation. Also, on the RES dataset (Figure 6c), Angular+ outperforms Sliced+, so the claim of uniform benefit needs to be restricted to the tested configurations and to the comparison of each optimization against its plain baseline (Sliced+ vs. Sliced, Angular+ vs. Angular), not as an absolute statement across all strategies and parameters.
  4. [§5.2, Figures 4–7] All efficiency plots report a single run per configuration with no error bars, variance, or statistical significance. Since the experiments run on a Spark cluster of virtual machines, scheduling and network variability are expected to cause measurable run-to-run differences. Without repeated runs (or at least reporting mean and standard deviation over a few runs), the performance ordering among Sliced+, Angular+, and NoSeq could be within noise. Please add variance information or justify why single runs are representative.
minor comments (6)
  1. [§3.4] The formula p(t) = floor((i-1)*p/(N-1)) appears to be off by one: for N=4 and p=2 it yields indices 0, 0, 1, 2, i.e., three distinct partitions instead of two. The intended formula is probably floor((i-1)*p/N) or the range of the index should be clarified.
  2. [§4.2] In Proposition 2, pdi is defined as a subset of u \ ui, i.e., a set of tuples, but for Grid and Sliced it is written as a set of local skylines {uj | ...}. This is a type inconsistency; the text should say pdi = ⋃_{j in ...} uj (or use a notation that distinguishes indices from tuple sets).
  3. [§4.1] The Region strategy for representative selection requires normalized data, and the paper says 'in the following we shall always implicitly refer to the [0,1] domain', but it is not stated whether the real datasets (HOU, RES) were normalized before applying Region or any other strategy that uses the dominance-region volume. Please clarify.
  4. [§5.2] The sentence 'Figure 4c shows ... on other synthetic dataset types is available in [8–10]' appears to be a truncated fragment; please rewrite it to specify where the additional results are reported.
  5. [Throughout] The abbreviation NoSeq is never expanded at first use; consider defining it as 'no sequential phase'. Also, the paper would benefit from a short statement about code/data availability, since none is currently provided.
  6. [References] References [8], [9], and [10] are master's theses that are not publicly identified by URL or repository; if they are meant to support the 'fuller account' of experiments, please make them accessible or summarize the relevant results in the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the algorithmic identities are proven in-text, and the self-citations are not load-bearing; the Sliced tie-breaking gap is a correctness issue, not a circular derivation.

full rationale

The paper's central derivations are self-contained mathematical statements, not predictions from fitted inputs. Proposition 1 (Sky(r) = Sky(Sky(r1) ∪ ... ∪ Sky(rp))) is proved by the standard dominance-transitivity argument. Proposition 2 is an identity conditional on an explicit hypothesis about pdi; the paper then supplies concrete pdi sets for Random/Angular, Grid, and Sliced. These are derived from the partitions' geometric/order properties rather than from the target skyline, so the identity is not equivalent to its own conclusion by construction. No fitted parameters are renamed as predictions: the experimental section measures execution times directly, and the partition counts are set to available cores or to the nearest grid values, not adapted to make the results match. The paper's self-citations are not load-bearing. The reference to [3] for the scoring-function characterization of skylines is an independently known result; citations to [8]-[10] only point to fuller experimental accounts; and the related-work self-citations do not justify the correctness of the new algorithms. One genuine weakness exists, but it is a correctness gap rather than circularity: the Sliced strategy assumes a total ordering under which a dominating tuple always precedes the tuple it dominates, so that pdi = {uj | j < i} satisfies condition (2). The paper does not specify the tie-breaking criterion beyond 'possibly with the addition of a tie-breaking criterion to obtain a total ordering,' and an arbitrary tie-break can place a dominator after the dominated tuple, making both SFS-style local skylines and the NoSeq final phase return dominated tuples. This is an unproven assumption and an implementation/robustness risk, but it does not amount to the derivation reducing to its inputs or to a self-citation chain. The paper also explicitly reports the performance limitation of NoSeq when the number of partitions grows large (Section 5, Varying number of partitions), which is a stated limitation, not a circularity. Overall, no step in the claimed derivation chain exhibits self-definition, fitted-input-as-prediction, or load-bearing self-citation.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper's central claims are algorithmic; the only new 'things' are strategies, not entities. The free parameters are configuration choices that are not reported, which affects reproducibility. The axioms are standard skyline properties or explicit partition-property assumptions.

free parameters (3)
  • Number of representative tuples = unspecified
    Section 4.1 says 'a few' representatives but never states how many; the Sorted strategy picks 'the first tuples in each partition' without a count, so the pruning power and overhead depend on a hidden parameter.
  • Sorting attribute for Sliced partitioning = unspecified
    Section 3.4 says to sort by one chosen dimension but does not say how the dimension is chosen; results may depend on this choice.
  • Default number of partitions p = 120
    Chosen to equal the number of cores 'for maximum efficiency' (Section 5.2); it is a by-hand configuration, not derived.
assumptions (5)
  • domain assumption Attributes are numeric and restricted to non-negative reals; dominance is defined by ≤.
    Section 2 restricts attributes to R+; this is standard for skyline queries.
  • standard math SFS with a monotone scoring function produces a topological order with respect to dominance.
    Invoked in Algorithm 1; the topological-sort property is standard for SFS.
  • standard math Sky(r) = Sky(Sky(r1) ∪ ... ∪ Sky(rp)) for a partition of r.
    Proposition 1, proven in Section 3.
  • domain assumption For Sliced, tuples in earlier partitions cannot be dominated by tuples in later partitions.
    Used to define pdi = {uj | j < i} in Section 4.2.
  • domain assumption For Grid, absence of weak grid dominance between cells implies no tuple in one cell dominates any tuple in the other.
    Used to restrict pdi for Grid in Section 4.2.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Optimization Strategies for Parallel Computation of Skylines." pith.science (2026). https://pith.science/paper/RRAN42LN

@misc{pith2026241114968,
  author       = {Pith},
  title        = {Pith review of: Optimization Strategies for Parallel Computation of Skylines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RRAN42LN}},
  note         = {Machine review of arXiv:2411.14968}
}
read the original abstract

Skyline queries are one of the most widely adopted tools for Multi-Criteria Analysis, with applications covering diverse domains, including, e.g., Database Systems, Data Mining, and Decision Making. Skylines indeed offer a useful overview of the most suitable alternatives in a dataset, while discarding all the options that are dominated by (i.e., worse than) others. The intrinsically quadratic complexity associated with skyline computation has pushed researchers to identify strategies for parallelizing the task, particularly by partitioning the dataset at hand. In this paper, after reviewing the main partitioning approaches available in the relevant literature, we propose two orthogonal optimization strategies for reducing the computational overhead, and compare them experimentally in a multi-core environment equipped with PySpark.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Computing the Non-Dominated Flexible Skyline in Vertically Distributed Datasets with No Random Access

    cs.DB 2024-12 conditional novelty 6.0 of 10

    An NRA-style algorithm that computes the non-k-dominated flexible skyline in vertically distributed, no-random-access settings, with correctness and instance-optimality proofs.

  2. Parallelizing the Computation of Robustness for Measuring the Strength of Tuples

    cs.DB 2024-12 conditional novelty 5.0 of 10

    Known parallel skyline partitioning strategies can be adapted to compute grid resistance, with Sliced partitioning giving the most stable speedups across datasets.

Reference graph

Works this paper leans on

24 extracted references · 8 canonical work pages · cited by 2 Pith papers

  1. [1]

    Kung, H.T., Luccio, F., Preparata, F.P.: On finding the maxima of a set of vectors. J. ACM 22(4), 469–476 (1975) https://doi.org/10.1145/321906.321910

    arXiv 1975

  2. [2]

    In: Proceedings of the 17th International Conference on Data Engineering, April 2-6, 2001, Heidel- berg, Germany, pp

    B¨ orzs¨ onyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering, April 2-6, 2001, Heidel- berg, Germany, pp. 421–430 (2001). https://doi.org/10.1109/ICDE.2001.914855 . http://dx.doi.org/10.1109/ICDE.2001.914855

    arXiv 2001

  3. [3]

    SIGMOD Record 42(3), 6–18 (2013) https://doi.org/10.1145/2536669.2536671

    Chomicki, J., Ciaccia, P., Meneghetti, N.: Skyline queries, front and back. SIGMOD Record 42(3), 6–18 (2013) https://doi.org/10.1145/2536669.2536671

    work page arXiv 2013

  4. [4]

    In: Dayal, U., Ramamritham, K., Vijayaraman, T.M

    Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: Dayal, U., Ramamritham, K., Vijayaraman, T.M. (eds.) Proceedings of the 19th International Conference on Data Engineering, March 5-8, 2003, Banga- lore, India, pp. 717–719 (2003). https://doi.org/10.1109/ICDE.2003.1260846 . https://doi.org/10.1109/ICDE.2003.1260846

    arXiv 2003

  5. [5]

    In: 21st Annual International Symposium on High Performance Com- puting Systems and Applications (HPCS 2007), 13-16 May 2007, Saskatoon, Saskatchewan, Canada, p

    Cosgaya-Lozano, A., Rau-Chaplin, A., Zeh, N.: Parallel computation of skyline queries. In: 21st Annual International Symposium on High Performance Com- puting Systems and Applications (HPCS 2007), 13-16 May 2007, Saskatoon, Saskatchewan, Canada, p. 12 (2007). https://doi.org/10.1109/HPCS.2007.25 . https://doi.org/10.1109/HPCS.2007.25

    work page doi:10.1109/hpcs.2007.25 2007

  6. [6]

    In: Amer-Yahia, S., Christophides, V., Kementsietsidis, A., Garo- falakis, M.N., Idreos, S., Leroy, V

    Mullesgaard, K., Pederseny, J.L., Lu, H., Zhou, Y.: Efficient skyline computation in mapreduce. In: Amer-Yahia, S., Christophides, V., Kementsietsidis, A., Garo- falakis, M.N., Idreos, S., Leroy, V. (eds.) Proceedings of the 17th International Conference on Extending Database Technology, EDBT 2014, Athens, Greece, March 24-28, 2014, pp. 37–48 (2014). http...

    doi:10.5441/002/edbt.2014 2014

  7. [7]

    In: Wang, J.T

    Vlachou, A., Doulkeridis, C., Kotidis, Y.: Angle-based space partitioning for efficient parallel skyline computation. In: Wang, J.T. (ed.) Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, June 10-12, 2008, pp. 227–238 (2008). https: //doi.org/10.1145/1376616.1376642 . https://doi.org/10.1145...

    arXiv 2008

  8. [8]

    Master’s Thesis, Politecnico di Milano 16

    Pindozzi, A.: Scalable Solutions for Skyline Computation Using PySpark: Explor- ing Parallel Algorithms. Master’s Thesis, Politecnico di Milano 16

    work page

Show all 24 references
  1. [9]

    Master’s Thesis, Politecnico di Milano

    De Lorenzis, E.: Computation of Flexible Skylines in a Distributed Environment. Master’s Thesis, Politecnico di Milano

  2. [10]

    Master’s Thesis, Politecnico di Milano

    Pinari, E.: Parallel Implementations of the Skyline Query Using PySpark. Master’s Thesis, Politecnico di Milano

  3. [11]

    In: Yu, P.S., Tsotras, V.J., Fox, E.A., Liu, B

    Bartolini, I., Ciaccia, P., Patella, M.: Salsa: computing the skyline without scanning the whole sky. In: Yu, P.S., Tsotras, V.J., Fox, E.A., Liu, B. (eds.) Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management, Arlington, Virginia, ...

    2006

  4. [12]

    TODS 30(1), 41–82 (2005) https://doi.org/10.1145/1061318

    Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. TODS 30(1), 41–82 (2005) https://doi.org/10.1145/1061318. 1061320

    2005 doi

  5. [13]

    ACM Trans

    Ciaccia, P., Martinenghi, D.: Flexible skylines: Dominance for arbitrary sets of monotone functions. ACM Trans. Database Syst. 45(4), 18–11845 (2020) https: //doi.org/10.1145/3406113

    2020 doi

  6. [14]

    PVLDB 10(11), 1454–1465 (2017) https://doi.org/10.14778/3137628.3137653

    Ciaccia, P., Martinenghi, D.: Reconciling skyline and ranking queries. PVLDB 10(11), 1454–1465 (2017) https://doi.org/10.14778/3137628.3137653

    2017

  7. [15]

    In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pp

    Ciaccia, P., Martinenghi, D.: F A + TA < FSA: Flexible score aggregation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pp. 57–66 (2018). https://doi.org/10.1145/3269206.3271753 ...

    2018

  8. [16]

    In: Bergamaschi, S., Noia, T.D., Maurino, A

    Ciaccia, P., Martinenghi, D.: Beyond skyline and ranking queries: Restricted skylines (extended abstract). In: Bergamaschi, S., Noia, T.D., Maurino, A. (eds.) Proceedings of the 26th Italian Symposium on Advanced Database Sys- tems, Castellaneta Marina (Taranto), Italy, June 2...

    2018

  9. [17]

    In: Amato, G., Gennaro, C., Oria, V., Radovanovic, M

    Bedo, M.V.N., Ciaccia, P., Martinenghi, D., Oliveira, D.: A k-skyband approach for feature selection. In: Amato, G., Gennaro, C., Oria, V., Radovanovic, M. (eds.) Similarity Search and Applications - 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2-4, 2019...

    2019 doi

  10. [18]

    In: Mecella, M., Amato, G., Gennaro, C

    Ciaccia, P., Martinenghi, D.: Flexible score aggregation (extended abstract). In: Mecella, M., Amato, G., Gennaro, C. (eds.) Proceedings of the 27th Italian Symposium on Advanced Database Systems, Castiglione della Pescaia (Gros- seto), Italy, June 16-19, 2019. CEUR Workshop P...

    2019

  11. [19]

    In: Li, G., Li, Z., Idreos, S., Srivastava, D

    Mouratidis, K., Li, K., Tang, B.: Marrying top-k with skyline queries: Relax- ing the preference input while producing output of controllable size. In: Li, G., Li, Z., Idreos, S., Srivastava, D. (eds.) SIGMOD ’21: International Conference on Management of Data, Virtual Event, ...

    2021

  12. [20]

    IEEE Trans

    Trimponias, G., Bartolini, I., Papadias, D., Yang, Y.: Skyline processing on dis- tributed vertical decompositions. IEEE Trans. Knowl. Data Eng. 25(4), 850–862 (2013) https://doi.org/10.1109/TKDE.2011.266

    2013 doi

  13. [21]

    In: Mendelzon, A.O., Paredaens, J

    Fagin, R.: Fuzzy queries in multimedia database systems. In: Mendelzon, A.O., Paredaens, J. (eds.) Proceedings of the Seventeenth ACM SIGACT-SIGMOD- SIGART Symposium on Principles of Database Systems, June 1-3, 1998, Seattle, Washington, USA, pp. 1–10 (1998). https://doi.org/1...

    1998

  14. [22]

    IEEE Trans

    Cui, B., Chen, L., Xu, L., Lu, H., Song, G., Xu, Q.: Efficient skyline computation in structured peer-to-peer systems. IEEE Trans. Knowl. Data Eng. 21(7), 1059– 1072 (2009) https://doi.org/10.1109/TKDE.2008.235

    2009 doi

  15. [23]

    In: Antonellis, V.D., Diamantini, C., Tiberio, P

    Bartolini, I., Ciaccia, P., Patella, M.: Distributed aggregation strategies for prefer- ence queries. In: Antonellis, V.D., Diamantini, C., Tiberio, P. (eds.) Proceedings of the Fourteenth Italian Symposium on Advanced Database Systems, SEBD 2006, Portonovo (Ancona), Italy, 18...

    2006

  16. [24]

    Ciaccia, P., Martinenghi, D.: Directional Queries: Making Top-k Queries More Effective in Discovering Relevant Results. Proc. ACM Manag. Data 2(6) (2024) https://doi.org/10.1145/3698807 18

    2024 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.