3 Search Results for "Hu, Xiao"


Document
Finding Smallest Witnesses for Conjunctive Queries

Authors: Xiao Hu and Stavros Sintos

Published in: LIPIcs, Volume 290, 27th International Conference on Database Theory (ICDT 2024)


Abstract
A witness is a sub-database that preserves the query results of the original database but of much smaller size. It has wide applications in query rewriting and debugging, query explanation, IoT analytics, multi-layer network routing, etc. In this paper, we study the smallest witness problem (SWP) for the class of conjunctive queries (CQs) without self-joins. We first establish the dichotomy that SWP for a CQ can be computed in polynomial time if and only if it has head-cluster property, unless P = NP. We next turn to the approximated version by relaxing the size of a witness from being minimum. We surprisingly find that the head-domination property - that has been identified for the deletion propagation problem [Kimelfeld et al., 2012] - can also precisely capture the hardness of the approximated smallest witness problem. In polynomial time, SWP for any CQ with head-domination property can be approximated within a constant factor, while SWP for any CQ without such a property cannot be approximated within a logarithmic factor, unless P = NP. We further explore efficient approximation algorithms for CQs without head-domination property: (1) we show a trivial algorithm which achieves a polynomially large approximation ratio for general CQs; (2) for any CQ with only one non-output attribute, such as star CQs, we show a greedy algorithm with a logarithmic approximation ratio; (3) for line CQs, which contain at least two non-output attributes, we relate SWP problem to the directed steiner forest problem, whose algorithms can be applied to line CQs directly. Meanwhile, we establish a much higher lower bound, exponentially larger than the logarithmic lower bound obtained above. It remains open to close the gap between the lower and upper bound of the approximated SWP for CQs without head-domination property.

Cite as

Xiao Hu and Stavros Sintos. Finding Smallest Witnesses for Conjunctive Queries. In 27th International Conference on Database Theory (ICDT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 290, pp. 24:1-24:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{hu_et_al:LIPIcs.ICDT.2024.24,
  author =	{Hu, Xiao and Sintos, Stavros},
  title =	{{Finding Smallest Witnesses for Conjunctive Queries}},
  booktitle =	{27th International Conference on Database Theory (ICDT 2024)},
  pages =	{24:1--24:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-312-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{290},
  editor =	{Cormode, Graham and Shekelyan, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2024.24},
  URN =		{urn:nbn:de:0030-drops-198066},
  doi =		{10.4230/LIPIcs.ICDT.2024.24},
  annote =	{Keywords: conjunctive query, smallest witness, head-cluster, head-domination}
}
Document
Track A: Algorithms, Complexity and Games
Dynamic Enumeration of Similarity Joins

Authors: Pankaj K. Agarwal, Xiao Hu, Stavros Sintos, and Jun Yang

Published in: LIPIcs, Volume 198, 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)


Abstract
This paper considers enumerating answers to similarity-join queries under dynamic updates: Given two sets of n points A,B in ℝ^d, a metric ϕ(⋅), and a distance threshold r > 0, report all pairs of points (a, b) ∈ A × B with ϕ(a,b) ≤ r. Our goal is to store A,B into a dynamic data structure that, whenever asked, can enumerate all result pairs with worst-case delay guarantee, i.e., the time between enumerating two consecutive pairs is bounded. Furthermore, the data structure can be efficiently updated when a point is inserted into or deleted from A or B. We propose several efficient data structures for answering similarity-join queries in low dimension. For exact enumeration of similarity join, we present near-linear-size data structures for 𝓁₁, 𝓁_∞ metrics with log^{O(1)} n update time and delay. We show that such a data structure is not feasible for the 𝓁₂ metric for d ≥ 4. For approximate enumeration of similarity join, where the distance threshold is a soft constraint, we obtain a unified linear-size data structure for 𝓁_p metric, with log^{O(1)} n delay and update time. In high dimensions, we present an efficient data structure with worst-case delay-guarantee using locality sensitive hashing (LSH).

Cite as

Pankaj K. Agarwal, Xiao Hu, Stavros Sintos, and Jun Yang. Dynamic Enumeration of Similarity Joins. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


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@InProceedings{agarwal_et_al:LIPIcs.ICALP.2021.11,
  author =	{Agarwal, Pankaj K. and Hu, Xiao and Sintos, Stavros and Yang, Jun},
  title =	{{Dynamic Enumeration of Similarity Joins}},
  booktitle =	{48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)},
  pages =	{11:1--11:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-195-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{198},
  editor =	{Bansal, Nikhil and Merelli, Emanuela and Worrell, James},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2021.11},
  URN =		{urn:nbn:de:0030-drops-140803},
  doi =		{10.4230/LIPIcs.ICALP.2021.11},
  annote =	{Keywords: dynamic enumeration, similarity joins, worst-case delay guarantee}
}
Document
Enumeration Algorithms for Conjunctive Queries with Projection

Authors: Shaleen Deep, Xiao Hu, and Paraschos Koutris

Published in: LIPIcs, Volume 186, 24th International Conference on Database Theory (ICDT 2021)


Abstract
We investigate the enumeration of query results for an important subset of CQs with projections, namely star and path queries. The task is to design data structures and algorithms that allow for efficient enumeration with delay guarantees after a preprocessing phase. Our main contribution is a series of results based on the idea of interleaving precomputed output with further join processing to maintain delay guarantees, which maybe of independent interest. In particular, we design combinatorial algorithms that provide instance-specific delay guarantees in linear preprocessing time. These algorithms improve upon the currently best known results. Further, we show how existing results can be improved upon by using fast matrix multiplication. We also present {new} results involving tradeoff between preprocessing time and delay guarantees for enumeration of path queries that contain projections. CQs with projection where the join attribute is projected away is equivalent to boolean matrix multiplication. Our results can therefore be also interpreted as sparse, output-sensitive matrix multiplication with delay guarantees.

Cite as

Shaleen Deep, Xiao Hu, and Paraschos Koutris. Enumeration Algorithms for Conjunctive Queries with Projection. In 24th International Conference on Database Theory (ICDT 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 186, pp. 14:1-14:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Copy BibTex To Clipboard

@InProceedings{deep_et_al:LIPIcs.ICDT.2021.14,
  author =	{Deep, Shaleen and Hu, Xiao and Koutris, Paraschos},
  title =	{{Enumeration Algorithms for Conjunctive Queries with Projection}},
  booktitle =	{24th International Conference on Database Theory (ICDT 2021)},
  pages =	{14:1--14:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-179-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{186},
  editor =	{Yi, Ke and Wei, Zhewei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2021.14},
  URN =		{urn:nbn:de:0030-drops-137229},
  doi =		{10.4230/LIPIcs.ICDT.2021.14},
  annote =	{Keywords: Query result enumeration, joins, ranking}
}
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