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Documents authored by Yang, Jun

Found 3 Possible Name Variants:

Yang, Jun

Document
Invited Talk
DOI: 10.4230/LIPIcs.ICDT.2024.2
How Database Theory Helps Teach Relational Queries in Database Education (Invited Talk)

Authors: Sudeepa Roy, Amir Gilad, Yihao Hu, Hanze Meng, Zhengjie Miao, Kristin Stephens-Martinez, and Jun Yang

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

Abstract
Data analytics skills have become an indispensable part of any education that seeks to prepare its students for the modern workforce. Essential in this skill set is the ability to work with structured relational data. Relational queries are based on logic and may be declarative in nature, posing new challenges to novices and students. Manual teaching resources being limited and enrollment growing rapidly, automated tools that help students debug queries and explain errors are potential game-changers in database education. We present a suite of tools built on the foundations of database theory that has been used by over 1600 students in database classes at Duke University, showcasing a high-impact application of database theory in database education.
Cite as

Sudeepa Roy, Amir Gilad, Yihao Hu, Hanze Meng, Zhengjie Miao, Kristin Stephens-Martinez, and Jun Yang. How Database Theory Helps Teach Relational Queries in Database Education (Invited Talk). In 27th International Conference on Database Theory (ICDT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 290, pp. 2:1-2:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{roy_et_al:LIPIcs.ICDT.2024.2,
  author =	{Roy, Sudeepa and Gilad, Amir and Hu, Yihao and Meng, Hanze and Miao, Zhengjie and Stephens-Martinez, Kristin and Yang, Jun},
  title =	{{How Database Theory Helps Teach Relational Queries in Database Education}},
  booktitle =	{27th International Conference on Database Theory (ICDT 2024)},
  pages =	{2:1--2:9},
  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.2024.2},
  URN =		{urn:nbn:de:0030-drops-197841},
  doi =		{10.4230/LIPIcs.ICDT.2024.2},
  annote =	{Keywords: Query Debugging, SQL, Relational Algebra, Relational Calculus, Database Education, Boolean Provenance}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2024.18
Computing Data Distribution from Query Selectivities

Authors: Pankaj K. Agarwal, Rahul Raychaudhury, Stavros Sintos, and Jun Yang

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

Abstract
We are given a set 𝒵 = {(R_1,s_1), …, (R_n,s_n)}, where each R_i is a range in ℝ^d, such as rectangle or ball, and s_i ∈ [0,1] denotes its selectivity. The goal is to compute a small-size discrete data distribution 𝒟 = {(q₁,w₁),…, (q_m,w_m)}, where q_j ∈ ℝ^d and w_j ∈ [0,1] for each 1 ≤ j ≤ m, and ∑_{1≤j≤m} w_j = 1, such that 𝒟 is the most consistent with 𝒵, i.e., err_p(𝒟,𝒵) = 1/n ∑_{i = 1}ⁿ |s_i - ∑_{j=1}^m w_j⋅1(q_j ∈ R_i)|^p is minimized. In a database setting, 𝒵 corresponds to a workload of range queries over some table, together with their observed selectivities (i.e., fraction of tuples returned), and 𝒟 can be used as compact model for approximating the data distribution within the table without accessing the underlying contents. In this paper, we obtain both upper and lower bounds for this problem. In particular, we show that the problem of finding the best data distribution from selectivity queries is NP-complete. On the positive side, we describe a Monte Carlo algorithm that constructs, in time O((n+δ^{-d}) δ^{-2} polylog n), a discrete distribution 𝒟̃ of size O(δ^{-2}), such that err_p(𝒟̃,𝒵) ≤ min_𝒟 err_p(𝒟,𝒵)+δ (for p = 1,2,∞) where the minimum is taken over all discrete distributions. We also establish conditional lower bounds, which strongly indicate the infeasibility of relative approximations as well as removal of the exponential dependency on the dimension for additive approximations. This suggests that significant improvements to our algorithm are unlikely.
Cite as

Pankaj K. Agarwal, Rahul Raychaudhury, Stavros Sintos, and Jun Yang. Computing Data Distribution from Query Selectivities. In 27th International Conference on Database Theory (ICDT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 290, pp. 18:1-18:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{agarwal_et_al:LIPIcs.ICDT.2024.18,
  author =	{Agarwal, Pankaj K. and Raychaudhury, Rahul and Sintos, Stavros and Yang, Jun},
  title =	{{Computing Data Distribution from Query Selectivities}},
  booktitle =	{27th International Conference on Database Theory (ICDT 2024)},
  pages =	{18:1--18:20},
  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.2024.18},
  URN =		{urn:nbn:de:0030-drops-198007},
  doi =		{10.4230/LIPIcs.ICDT.2024.18},
  annote =	{Keywords: selectivity queries, discrete distributions, Multiplicative Weights Update, eps-approximation, learnable functions, depth problem, arrangement}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2021.11
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.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}
}

Yang, Jungwoo

Document
DOI: 10.4230/LIPIcs.SWAT.2020.6
Sea-Rise Flooding on Massive Dynamic Terrains

Authors: Lars Arge, Mathias Rav, Morten Revsbæk, Yujin Shin, and Jungwoo Yang

Published in: LIPIcs, Volume 162, 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020)

Abstract
Predicting floods caused by storm surges is a crucial task. Since the rise of ocean water can create floods that extend far onto land, the flood damage can be severe. By developing efficient flood prediction algorithms that use very detailed terrain models and accurate sea-level forecasts, users can plan mitigations such as flood walls and gates to minimize the damage from storm surge flooding. In this paper we present a data structure for predicting floods from dynamic sea-level forecast data on dynamic massive terrains. The forecast data is dynamic in the sense that new forecasts are released several times per day; the terrain is dynamic in the sense that the terrain model may be updated to plan flood mitigations. Since accurate flood risk computations require using very detailed terrain models, and such terrain models can easily exceed the size of the main memory in a regular computer, our data structure is I/O-efficient, that is, it minimizes the number of I/Os (i.e. block transfers) between main memory and disk. For a terrain represented as a raster of N cells, it can be constructed using O(N/B log_M/B N/B) I/Os, it can compute the flood risk in a given small region using O(log_B N) I/Os, and it can handle updating the terrain elevation in a given small region using O(log²_B N) I/Os, where B is the block size and M is the capacity of main memory.
Cite as

Lars Arge, Mathias Rav, Morten Revsbæk, Yujin Shin, and Jungwoo Yang. Sea-Rise Flooding on Massive Dynamic Terrains. In 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 162, pp. 6:1-6:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{arge_et_al:LIPIcs.SWAT.2020.6,
  author =	{Arge, Lars and Rav, Mathias and Revsb{\ae}k, Morten and Shin, Yujin and Yang, Jungwoo},
  title =	{{Sea-Rise Flooding on Massive Dynamic Terrains}},
  booktitle =	{17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020)},
  pages =	{6:1--6:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-150-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{162},
  editor =	{Albers, Susanne},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2020.6},
  URN =		{urn:nbn:de:0030-drops-122539},
  doi =		{10.4230/LIPIcs.SWAT.2020.6},
  annote =	{Keywords: Computational geometry, I/O-algorithms, merge tree, dynamic terrain}
}
Document
DOI: 10.4230/LIPIcs.SOCG.2015.796
Maintaining Contour Trees of Dynamic Terrains

Authors: Pankaj K. Agarwal, Thomas Mølhave, Morten Revsbæk, Issam Safa, Yusu Wang, and Jungwoo Yang

Published in: LIPIcs, Volume 34, 31st International Symposium on Computational Geometry (SoCG 2015)

Abstract
We study the problem of maintaining the contour tree T of a terrain Sigma, represented as a triangulated xy-monotone surface, as the heights of its vertices vary continuously with time. We characterize the combinatorial changes in T and how they relate to topological changes in Sigma. We present a kinetic data structure (KDS) for maintaining T efficiently. It maintains certificates that fail, i.e., an event occurs, only when the heights of two adjacent vertices become equal or two saddle vertices appear on the same contour. Assuming that the heights of two vertices of Sigma become equal only O(1) times and these instances can be computed in O(1) time, the KDS processes O(kappa + n) events, where n is the number of vertices in Sigma and kappa is the number of events at which the combinatorial structure of T changes, and processes each event in O(log n) time. The KDS can be extended to maintain an augmented contour tree and a join/split tree.
Cite as

Pankaj K. Agarwal, Thomas Mølhave, Morten Revsbæk, Issam Safa, Yusu Wang, and Jungwoo Yang. Maintaining Contour Trees of Dynamic Terrains. In 31st International Symposium on Computational Geometry (SoCG 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 34, pp. 796-811, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{agarwal_et_al:LIPIcs.SOCG.2015.796,
  author =	{Agarwal, Pankaj K. and M{\o}lhave, Thomas and Revsb{\ae}k, Morten and Safa, Issam and Wang, Yusu and Yang, Jungwoo},
  title =	{{Maintaining Contour Trees of Dynamic Terrains}},
  booktitle =	{31st International Symposium on Computational Geometry (SoCG 2015)},
  pages =	{796--811},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-83-5},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{34},
  editor =	{Arge, Lars and Pach, J\'{a}nos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SOCG.2015.796},
  URN =		{urn:nbn:de:0030-drops-51406},
  doi =		{10.4230/LIPIcs.SOCG.2015.796},
  annote =	{Keywords: Contour tree, dynamic terrain, kinetic data structure}
}

Yang, Junxing

Document
Invited Paper
DOI: 10.4230/LIPIcs.CONCUR.2016.4
Love Thy Neighbor: V-Formation as a Problem of Model Predictive Control (Invited Paper)

Authors: Junxing Yang, Radu Grosu, Scott A. Smolka, and Ashish Tiwari

Published in: LIPIcs, Volume 59, 27th International Conference on Concurrency Theory (CONCUR 2016)

Abstract
We present a new formulation of the V-formation problem for migrating birds in terms of model predictive control (MPC). In our approach, to drive a collection of birds towards a desired formation, an optimal velocity adjustment (acceleration) is performed at each time-step on each bird's current velocity using a model-based prediction window of $T$ time-steps. We present both centralized and distributed versions of this approach. The optimization criteria we consider are based on fitness metrics of candidate accelerations that birds in a V-formations are known to benefit from, including velocity matching, clear view, and upwash benefit. We validate our MPC-based approach by showing that for a significant majority of simulation runs, the flock succeeds in forming the desired formation. Our results help to better understand the emergent behavior of formation flight, and provide a control strategy for flocks of autonomous aerial vehicles.
Cite as

Junxing Yang, Radu Grosu, Scott A. Smolka, and Ashish Tiwari. Love Thy Neighbor: V-Formation as a Problem of Model Predictive Control (Invited Paper). In 27th International Conference on Concurrency Theory (CONCUR 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 59, pp. 4:1-4:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{yang_et_al:LIPIcs.CONCUR.2016.4,
  author =	{Yang, Junxing and Grosu, Radu and Smolka, Scott A. and Tiwari, Ashish},
  title =	{{Love Thy Neighbor: V-Formation as a Problem of Model Predictive Control}},
  booktitle =	{27th International Conference on Concurrency Theory (CONCUR 2016)},
  pages =	{4:1--4:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-017-0},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{59},
  editor =	{Desharnais, Jos\'{e}e and Jagadeesan, Radha},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2016.4},
  URN =		{urn:nbn:de:0030-drops-61896},
  doi =		{10.4230/LIPIcs.CONCUR.2016.4},
  annote =	{Keywords: bird flocking, v-formation, model predictive control, particle swarm optimization}
}
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