7 Search Results for "Abo Khamis, Mahmoud"

Document
DOI: 10.4230/LIPIcs.ICDT.2024.11
On the Convergence Rate of Linear Datalog ^∘ over Stable Semirings

Authors: Sungjin Im, Benjamin Moseley, Hung Ngo, and Kirk Pruhs

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

Abstract
Datalog^∘ is an extension of Datalog, where instead of a program being a collection of union of conjunctive queries over the standard Boolean semiring, a program may now be a collection of sum-product queries over an arbitrary commutative partially ordered pre-semiring. Datalog^∘ is more powerful than Datalog in that its additional algebraic structure alows for supporting recursion with aggregation. At the same time, Datalog^∘ retains the syntactic and semantic simplicity of Datalog: Datalog^∘ has declarative least fixpoint semantics. The least fixpoint can be found via the naïve evaluation algorithm that repeatedly applies the immediate consequence operator until no further change is possible. It was shown in [Mahmoud Abo Khamis et al., 2022] that, when the underlying semiring is p-stable, then the naïve evaluation of any Datalog^∘ program over the semiring converges in a finite number of steps. However, the upper bounds on the rate of convergence were exponential in the number n of ground IDB atoms. This paper establishes polynomial upper bounds on the convergence rate of the naïve algorithm on linear Datalog^∘ programs, which is quite common in practice. In particular, the main result of this paper is that the convergence rate of linear Datalog^∘ programs under any p-stable semiring is O(pn³). Furthermore, we show a matching lower bound by constructing a p-stable semiring and a linear Datalog^∘ program that requires Ω(pn³) iterations for the naïve iteration algorithm to converge. Next, we study the convergence rate in terms of the number of elements in the semiring for linear Datalog^∘ programs. When L is the number of elements, the convergence rate is bounded by O(pn log L). This significantly improves the convergence rate for small L. We show a nearly matching lower bound as well.
Cite as

Sungjin Im, Benjamin Moseley, Hung Ngo, and Kirk Pruhs. On the Convergence Rate of Linear Datalog ^∘ over Stable Semirings. In 27th International Conference on Database Theory (ICDT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 290, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{im_et_al:LIPIcs.ICDT.2024.11,
  author =	{Im, Sungjin and Moseley, Benjamin and Ngo, Hung and Pruhs, Kirk},
  title =	{{On the Convergence Rate of Linear Datalog ^∘ over Stable Semirings}},
  booktitle =	{27th International Conference on Database Theory (ICDT 2024)},
  pages =	{11:1--11:20},
  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.2024.11},
  URN =		{urn:nbn:de:0030-drops-197939},
  doi =		{10.4230/LIPIcs.ICDT.2024.11},
  annote =	{Keywords: Datalog, convergence rate, semiring}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2024.19
Information Inequality Problem over Set Functions

Authors: Miika Hannula

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

Abstract
Information inequalities appear in many database applications such as query output size bounds, query containment, and implication between data dependencies. Recently Khamis et al. [Mahmoud Abo Khamis et al., 2020] proposed to study the algorithmic aspects of information inequalities, including the information inequality problem: decide whether a linear inequality over entropies of random variables is valid. While the decidability of this problem is a major open question, applications often involve only inequalities that adhere to specific syntactic forms linked to useful semantic invariance properties. This paper studies the information inequality problem in different syntactic and semantic scenarios that arise from database applications. Focusing on the boundary between tractability and intractability, we show that the information inequality problem is coNP-complete if restricted to normal polymatroids, and in polynomial time if relaxed to monotone functions. We also examine syntactic restrictions related to query output size bounds, and provide an alternative proof, through monotone functions, for the polynomial-time computability of the entropic bound over simple sets of degree constraints.
Cite as

Miika Hannula. Information Inequality Problem over Set Functions. In 27th International Conference on Database Theory (ICDT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 290, pp. 19:1-19:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hannula:LIPIcs.ICDT.2024.19,
  author =	{Hannula, Miika},
  title =	{{Information Inequality Problem over Set Functions}},
  booktitle =	{27th International Conference on Database Theory (ICDT 2024)},
  pages =	{19:1--19:20},
  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.2024.19},
  URN =		{urn:nbn:de:0030-drops-198011},
  doi =		{10.4230/LIPIcs.ICDT.2024.19},
  annote =	{Keywords: entropy, information theory, worst-case output size, computational complexity}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.ICDT.2022.1
On an Information Theoretic Approach to Cardinality Estimation (Invited Talk)

Authors: Hung Q. Ngo

Published in: LIPIcs, Volume 220, 25th International Conference on Database Theory (ICDT 2022)

Abstract
This article is a companion to an invited talk at ICDT'2022 with the same title. Cardinality estimation is among the most important problems in query optimization. It is well-documented that, when query plans go haywire, in most cases one can trace the root cause to the cardinality estimator being far off. In particular, traditional cardinality estimation based on selectivity estimation may sometimes under-estimate cardinalities by orders of magnitudes, because the independence or the uniformity assumptions do not typically hold. This talk outlines an approach to cardinality estimation that is "model-free" from a statistical stand-point. Being model-free means the approach tries to avoid making any distributional assumptions. Our approach is information-theoretic, and generalizes recent results on worst-case output size bounds of queries, allowing the estimator to take into account histogram information from the input relations. The estimator turns out to be the objective of a maximization problem subject to concave constraints, over an exponential number of variables. We then explain how the estimator can be computed in polynomial time for some fragment of these constraints. Overall, the talk introduces a new direction to address the classic problem of cardinality estimation that is designed to circumvent some of the pitfalls of selectivity-based estimation. We will also explain connections to other fundamental problems in information theory and database theory regarding information inequalities. The talk is based on (published and unpublished) joint works with Mahmoud Abo Khamis, Sungjin Im, Hossein Keshavarz, Phokion Kolaitis, Ben Moseley, XuanLong Nguyen, Kirk Pruhs, Dan Suciu, and Alireza Samadian Zakaria
Cite as

Hung Q. Ngo. On an Information Theoretic Approach to Cardinality Estimation (Invited Talk). In 25th International Conference on Database Theory (ICDT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 220, pp. 1:1-1:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{ngo:LIPIcs.ICDT.2022.1,
  author =	{Ngo, Hung Q.},
  title =	{{On an Information Theoretic Approach to Cardinality Estimation}},
  booktitle =	{25th International Conference on Database Theory (ICDT 2022)},
  pages =	{1:1--1:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-223-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{220},
  editor =	{Olteanu, Dan and Vortmeier, Nils},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2022.1},
  URN =		{urn:nbn:de:0030-drops-158750},
  doi =		{10.4230/LIPIcs.ICDT.2022.1},
  annote =	{Keywords: Cardinality Estimation, Information Theory, Polymatroid Bound, Worst-case Optimal Join}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2021.6
An Approximation Algorithm for the Matrix Tree Multiplication Problem

Authors: Mahmoud Abo-Khamis, Ryan Curtin, Sungjin Im, Benjamin Moseley, Hung Ngo, Kirk Pruhs, and Alireza Samadian

Published in: LIPIcs, Volume 202, 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021)

Abstract
We consider the Matrix Tree Multiplication problem. This problem is a generalization of the classic Matrix Chain Multiplication problem covered in the dynamic programming chapter of many introductory algorithms textbooks. An instance of the Matrix Tree Multiplication problem consists of a rooted tree with a matrix associated with each edge. The output is, for each leaf in the tree, the product of the matrices on the chain/path from the root to that leaf. Matrix multiplications that are shared between various chains need only be computed once, potentially being shared between different root to leaf chains. Algorithms are evaluated by the number of scalar multiplications performed. Our main result is a linear time algorithm for which the number of scalar multiplications performed is at most 15 times the optimal number of scalar multiplications.
Cite as

Mahmoud Abo-Khamis, Ryan Curtin, Sungjin Im, Benjamin Moseley, Hung Ngo, Kirk Pruhs, and Alireza Samadian. An Approximation Algorithm for the Matrix Tree Multiplication Problem. In 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 202, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{abokhamis_et_al:LIPIcs.MFCS.2021.6,
  author =	{Abo-Khamis, Mahmoud and Curtin, Ryan and Im, Sungjin and Moseley, Benjamin and Ngo, Hung and Pruhs, Kirk and Samadian, Alireza},
  title =	{{An Approximation Algorithm for the Matrix Tree Multiplication Problem}},
  booktitle =	{46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021)},
  pages =	{6:1--6:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-201-3},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{202},
  editor =	{Bonchi, Filippo and Puglisi, Simon J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2021.6},
  URN =		{urn:nbn:de:0030-drops-144464},
  doi =		{10.4230/LIPIcs.MFCS.2021.6},
  annote =	{Keywords: Matrix Multiplication, Approximation Algorithm}
}
Document
Track B: Automata, Logic, Semantics, and Theory of Programming
DOI: 10.4230/LIPIcs.ICALP.2020.106
Decision Problems in Information Theory

Authors: Mahmoud Abo Khamis, Phokion G. Kolaitis, Hung Q. Ngo, and Dan Suciu

Published in: LIPIcs, Volume 168, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)

Abstract
Constraints on entropies are considered to be the laws of information theory. Even though the pursuit of their discovery has been a central theme of research in information theory, the algorithmic aspects of constraints on entropies remain largely unexplored. Here, we initiate an investigation of decision problems about constraints on entropies by placing several different such problems into levels of the arithmetical hierarchy. We establish the following results on checking the validity over all almost-entropic functions: first, validity of a Boolean information constraint arising from a monotone Boolean formula is co-recursively enumerable; second, validity of "tight" conditional information constraints is in Π⁰₃. Furthermore, under some restrictions, validity of conditional information constraints "with slack" is in Σ⁰₂, and validity of information inequality constraints involving max is Turing equivalent to validity of information inequality constraints (with no max involved). We also prove that the classical implication problem for conditional independence statements is co-recursively enumerable.
Cite as

Mahmoud Abo Khamis, Phokion G. Kolaitis, Hung Q. Ngo, and Dan Suciu. Decision Problems in Information Theory. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 106:1-106:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{abokhamis_et_al:LIPIcs.ICALP.2020.106,
  author =	{Abo Khamis, Mahmoud and Kolaitis, Phokion G. and Ngo, Hung Q. and Suciu, Dan},
  title =	{{Decision Problems in Information Theory}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{106:1--106:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Czumaj, Artur and Dawar, Anuj and Merelli, Emanuela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2020.106},
  URN =		{urn:nbn:de:0030-drops-125137},
  doi =		{10.4230/LIPIcs.ICALP.2020.106},
  annote =	{Keywords: Information theory, decision problems, arithmetical hierarchy, entropic functions}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.ICDT.2019.1
Learning Models over Relational Databases (Invited Talk)

Authors: Dan Olteanu

Published in: LIPIcs, Volume 127, 22nd International Conference on Database Theory (ICDT 2019)

Abstract
In this talk, I will make the case for a first-principles approach to machine learning over relational databases that exploits recent development in database systems and theory. The input to learning classification and regression models is defined by feature extraction queries over relational databases. The mainstream approach to learning over relational data is to materialize the training dataset, export it out of the database, and then learn over it using statistical software packages. These three steps are expensive and unnecessary. Instead, one can cast the machine learning problem as a database problem by decomposing the learning task into a batch of aggregates over the feature extraction query and by computing this batch over the input database. The performance of this database-centric approach benefits tremendously from structural properties of the relational data and of the feature extraction query; such properties may be algebraic (semi-ring), combinatorial (hypertree width), or statistical (sampling). It also benefits from database systems techniques such as factorized query evaluation and query compilation. For a variety of models, including factorization machines, decision trees, and support vector machines, this approach may come with lower computational complexity than the materialization of the training dataset used by the mainstream approach. Recent results show that this translates to several orders-of-magnitude speed-up over state-of-the-art systems such as TensorFlow, R, Scikit-learn, and mlpack. While these initial results are promising, there is much more awaiting to be discovered.
Cite as

Dan Olteanu. Learning Models over Relational Databases (Invited Talk). In 22nd International Conference on Database Theory (ICDT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 127, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{olteanu:LIPIcs.ICDT.2019.1,
  author =	{Olteanu, Dan},
  title =	{{Learning Models over Relational Databases}},
  booktitle =	{22nd International Conference on Database Theory (ICDT 2019)},
  pages =	{1:1--1:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-101-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{127},
  editor =	{Barcelo, Pablo and Calautti, Marco},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2019.1},
  URN =		{urn:nbn:de:0030-drops-103034},
  doi =		{10.4230/LIPIcs.ICDT.2019.1},
  annote =	{Keywords: In-database analytics, Data complexity, Feature extraction queries, Database dependencies, Model reparameterization}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2019.21
Boolean Tensor Decomposition for Conjunctive Queries with Negation

Authors: Mahmoud Abo Khamis, Hung Q. Ngo, Dan Olteanu, and Dan Suciu

Published in: LIPIcs, Volume 127, 22nd International Conference on Database Theory (ICDT 2019)

Abstract
We propose an approach for answering conjunctive queries with negation, where the negated relations have bounded degree. Its data complexity matches that of the InsideOut and PANDA algorithms for the positive subquery of the input query and is expressed in terms of the fractional hypertree width and the submodular width respectively. Its query complexity depends on the structure of the conjunction of negated relations; in general it is exponential in the number of join variables occurring in negated relations yet it becomes polynomial for several classes of queries. This approach relies on several contributions. We show how to rewrite queries with negation on bounded-degree relations into equivalent conjunctive queries with not-all-equal (NAE) predicates, which are a multi-dimensional analog of disequality (!=). We then generalize the known color-coding technique to conjunctions of NAE predicates and explain it via a Boolean tensor decomposition of conjunctions of NAE predicates. This decomposition can be achieved via a probabilistic construction that can be derandomized efficiently.
Cite as

Mahmoud Abo Khamis, Hung Q. Ngo, Dan Olteanu, and Dan Suciu. Boolean Tensor Decomposition for Conjunctive Queries with Negation. In 22nd International Conference on Database Theory (ICDT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 127, pp. 21:1-21:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{abokhamis_et_al:LIPIcs.ICDT.2019.21,
  author =	{Abo Khamis, Mahmoud and Ngo, Hung Q. and Olteanu, Dan and Suciu, Dan},
  title =	{{Boolean Tensor Decomposition for Conjunctive Queries with Negation}},
  booktitle =	{22nd International Conference on Database Theory (ICDT 2019)},
  pages =	{21:1--21:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-101-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{127},
  editor =	{Barcelo, Pablo and Calautti, Marco},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2019.21},
  URN =		{urn:nbn:de:0030-drops-103236},
  doi =		{10.4230/LIPIcs.ICDT.2019.21},
  annote =	{Keywords: color-coding, combined complexity, negation, query evaluation}
}
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