Ajar

Joglekar, Manas; Puttagunta, Rohan; Ré, Christopher

doi:10.1145/2902251.2902293

Cited by 28 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Join-Aggregate Queries. To study join-aggregate queries, we consider annotated relations [13,19]. Let (R, ⊕, ⊗) be a commutative semi-ring.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The Yannakakis framework can be slightly adapted for computing acyclic join-aggregate queries [28,19].…”

Section: Previous Upper Boundsmentioning

confidence: 99%

“…We study the class of join-aggregate queries defined over commutative semi-rings, which has wide applications in data analytical tasks. The Yannakakis framework proposed in 1981 is still the state-of-the-art approach for tackling acyclic join-aggregate queries [28,19]. A few fundamental questions remain after 40 years: What is the minimum number of semi-ring operations required for computing an acyclic join-aggregate query?…”

Section: Introductionmentioning

confidence: 99%

“…

The classic Yannakakis framework proposed in 1981 is still the state-of-the-art approach for tackling acyclic join-aggregate queries [28,19] defined over commutative semi-rings. It has been shown that the time complexity of the Yannakakis framework is O(N + OUT) for any free-connex join-aggregate query, where N is the input size of the database and OUT is the output size of the query result.

…”

mentioning

confidence: 99%

See 3 more Smart Citations

Instance and Output Optimal Parallel Algorithms for Acyclic Joins

2019

Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

View full text Add to dashboard Cite

Massively parallel join algorithms have received much attention in recent years, while most prior work has focused on worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on hard instances having very large output sizes, which rarely appear in practice. A stronger notion of optimality is output-optimal, which requires an algorithm to be optimal within the class of all instances sharing the same input and output size. An even stronger optimality is instance-optimal, i.e., the algorithm is optimal on every single instance, but this may not always be achievable.In the traditional RAM model of computation, the classical Yannakakis algorithm is instanceoptimal on any acyclic join. But in the massively parallel computation (MPC) model, the situation becomes much more complicated. We first show that for the class of r-hierarchical joins, instance-optimality can still be achieved in the MPC model. Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ( IN p + √ IN·OUT p ), where IN, OUT are the input and output sizes of the join, and p is the number of servers in the MPC model. This improves the MPC version of the Yannakakis algorithm by an O( OUT IN ) factor. Furthermore, we show that this is output-optimal when OUT = O(p · IN), for every acyclic but non-r-hierarchical join. Finally, we give the first output-sensitive lower bound for the triangle join in the MPC model, showing that it is inherently more difficult than acyclic joins. other servers, receives messages from other servers, and then does some local computation. The complexity of the algorithm is measured by the number of rounds and the load, denoted as L, which is the maximum message size received by any server in any round., all problems can be solved trivially in one round by simply sending all data to one server. Initial efforts were mostly spent on what can be done in a single round of computation [3,26,7,8,24,26], but recently, more interest has been given to multi-round (but still a constant) algorithms [2,22,24], since new main memory based systems, such as Spark and Flink, have much lower overhead per round than previous generations like Hadoop.The MPC model can be considered as a simplified version of the BSP model [32], but it has enjoyed more popularity in recent years. This is mostly because the BSP model takes too many measures into consideration, such as communication costs, local computation time, memory consumption, etc. The MPC model unifies all these costs with one parameter L, which makes the model much simpler. Meanwhile, although L is defined as the maximum incoming message size of a server, it is also closely related with the local computation time and memory consumption, which are both increasing functions of L. Thus, L serves as a good surrogate of these other cost measures. This is also why the MPC model does not limit the outgoing message size of a server, which is less relevant to other costs.All our algorithms work under the mild assumption IN ≥ p 1+ where > 0 is any sma...

show abstract

“…Join-Aggregate Queries. To study join-aggregate queries, we consider annotated relations [13,19]. Let (R, ⊕, ⊗) be a commutative semi-ring.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The Yannakakis framework can be slightly adapted for computing acyclic join-aggregate queries [28,19].…”

Section: Previous Upper Boundsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…

…”

mentioning

confidence: 99%

See 2 more Smart Citations

Instance and Output Optimal Parallel Algorithms for Acyclic Joins

2019

Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

View full text Add to dashboard Cite

show abstract

“…Related work. Generalizations of the MPF framework such as functional aggregate queries (FAQ) and aggregations and joins over annotated relations (AJAR), which use more than one aggregation operation, have been discussed by Khamis, Ngo, and Rudra (2016) and Joglekar, Puttagunta, and Ré (2016), respectively. Our selective semiring extension can be used also with selective aggregation operations in these frameworks.…”

Section: Introductionmentioning

confidence: 99%

Model Counting and Sampling via Semiring Extensions

Goral,

Giesen,

Blacher

et al. 2024

AAAI

View full text Add to dashboard Cite

Many decision and optimization problems have natural extensions as counting problems. The best known example is the Boolean satisfiability problem (SAT), where we want to count the satisfying assignments of truth values to the variables, which is known as the #SAT problem. Likewise, for discrete optimization problems, we want to count the states on which the objective function attains the optimal value. Both SAT and discrete optimization can be formulated as selective marginalize a product function (MPF) queries. Here, we show how general selective MPF queries can be extended for model counting. MPF queries are encoded as tensor hypernetworks over suitable semirings that can be solved by generic tensor hypernetwork contraction algorithms. Our model counting extension is again an MPF query, on an extended semiring, that can be solved by the same contraction algorithms. Model counting is required for uniform model sampling. We show how the counting extension can be further extended for model sampling by constructing yet another semiring. We have implemented the model counting and sampling extensions. Experiments show that our generic approach is competitive with the state of the art in model counting and model sampling.

show abstract

On Matrices and K-Relations

Brijder

Gyssens

Bussche

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We show that the matrix query language MATLANG corresponds to a natural fragment of the positive relational algebra on K-relations. The fragment is defined by introducing a composition operator and restricting K-relation arities to two. We then proceed to show that MATLANG can express all matrix queries expressible in the positive relational algebra on K-relations, when intermediate arities are restricted to three. Thus we offer an analogue, in a model with numerical data, to the situation in classical logic, where the algebra of binary relations is equivalent to first-order logic with three variables.

show abstract

Ajar

Cited by 28 publications

References 25 publications

Instance and Output Optimal Parallel Algorithms for Acyclic Joins

Instance and Output Optimal Parallel Algorithms for Acyclic Joins

Model Counting and Sampling via Semiring Extensions

On Matrices and K-Relations

Contact Info

Product

Resources

About