Name
Abstract | ||
|---|---|---|
Modern data analytics applications, such as knowledge graph reasoning and machine learning, typically involve recursion through aggregation. Such computations pose great challenges to both system builders and theoreticians: first, to derive simple yet powerful abstractions for these computations; second, to define and study the semantics for the abstractions; third, to devise optimization techniques for these computations. In recent work we presented a generalization of Datalog called Datalogffi, which addresses these challenges. Datalogffi is a simple abstraction, which allows aggregates to be interleaved with recursion, and retains much of the simplicity and elegance of Datalog. We define its formal semantics based on an algebraic structure called Partially Ordered Pre-Semirings, and illustrate through several examples how Datalogffi can be used for a variety of applications. Finally, we describe a new optimization rule for Datalogffi, called the FGH-rule, then illustrate the FGH-rule on several examples, including a simple magic-set rewriting, generalized semi-naive evaluation, and a bill-of-material example, and briefly discuss the implementation of the FGH-rule and present some experimental validation of its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3552490.3552492 | SIGMOD RECORD |
DocType | Volume | Issue |
Journal | 51 | 2 |
ISSN | Citations | PageRank |
0163-5808 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahmoud Abo Khamis | 1 | 0 | 1.01 |
| Hung Q. Ngo | 2 | 0 | 0.68 |
| Reinhard Pichler | 3 | 0 | 0.68 |
| Dan Suciu | 4 | 9625 | 1349.54 |
| Yisu Remy Wang | 5 | 1 | 1.70 |