Comprehensive Survey of Recursive Query Processing and Optimization Techniques using Datalog
In recent years, we have witnessed a revival of the use of recursive queries in a variety of emerging application domains such as data integration and exchange, information extraction, networking, and program analysis. A popular language used for expressing these queries is Datalog.
Functional Aggregate Query (FAQ): Questions Asked Frequently
We define and study the Functional Aggregate Query (FAQ) problem, which encompasses many frequently asked questions in constraint satisfaction, databases, matrix operations, probabilistic graphical models and logic. This is our main conceptual contribution.
Design and Implementation of the LogicBlox System
The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a “self-service” model.
Join Processing for Graph Patterns: An Old Dog with New Tricks
Join optimization has been dominated by Selinger-style, pairwise optimizers for decades. But, Selinger-style algorithms are asymptotically suboptimal for applications in graphic analytics. This suboptimality is one of the reasons that many have advocated supplementing relational engines with specialized graph processing engines.
Leapfrog Triejoin: A Simple, Worst-Case Optimal Join Algorithm
In 2012, Ngo, Porat, R«e and Rudra (henceforth NPRR) devised a join algorithm with worst-case running time proportional to the AGM bound [8]. Our commercial database system LogicBlox employs a novel join algorithm, leapfrog triejoin, which compared conspicuously well to the NPRR algorithm in preliminary benchmarks.
Hybrid Context-Sensitivity for Points-To Analysis
Context sensitive points-to analysis is valuable for achieving high precision with good performance.The standard flavors of context sensitivity are call site-sensitivity (kCFA) and object-sensitivity. Combining both flavors of context-sensitivity increases precision but at an infeasibly high cost.