Functional Aggregate Query (FAQ): Questions Asked Frequently
RelationalAI
02 January 2016
less than a minute read
FAQ: Questions Asked Frequently
This paper developed theory and algorithms for semantic query optimization for queries expressible in the "functional aggregate queries" format, which includes a vast number of query classes from database, constraint satisfaction, to machine learning and AI.
Authors: Mahmoud Abo Khamis, Hung Q. Ngo, Atri Rudra. 2016.
In Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS ‘16). (Best Paper Award, Invited to the Journal of the ACM)
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. We then present a simple algorithm called InsideOut to solve this general problem. InsideOut is a variation of the traditional dynamic programming approach for constraint programming based on variable elimination. Our variation adds a couple of simple twists to basic variable elimination in order to deal with the generality of FAQ, to take full advantage of Grohe and Marx’s fractional edge cover framework, and of the analysis of recent worst-case optimal relational join algorithms.
Related Posts
Building a Named Entity Recognition Model for the Legal Domain
We defined named entity recognition (NER) in the legal domain and presented our approach towards generating ground truth data. In what follows, we go over the state-of-the-art in the NER domain and elaborate on the experiments we ran and the lessons we learned.
Read More
Named Entity Recognition in the Legal Domain
Named entity recognition is a difficult challenge to solve, particularly in the legal domain. Extracting ground truth labels from long, hierarchical documents is often slow and prone to error. RelationalAI proposes a new, scalable algorithm based on the principles of data-centric AI, designed to meet this challenge and generate high-quality annotations with minimal supervision.
Read More
Deep Learning with Relations, NeurIPS 2021
Molham shares some history of relational databases, trends in modern cloud-native database systems, and the innovations pioneered at RelationalAI to bring deep learning with relations from idea to reality.
Read More