Query Languages with Generalized Quantifiers

This project is supported by NSF under the NSF CAREER award IIS-0347555.

It has been argued in the literature that current commercial query languages (SQL) do not have enough power and flexibility to express, in a easy and convenient way, many queries that database systems are being asked to solve nowadays. Complex new applications like Decision Support, Data Mining and E-commerce ask the database to solve queries involving complex relationships among sets of data items in the database.
In this project, we define a Query Language with Generalized Quantifiers (QLGQ), show how it can state complex questions in a simpler form than SQL, and study its implementation and optimization. The ultimate goal is to gain understanding of the trade-offs between language design and optimization. We expect that the study of QLGQ optimization will yield lessons that will help improve optimization in SQL, and suggest improvements to the language design.

RESEARCH TOPICS PUBLICATIONS

Please come back soon! Results and papers will be posted as research progresses

BACKGROUND REFERENCES

For those interested, some classical references on Generalized Quantifiers, including background material in logic and linguistics: