UnQL: a query language and algebra for semistructured data based on structural recursion

By Peter Buneman, M. Fernández, Dan Suciu. The VLDB journal, 2000

299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c54bbf3891bea58d4d41f7183e717428ca4bad35

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nate Foster
2. Pradeep Shenoy
3. Raghav Kaushik
4. Andy Schürr
5. Krzysztof Czarnecki
6. Philip Bohannon
7. Ralf Lämmel
8. Ehud Gudes

In Computer Networks and Communications

1. Nate Foster
2. Pradeep Shenoy
3. Raghav Kaushik
4. Philip Bohannon
5. Ralf Lämmel
6. Ehud Gudes

In Signal Processing

1. Raghav Kaushik
2. Krzysztof Czarnecki
3. Anshumali Shrivastava
4. Ping Li

Citations in the corpus

• Bidirectional Transformations: A Cross-Discipline Perspective (2009, methodology): “This work aims at the development of an algebraic framework for bidirectional model transformation by integrating the state-of-the-art technologies on bidirectional tree transformations and the algebraic graph querying language UnQL+ [52, 53], which is an extension of the known UnQL [18].”
• Exploiting local similarity for indexing paths in graph-structured data (2002, methodology): “In each of the many query languages proposed for querying such data [1, 5, 9, 10, 11, 27], the relatively simpleFROM clause of SQL has been replaced by a pattern language[12] built aroundpath expressions .”