A Practically Efficient Algorithm for Generating Answers to Keyword Search over Data Graphs

TL;DR

This paper introduces GTF, an efficient algorithm for keyword search over data graphs that generates all answers by smartly avoiding unnecessary paths, outperforming existing methods in scalability and completeness.

Contribution

The paper presents GTF, a novel algorithm that efficiently enumerates all answer subtrees in data graphs using a freezing technique to improve performance.

Findings

GTF outperforms existing systems in speed and scalability.

GTF can handle large data graphs effectively.

It produces complete answer sets even with many answers required.

Abstract

In keyword search over a data graph, an answer is a non-redundant subtree that contains all the keywords of the query. A naive approach to producing all the answers by increasing height is to generalize Dijkstra's algorithm to enumerating all acyclic paths by increasing weight. The idea of freezing is introduced so that (most) non-shortest paths are generated only if they are actually needed for producing answers. The resulting algorithm for generating subtrees, called GTF, is subtle and its proof of correctness is intricate. Extensive experiments show that GTF outperforms existing systems, even ones that for efficiency's sake are incomplete (i.e., cannot produce all the answers). In particular, GTF is scalable and performs well even on large data graphs and when many answers are needed.