Finding Top-k Min-Cost Connected Trees in Databases

By Bolin Ding, Jeffrey Xu Yu, Shan Wang, Lu Qin, Xiao Zhang, Xuemin Lin. IEEE International Conference on Data Engineering, 2007

378 citations recorded by the source corpus.

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Effective Community Search for Large Attributed Graphs (2016, background): “Given an attributed graph G and a set Q of keywords, graph keyword search solutions output a tree structure, whose nodes are vertices of G, and the union of these vertices’ keyword sets is a superset of Q [3, 6, 16].”
Spark: top-k keyword query in relational databases (2007, neutral): “The rationale for choosing this architecture over other alternatives (such as graph-based method [3, 18, 9]) is that (a) Relational databases are widely used in enterprises and over the Internet, and a sizeable percentage of information stored inside RDBMSs is text; (b) it is easier to exploit many…”
Keyword Search in Relational Databases: A Survey (2010, methodology): “A commonly used weighting scheme [4, 10] is as follows.”
BLINKS: ranked keyword searches on graphs (2007, methodology): “[6] proposes a method based on dynamic programming that targets the case where the number of keywords in the query is small.”
A survey of community search over big graphs (2019, methodology): “graph, which contains a set of query keywords, from a large graph G. Earlier studies often output a tree structure. In [21], Bhalotia et al. developed a backward algorithm for nding Steiner trees. In [50], Ding et al. proposed a dynamic programming algorithm nding Steiner trees. In [79], Golenberg e”