Evaluating top-k queries over Web-accessible databases

By Nicolas Bruno, Luis Gravano, Amélie Marian. Proceedings / International Conference on Data Engineering, 2002

572 citations recorded by the source corpus.

How researchers see this paper

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

RankSQL: query algebra and optimization for relational top-k queries (2005, methodology): “The implementation of μ is straightforward from Example 3 and it is a special case (because it schedules one predicate) of the algorithms (MPro [4], Upper [2]) for scheduling random object accesses in middleware top-k query evaluation.”
LARS: A Location-Aware Recommender System (2012, unknown): “(2) Preference methods such as skylines [22] (and spatial variants [23]) and location-based topk methods [24] require users to expressexplicit preference constraints.”
A survey of top-k query processing techniques in relational database systems (2008, methodology): “The Upper and Pick algorithms [Bruno et al. 2002; Marian et al. 2004] are examples of these methods.”
Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects (2009, background): “Our work is also related to top-k query processing [9,17].”
Spark: top-k keyword query in relational databases (2007, background): “Expensive predicates checking for top-k query has been studied [13], [63]–[65] to support user-defined functions, external predicates, fuzzy joins, etc.”
Efficient IR-Style Keyword Search over Relational Databases (2003, methodology): “The design of the pipelined algorithms that we propose in this paper faces challenges that are related to other top-k work (e.g., [14, 6, 10, 4]).”