Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluating rank joins with optimal cost
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Ranking continuous probabilistic datasets
Proceedings of the VLDB Endowment
Building ranked mashups of unstructured sources with uncertain information
Proceedings of the VLDB Endowment
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At the core of the query processing engine of a search computing system are operators that retrieve, filter, join and aggregate results from theseWeb services. The main goal is to deliver relevant and multi-domain answers to user queries. In these scenarios, users usually expect a ranked list of relevant answers in contrast to the full answer set. Hence, ranking query results in the presence of uncertainty is a fundamental query processing challenge in search computing environments. Rank-join is a basic relational operator that reports the top-k join results as soon as possible, avoiding the expensive materialize-then-sort approach. Due to the early-out and pipelined nature of rank-join, it acts as one of the major building blocks in compiling execution plans for multi-domain queries (also knows as liquid queries). In this chapter, we discuss the implication of data uncertainty on the semantics and implementation of rank-join operators, and we survey some of the recent techniques to address these challenges.