Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Evaluating Top-k Queries over Web-Accessible Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Algorithms and applications for answering ranked queries using ranked views
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Adaptive rank-aware query optimization in relational databases
ACM Transactions on Database Systems (TODS)
Depth estimation for ranking query optimization
VLDB '07 Proceedings of the 33rd international conference 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
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Proceedings of the Second ACM International Conference on Web Search and Data Mining
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Multiple approaches to analysing query diversity
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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Joins represent the basic functional operations of complex query plans in a Search Computing system, as discussed in the previous chapter. In this chapter we provide further insight on this matter, by focusing on algorithms that deal with joining ranked results produced by search services. We cast this problem as a generalization of the traditional rank aggregation problem, i.e., combining several ranked lists of objects to produce a single consensus ranking. Rank-join algorithms, also called top-k join algorithms, aim at determining the best overall results without accessing all the objects. The rank-join problem has been dealt with in the literature by extending rank aggregation algorithms to the case of join in the setting of relational databases. However, previous approaches to top-k queries did not consider some of the distinctive features of search engines on the Web. Indeed, as pointed out in the previous chapter, joins in this context differ from the traditional relational setting for a number of aspects: services can be accessed according to limited patterns, i.e. some inputs need to be provided; accessing services is costly, since they are typically remote; the output is returned in pages of results and typically according to some ranking criterion; multiple search services can be used to answer the same query; users can interact with the system in order to refine their search criteria. This chapter analyzes the challenges that need to be tackled in the design of rank-join algorithms within the context of Search Computing.