Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Efficient top-k aggregation of ranked inputs
ACM Transactions on Database Systems (TODS)
Efficient online top-K retrieval with arbitrary similarity measures
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Efficient processing of exact top-k queries over disk-resident sorted lists
The VLDB Journal — The International Journal on Very Large Data Bases
A general top-k algorithm for web data sources
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Supporting efficient top-k queries in type-ahead search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Top-k join queries: overcoming the curse of anti-correlation
Proceedings of the 17th International Database Engineering & Applications Symposium
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A top-k query combines different rankings of the same set of objects and returns the k objects with the highest combined score according to an aggregate function. We bring to light some key observations, which impose two phases that any top-k algorithm, based on sorted accesses, should go through. Based on them, we propose a new algorithm, which is designed to minimize the number of object accesses, the computational cost, and the memory requirements of top-k search. Adaptations of our algorithm for search variants (exact scores, on-line and incremental search, top-k joins, other aggregate functions, etc.) are also provided. Extensive experiments with synthetic and real data show that, compared to previous techniques, our method accesses fewer objects, while being orders of magnitude faster.