ABSTRACT
In the rank join problem, we are given a set of relations and a scoring function, and the goal is to return the join results with the top K scores. It is often the case in practice that the inputs may be accessed in ranked order and the scoring function is monotonic. These conditions allow for efficient algorithms that solve the rank join problem without reading all of the input. In this paper, we present a thorough analysis of such rank join algorithms. A strong point of our analysis is that it is based on a more general problem statement than previous work, making it more relevant to the execution model that is employed by database systems. One of our results indicates that the well known HRJN algorithm has shortcomings, because it does not stop reading its input as soon as possible. We find that it is NP-hard to overcome this weakness in the general case, but cases of limited query complexity are tractable. We prove the latter with an algorithm that infers provably tight bounds on the potential benefit of reading more input in order to stop as soon as possible. As a result, the algorithm achieves a cost that is within a constant factor of optimal.
- Parag Agrawal and Jennifer Widom. Confidence-aware joins in large uncertain databases. Technical report, Stanford University, 2007. Available at http://dbpubs.stanford.edu/pub/2007-14.Google Scholar
- Ronald Fagin. Combining fuzzy information from multiple systems. Journal of Computer and System Sciences, 58(1):83--99, 1999. Google Scholar
Digital Library
- Ronald Fagin, Amnon Lotem, and Moni Naor. Optimal aggregation algorithms for middleware. Journal of Computer and System Sciences, 66(4):614--656, 2003. Google Scholar
Digital Library
- Ihab F. Ilyas, Walid G. Aref, and Ahmed K. Elmagarmid. Supporting top-k join queries in relational databases. The VLDB Journal, 13(3):207--221, 2004. Google Scholar
Digital Library
- Ihab F. Ilyas, Walid G. Aref, Ahmed K. Elmagarmid, Hicham G. Elmongui, Rahul Shah, and Jeffrey Scott Vitter. Adaptive rank-aware query optimization in relational databases. ACM Transaction on Database Systems, 31(4):1257--1304, 2006. Google Scholar
Digital Library
- Chengkai Li, Kevin Chen-Chuan Chang, Ihab F. Ilyas, and Sumin Song. RankSQL: query algebra and optimization for relational top-k queries. In ACM SIGMOD International Conference on Management of Data, pages 131--142, 2005. Google Scholar
Digital Library
- Nikos Mamoulis, Man Lung Yiu, Kit Hung Cheng, and David W. Cheung. Efficient top-k aggregation of ranked inputs. ACM Transaction on Database Systems, 32(3):19, 2007. Google Scholar
Digital Library
- Apostol Natsev, Yuan-Chi Chang, John R. Smith, Chung-Sheng Li, and Jeffrey Scott Vitter. Supporting incremental join queries on ranked inputs. In International Conference on Very Large Databases, pages 281--290, 2001. Google Scholar
Digital Library
- Karl Schnaitter and Neoklis Polyzotis. Evaluating rank joins with optimal cost. Technical report, UC Santa Cruz, 2007. Available at http://www.soe.ucsc.edu/research/reports/UCSC-CRL-07-10.pdf.Google Scholar
- Karl Schnaitter, Joshua Spiegel, and Neoklis Polyzotis. Depth estimation for ranking query optimization. In International Conference on Very Large Databases, pages 902--913, 2007. Google Scholar
Digital Library
Index Terms
Evaluating rank joins with optimal cost
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