Best-Effort Top-k Query Processing Under Budgetary Constraints

Shmueli-Scheuer, Michal; Li, Chen; Mass, Yosi; Roitman, Haggai; Schenkel, Ralf; Weikum, Gerhard

doi:10.1109/icde.2009.109

Cited by 26 publications

(16 citation statements)

References 13 publications

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“…The same critique applies to work on index pruning [8,23], where the goal is to improve query evaluation speed by reducing the size of the index. While Shmueli-Scheuer et al [27] consider query evaluation under budgetary constraints, but similar to the aforementioned early-termination techniques, the work is concerned with efficient query evaluation, rather than building efficiency-minded ranking functions.…”

Section: Related Workmentioning

confidence: 99%

Ranking under temporal constraints

Wang

Metzler

Lin

2010

Proceedings of the 19th ACM International Conference on Information and Knowledge Management

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This paper introduces the notion of temporally constrained ranked retrieval, which, given a query and a time constraint, produces the best possible ranked list within the specified time limit. Naturally, more time should translate into better results, but the ranking algorithm should always produce some results. This property is desirable from a number of perspectives: to cope with diverse users and information needs, as well as to better manage system load and variance in query execution times. We propose two temporally constrained ranking algorithms based on a class of probabilistic prediction models that can naturally incorporate efficiency constraints: one that makes independent feature selection decisions, and the other that makes joint feature selection decisions. Experiments on three different test collections show that both ranking algorithms are able to satisfy imposed time constraints, although the joint model outperforms the independent model in being able to deliver more effective results, especially under tight time constraints, due to its ability to capture feature dependencies.

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Section: Related Workmentioning

confidence: 99%

Ranking under temporal constraints

Wang

Metzler

Lin

2010

Proceedings of the 19th ACM International Conference on Information and Knowledge Management

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“…Previous work on top-k queries in database community [20] focuses on different goals not related to making a database user-friendly.…”

Section: Related Workmentioning

confidence: 99%

User-driven refinement of imprecise queries

Qarabaqi

Riedewald

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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Abstract-We propose techniques for exploratory search in large databases. The goal is to provide new functionality that aids users in homing in on the right query conditions to find what they are looking for. Query refinement proceeds interactively by repeatedly consulting the user to manage query conditions. This process is characterized by three key challenges: (1) dealing with incomplete and imprecise user input, (2) keeping user effort low, and (3) guaranteeing interactive system response time. We address the first two challenges with a probability-based framework that guides the user to the most important query conditions. To recover from input errors, we introduce the notion of sensitivity and propose efficient algorithms for identifying the most sensitive user input, i.e., those inputs that had the greatest influence on the query results. For the third challenge, we develop techniques that can deliver estimates of the required probabilities within a given hard realtime limit and are able to adapt automatically as the interactive query refinement proceeds.

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“…The k and size parameters are used for both real and synthetic datasets, whereas xsize and mean parameters are used for the synthetic datasets only. We report the I/O cost follow the same approach given in [33]. The cost is measured in unit, one sequential I/O contributes one unit, and one random I/O contributes 5 units.…”

Section: Performance Studiesmentioning

confidence: 99%

Probabilistic ranking over relations

Chang

Qin

et al. 2010

Proceedings of the 13th International Conference on Extending Database Technology

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Probabilistic top-k ranking queries have been extensively studied due to the fact that data obtained can be uncertain in many real applications. A probabilistic top-k ranking query ranks objects by the interplay of score and probability, with an implicit assumption that both scores based on which objects are ranked and probabilities of the existence of the objects are stored in the same relation. We observe that in general scores and probabilities are highly possible to be stored in different relations, for example, in column-oriented DBMSs and in data warehouses. In this paper we study probabilistic top-k ranking queries when scores and probabilities are stored in different relations. We focus on reducing the join cost in probabilistic top-k ranking. We investigate two probabilistic score functions, discuss the upper/lower bounds in random access and sequential access, and provide insights on the advantages and disadvantages of random/sequential access in terms of upper/lower bounds. We also propose random, sequential, and hybrid algorithms to conduct probabilistic top-k ranking. We conducted extensive performance studies using real and synthetic datasets, and report our findings in this paper.

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Best-Effort Top-k Query Processing Under Budgetary Constraints

Cited by 26 publications

References 13 publications

Ranking under temporal constraints

Ranking under temporal constraints

User-driven refinement of imprecise queries

Probabilistic ranking over relations

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