Probabilistic Ranking of Database Query Results

Chaudhuri, Surajit; Das, Gautam; Hristidis, Vagelis; Weikum, Gerhard

doi:10.1016/b978-012088469-8.50078-4

Cited by 114 publications

(47 citation statements)

References 14 publications

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“…Recall Section 1, we mentioned potential purposes/applications of the explicit and implicit queries. One is to sort the qualified objects according to their appearance probabilities, which is (somewhat) similar to the ranking query [3,16,23]; another is to return the number of qualified objects, which is (somewhat) similar to the aggregate query [31,47]. Both of tasks can be easily achieved by the minor modifications of our proposed algorithms.…”

Section: Query Processing For Implicit Csptrqmentioning

confidence: 97%

SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects

Wang

Yao

Cheng³

et al. 2015

Geoinformatica

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This paper studies the constrained-space probabilistic threshold range query (CSPTRQ) for moving objects, where objects move in a constrained-space (i.e., objects are forbidden to be located in some specific areas), and objects' locations are uncertain. We differentiate two forms of CSPTRQs: explicit and implicit ones. Specifically, for each moving object o, we model its location uncertainty as a closed region, u, together with a probability density function. We also model a query range, R, as an arbitrary polygon. An explicit query can be reduced to a search (over all the u) that returns a set of tuples in form of (o, p) such that p ≥ p t , where p is the probability of o being located in R, and 0 ≤ p t ≤ 1 is a given probabilistic threshold. In contrast, an implicit query returns only a set of objects (without attaching the specific probability information), whose probabilities being located in R are higher than p t . The CSPTRQ is a variant of the traditional probabilistic threshold range query (PTRQ). As objects moving in a constrained-space are common, clearly, it can also find many applications. At the first sight, our problem can be easily tackled by extending existing methods used to answer the PTRQ. Unfortunately, those classical techniques are not well suitable for our problem, due to a set of new challenges. Another method used to answer the constrained-space probabilistic range query (CSPRQ) can be easily extended to tackle our problem, but a simple adaptation of this method is inefficient, due to its weak pruning/validating capability. To solve our problem, we develop targeted solutions that are easy-to-understand and also easy-to-implement. Our central idea Zhi-Jie Wang is to swap the order of geometric operations and to compute the appearance probability in a multi-step manner. We demonstrate the efficiency and effectiveness of the proposed methods through extensive experiments. Meanwhile, from the experimental results, we further perceive the difference between explicit and implicit queries; this finding is interesting and also meaningful especially for the topics of other types of probabilistic threshold queries.

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Section: Query Processing For Implicit Csptrqmentioning

confidence: 97%

SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects

Wang

Yao

Cheng³

et al. 2015

Geoinformatica

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“…For example, instead of simple tuple scoring functions (such as price), one could also use more complex scoring functions such as assigning a relevance score [6] to each of the tuples. There exists a large volume of literature on such ranking/scoring functions [1,6,14]. Even though any of these functions are possible, in our implementation, we use a simple and intuitive measure, which is based on the normalized inverse document frequency [1].…”

Section: Application-specific Instantiations Of the Probabilistic Framentioning

confidence: 99%

“…This approach can be very effective when the user is relatively sophisticated and knows what she wants, because the ranking function can be directly provided by the user. In the case of a naive user, who is unable to provide a good ranking function, there have been many efforts to develop suitable system-generated ranking functions, by both IR [6] and database [1,14,15] researchers. At the same time, it has also been recognized [7,25] that the rank-retrieval-based approach has an inherent limitation for naive users: it is not an interactive process, and if the user does not like the returned results, it is not easy to determine how the ranking function should be changed.…”

Section: Related Workmentioning

confidence: 99%

A holistic and principled approach for the empty-answer problem

et al. 2016

Self Cite

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We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty-answer set, our framework dynamically computes and suggests alternative queries with fewer conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty-answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. Moreover, we discuss two important extensions to the base framework: the specification of a minimum size on the The work was primarily done while D. Mottin and A. Marascu were at the University of Trento. number of results returned by a relaxed query and the possibility of proposing multiple conditions at the same time. We analyze the proposed solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantages over the existing approaches.

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“…Top-k query processing has received much attention in a variety of settings such as similarity search on multimedia data [7,24,29,30,45,46], ranked retrieval on text and semistructured documents in digital libraries and on the Web [3,6,36,40,48,52,55], network and stream monitoring [4,14] collaborative recommendation and preference queries on ecommerce product catalogs [17,31,42,56], and ranking of SQL-style query results on structured data sources in general [1,11,18]. Among the ample work on top-k query processing, the TA family of algorithms for monotonic score aggregation [25,30,46] stands out as an extremely efficient and highly versatile method.…”

Section: Related Workmentioning

confidence: 99%

Algebraic query optimization for distributed top-k queries

Neumann

Michel

2007

Informatik Forsch. Entw.

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Distributed top-k query processing is increasingly becoming an essential functionality in a large number of emerging application classes. This paper addresses the efficient algebraic optimization of top-k queries in wide-area distributed data repositories where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers and the computational costs include network latency, bandwidth consumption, and local peer work. We use a dynamic programming approach to find the optimal execution plan using compact data synopses for selectivity estimation that is the basis for our cost model. The optimized query is executed in a hierarchical way involving a small and fixed number of communication phases. We have performed experiments on real web data that show the benefits of distributed top-k query optimization both in network resource consumption and query response time

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Probabilistic Ranking of Database Query Results

Cited by 114 publications

References 14 publications

SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects

SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects

A holistic and principled approach for the empty-answer problem

Algebraic query optimization for distributed top-k queries

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