Probabilistic Similarity Join on Uncertain Data

Kriegel, Hans‐Peter; Kunath, Peter; Pfeifle, Martin; Renz, Matthias

doi:10.1007/11733836_22

Cited by 78 publications

(78 citation statements)

References 27 publications

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“…It is extensively studied in [4], [11], [14]. Note that all instances in a multi-valued object exist simultaneously instead of mutually exclusive in an uncertain object.…”

Section: Related Workmentioning

confidence: 99%

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Top-k Similarity Join over Multi-valued Objects

Zhang

Liang

et al. 2012

Database Systems for Advanced Applications

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Abstract. The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and data mining. Given two sets of objects U and V, a top-k similarity join returns k pairs of most similar objects from U ×V. In the conventional model of top-k similarity join processing, an object is usually regarded as a point in a multi-dimensional space and the similarity between two objects is usually measured by distance metrics such as Euclidean distance. However, in many applications an object may be described by multiple values (instances) and the conventional model is not applicable since it does not address the distributions of object instances. In this paper, we study top-k similarity join queries over multi-valued objects. We apply quantile based distance to explore the relative instance distribution among the multiple instances of objects. Efficient and effective techniques to process top-k similarity joins over multi-valued objects are developed following a filtering-refinement framework. Novel distance, statistic and weight based pruning techniques are proposed. Comprehensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our techniques.

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“…It is extensively studied in [4], [11], [14]. Note that all instances in a multi-valued object exist simultaneously instead of mutually exclusive in an uncertain object.…”

Section: Related Workmentioning

confidence: 99%

“…The existing model for handling similarity joins over objects with multiple instances follows the probabilistic semantics on uncertain objects [4], [11], [14] and aims to capture relative instance distribution among objects with multiple instances. Nevertheless, uncertain objects are inherently different than multi-valued objects.…”

Section: Introductionmentioning

confidence: 99%

Top-k Similarity Join over Multi-valued Objects

Zhang

Liang

et al. 2012

Database Systems for Advanced Applications

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“…A number of problems in querying uncertain data have also been studied, such as indexing [41], similarity join [26], nearest neighbor query [27], skyline query [34], clustering [28,30], etc. Top-k Query Processing over Uncertain Data.…”

Section: Related Workmentioning

confidence: 99%

“…Uncertainty is inherent in such applications due to various factors such as data randomness and incompleteness, limitation of equipment, and delay or loss in data transfer. A number of issues have been recently addressed; these include modeling uncertainty [2,36], query evaluation [10,13,14,37], indexing [11,41], top-k queries [22,35,39,42], skyline queries [34], joins [26,27], nearest neighbor query [5,9,27], clustering [28,30], etc.…”

Section: Introductionmentioning

confidence: 99%

Threshold-based probabilistic top-k dominating queries

et al. 2009

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Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Topk dominating queries are very important in many applications including decision making in a multidimensional space. In this paper, we study the problem of efficiently computing top-k dominating queries on uncertain data. We first formally define the problem. Then, we develop an efficient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deficiency in an exact computation, we develop an efficient randomized algorithm with an accuracy guarantee. Our extensive experiments demonstrate that both algorithms are quite efficient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice.

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“…A number of problems in querying uncertain data have also been studied, such as indexing [18], similarity join [5], nearest neighbor query [9], skyline query [15], clustering [10], etc. Relatively complete and detailed study of existing techniques on managing uncertainty can be found in [14] and [20].…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data

Yang

Zhang

et al. 2009

Advances in Data and Web Management

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Abstract. Large amount of uncertain data is inherent in many novel and important applications such as sensor data analysis and mobile data management. A probabilistic threshold range aggregate (PTRA) query retrieves summarized information about the uncertain objects satisfying a range query, with respect to a given probability threshold. This paper is the first one to address this important type of query. We develop a new index structure aU-tree and propose an exact querying algorithm based on aU-tree. For the pursue of efficiency, two techniques SingleSample and DoubleSample are developed. Both techniques provide approximate answers to a PTRA query with accuracy guarantee. Experimental study demonstrates the efficiency and effectiveness of our proposed methods.

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Probabilistic Similarity Join on Uncertain Data

Abstract: Abstract.

Cited by 78 publications

References 27 publications

Top-k Similarity Join over Multi-valued Objects

Top-k Similarity Join over Multi-valued Objects

Threshold-based probabilistic top-k dominating queries

Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data

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