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Range-constrained probabilistic mutual furthest neighbor queries in uncertain databases.

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Title: Range-constrained probabilistic mutual furthest neighbor queries in uncertain databases.
Authors: Bavi, Kovan1,2 (AUTHOR), Lian, Xiang1 (AUTHOR) xlian@kent.edu
Source: Knowledge & Information Systems. Jun2023, Vol. 65 Issue 6, p2375-2402. 28p.
Subjects: Probabilistic databases, Search algorithms, Biological databases, Sensor networks, Databases, Location-based services
Abstract: For decades, query processing over uncertain databases has received much attention from the database community due to the pervasive data uncertainty in many real-world applications such as location-based services (LBS), sensor networks, business planning, biological databases, and so on. In this paper, we will study a novel query type, namely range-constrained probabilistic mutual furthest neighbor query (PMFN), over uncertain databases. PMFN retrieves a set of object pairs, (o i , o j) , within a given query range Q, such that uncertain objects o i and o j are furthest neighbors of each other with high probabilities. In order to efficiently tackle the PMFN problem, we propose effective pruning methods, range, convex hull, and hypersphere pruning, for filtering out uncertain objects that can never appear in the PMFN answer set. Then, we also design spatial and probabilistic pruning methods to rule out false alarms of PMFN candidate pairs. Finally, we utilize a variant of the R ∗ -tree to integrate our proposed pruning methods and efficiently process ad hoc PMFN queries. Extensive experiments show the efficiency and effectiveness of our pruning techniques and PMFN query processing algorithms over real and synthetic data sets. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Range-constrained probabilistic mutual furthest neighbor queries in uncertain databases.
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  Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. Jun2023, Vol. 65 Issue 6, p2375-2402. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Probabilistic+databases%22">Probabilistic databases</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+databases%22">Biological databases</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Location-based+services%22">Location-based services</searchLink>
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  Data: For decades, query processing over uncertain databases has received much attention from the database community due to the pervasive data uncertainty in many real-world applications such as location-based services (LBS), sensor networks, business planning, biological databases, and so on. In this paper, we will study a novel query type, namely range-constrained probabilistic mutual furthest neighbor query (PMFN), over uncertain databases. PMFN retrieves a set of object pairs, (o i , o j) , within a given query range Q, such that uncertain objects o i and o j are furthest neighbors of each other with high probabilities. In order to efficiently tackle the PMFN problem, we propose effective pruning methods, range, convex hull, and hypersphere pruning, for filtering out uncertain objects that can never appear in the PMFN answer set. Then, we also design spatial and probabilistic pruning methods to rule out false alarms of PMFN candidate pairs. Finally, we utilize a variant of the R ∗ -tree to integrate our proposed pruning methods and efficiently process ad hoc PMFN queries. Extensive experiments show the efficiency and effectiveness of our pruning techniques and PMFN query processing algorithms over real and synthetic data sets. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10115-022-01807-0
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      – Code: eng
        Text: English
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        PageCount: 28
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      – SubjectFull: Probabilistic databases
        Type: general
      – SubjectFull: Search algorithms
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      – SubjectFull: Biological databases
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      – SubjectFull: Location-based services
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              M: 06
              Text: Jun2023
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              Y: 2023
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