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Distributed probabilistic top-k dominating queries over uncertain databases.

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Title: Distributed probabilistic top-k dominating queries over uncertain databases.
Authors: Rai, Niranjan1 (AUTHOR), Lian, Xiang1 (AUTHOR) xlian@kent.edu
Source: Knowledge & Information Systems. Nov2023, Vol. 65 Issue 11, p4939-4965. 27p.
Subjects: Probabilistic databases, Search algorithms, Distributed databases, False alarms, Databases, Business planning
Abstract: In many real-world applications such as business planning and sensor data monitoring, one important, yet challenging, task is to rank objects (e.g., products, documents, or spatial objects) based on their ranking scores and efficiently return those objects with the highest scores. In practice, due to the unreliability of data sources, many real-world objects often contain noises and are thus imprecise and uncertain. In this paper, we study the problem of probabilistic top-k dominating (PTD) query on such large-scale uncertain data in a distributed environment, which retrieves k uncertain objects from distributed uncertain databases (on multiple distributed servers), having the largest ranking scores with high confidences. In order to efficiently tackle the distributed PTD problem, we propose a MapReduce framework for processing distributed PTD queries over distributed uncertain databases. In this MapReduce framework, we design effective pruning strategies to filter out false alarms in the distributed setting, propose cost-model-based index distribution mechanisms over servers, and develop efficient distributed PTD query processing algorithms. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed distributed PTD approaches on both real and synthetic data sets through various experimental settings. [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: <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="%22Distributed+databases%22">Distributed databases</searchLink><br /><searchLink fieldCode="DE" term="%22False+alarms%22">False alarms</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Business+planning%22">Business planning</searchLink>
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  Data: In many real-world applications such as business planning and sensor data monitoring, one important, yet challenging, task is to rank objects (e.g., products, documents, or spatial objects) based on their ranking scores and efficiently return those objects with the highest scores. In practice, due to the unreliability of data sources, many real-world objects often contain noises and are thus imprecise and uncertain. In this paper, we study the problem of probabilistic top-k dominating (PTD) query on such large-scale uncertain data in a distributed environment, which retrieves k uncertain objects from distributed uncertain databases (on multiple distributed servers), having the largest ranking scores with high confidences. In order to efficiently tackle the distributed PTD problem, we propose a MapReduce framework for processing distributed PTD queries over distributed uncertain databases. In this MapReduce framework, we design effective pruning strategies to filter out false alarms in the distributed setting, propose cost-model-based index distribution mechanisms over servers, and develop efficient distributed PTD query processing algorithms. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed distributed PTD approaches on both real and synthetic data sets through various experimental settings. [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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        Text: English
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        Type: general
      – SubjectFull: Search algorithms
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      – SubjectFull: Distributed databases
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              Text: Nov2023
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