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by Ihab F. Ilyas,Mohamed A. Soliman
Download Probabilistic Ranking Techniques in Relational Databases (Synthesis Lectures on Data Management) fb2
Networking & Cloud Computing
  • Author:
    Ihab F. Ilyas,Mohamed A. Soliman
  • ISBN:
    160845567X
  • ISBN13:
    978-1608455676
  • Genre:
  • Publisher:
    Morgan & Claypool Publishers; 1 edition (March 22, 2011)
  • Pages:
    80 pages
  • Subcategory:
    Networking & Cloud Computing
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Ihab F. Ilyas, Mohamed A. Soliman Probabilistic Ranking Techniques in Relational Databases Synthesis lectures on data management (Том 14), ISSN 2153-5426.

Ihab F. Soliman. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. Probabilistic Ranking Techniques in Relational Databases Synthesis lectures on data management (Том 14), ISSN 2153-5426. Ihab F.

Probabilistic Ranking Techniques in Relational Databases. Download (pdf, . 0 Mb) Donate Read. Epub FB2 mobi txt RTF. Converted file can differ from the original. If possible, download the file in its original format.

Series: Synthesis Lectures on Data Management (Book 16). Paperback: 180 pages. Published: 20 March 2011. Synthesis Lectures on Data Management, Volume 3, pp 1-71; doi:10. Keywords: Relational databases, Probabilistic Ranking Techniques, Techniques in Relational.

Probabilistic databases have been established as a powerful technique for managing . Probabilistic Ranking Techniques in Relational Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers (2011)Google Scholar.

Probabilistic databases have been established as a powerful technique for managing and analysing large uncertain data sets. A major challenge for probabilistic databases is query evaluation. Synthesis Lectures on DM. Morgan & Claypool (2011)Google Scholar.

Synthesis Lectures on Data Management

Synthesis Lectures on Data Management. The series will publish 50- to 125 page publications on topics pertaining to data management. Probabilistic Ranking Techniques in Relational Databases Ihab F. Ilyas and Mohamed A. Soliman 2011. Uncertain Schema Matching Avigdor Gal 2011.

By: Ihab Ilyas; Mohamed Soliman. Ranking queries are widely used in data exploration, data analysis and decision making scenarios. This lecture describes new formulations and processing techniques for ranking queries on uncertain data

By: Ihab Ilyas; Mohamed Soliman. Publisher: Morgan & Claypool Publishers. Print ISBN: 9781608455676, 160845567X. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models.

Synthesis lectures on data management - 14. Other Titles. Synthesis lectures on data management - 14.

Probabilistic Databases. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable.

Synthesis Lectures on Data Management. Incomplete data and data dependencies in relational databases. Morgan & Claypool. The problem of incomplete information in relational databases, volume 554. Springer Science+Business Media.

Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion