Managing and Mining Uncertain Data Advances in Database Systems 1st Edition by Charu Aggarwal ISBN 0387096892 9780387096896

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ISBN 10:       0387096892
ISBN 13: 978-0387096896
Author:         Charu C. Aggarwal

Managing and Mining Uncertain Data Advances in Database Systems 1st Table of contents:

• Models for Incomplete and Probabilistic Information

  • Exploration of models used to represent incomplete and probabilistic data, highlighting their importance in data analysis.

• Relational Models and Algebra for Uncertain Data

  • Discussion on how relational models and algebraic operations can be adapted to handle uncertain data.

• Graphical Models for Uncertain Data

  • Introduction to the use of graphical models, such as Bayesian networks, for representing and reasoning about uncertain data.

• Trio: A System for Data Uncertainty and Lineage

  • Overview of the Trio system, designed to manage uncertainty and track data lineage for probabilistic data.

• MayBMS: A System for Managing Large Probabilistic Databases

  • Examination of MayBMS, a system built for managing large-scale probabilistic databases efficiently.

• Uncertainty in Data Integration

  • A look into techniques for integrating data from various sources while accounting for uncertainty.

• Sketching Aggregates over Probabilistic Streams

  • Techniques for efficiently computing aggregate functions on probabilistic data streams.

• Probabilistic Join Queries in Uncertain Databases

  • Approaches to performing join operations in databases that store uncertain data, ensuring accuracy and efficiency.

• Indexing Uncertain Data

  • Methods for indexing uncertain data to improve the performance of queries on large uncertain datasets.

• Querying Uncertain Spatiotemporal Data

  • Approaches for querying data that involves both spatial and temporal uncertainty, such as in geographic or time-series data.

• Probabilistic XML

  • Techniques for handling and querying probabilistic data stored in XML format, addressing the challenges of uncertainty.

• On Clustering Algorithms for Uncertain Data

  • Exploration of clustering algorithms that can handle uncertain data, including how they differ from traditional clustering methods.

• On Applications of Density Transforms for Uncertain Data Mining

  • Investigation of density transformation techniques for mining uncertain data, particularly in detecting patterns and trends.

• Frequent Pattern Mining Algorithms with Uncertain Data

  • Algorithms for mining frequent patterns in datasets that contain uncertainty, crucial for pattern discovery in noisy data.

• Probabilistic Querying and Mining of Biological Images

  • Techniques for querying and mining biological image data, incorporating probabilistic methods to handle uncertainty in image analysis.

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