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A Survey of Uncertain Data Algorithms and Applications (2009)
| Venue: | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
| Citations: | 68 - 13 self |
BibTeX
@ARTICLE{Aggarwal09asurvey,
author = {Charu C. Aggarwal and Philip S. Yu},
title = {A Survey of Uncertain Data Algorithms and Applications},
journal = {IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING},
year = {2009},
volume = {21},
number = {5},
pages = {609--623}
}
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Abstract
In recent years, a number of indirect data collection methodologies have led to the proliferation of uncertain data. Such databases are much more complex because of the additional challenges of representing the probabilistic information. In this paper, we provide a survey of uncertain data mining and management applications. We will explore the various models utilized for uncertain data representation. In the field of uncertain data management, we will examine traditional database management methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as frequent pattern mining, outlier detection, classification, and clustering. We discuss different methodologies to process and mine uncertain data in a variety of forms.
Keyphrases
uncertain data algorithm uncertain data mining uncertain data representation different methodology uncertain data management selectivity estimation probabilistic information mine uncertain data additional challenge join processing management application traditional database management method olap query various model uncertain data query processing indirect data collection methodology recent year traditional mining problem frequent pattern mining
