Data summarization on probabilistic databases

数据汇总技术已用于许多数据库管理系统中，以便在不需要确切答案或早期反馈有用的情况下为查询提供快速近似答案。例如，数据库管理系统中的查询优化器使用数据汇总技术来快速估计中间结果的大小和不同查询执行计划的成本。许多在线分析处理、数据挖掘和数据可视化应用程序也依赖于数据摘要技术来计算查询的快速近似答案。 目前，数据汇总技术一般应用于所有数据值都准确的数据库。然而，在许多数据库中，数据值往往与不确定性相关，并且服从概率分布。不确定度可能源于固有的测量不准确、采样误差或网络延迟。这样的数据库在文献中被称为概率数据库。概率数据库最近在许多领域得到了研究，包括数据库管理、传感器网络、消费者行为建模和生物信息学。 这个项目的主要目标是开发关于概率数据库的新的数据汇总技术。这些技术将通过在合成数据集和真实数据集上的实验进行评估。该项目的结果将对概率数据库管理作出重大贡献。

Data summarization techniques have been used in many database management systems to provide quick approximate answers to queries when exact answers are not required or when early feedbacks are helpful. For example, the query optimizers in database management systems employ data summarization techniques to quickly estimate the sizes of intermediate results and the costs of different query execution plans. Many on-line analytical processing, data mining, and data visualization applications also rely on data summarization techniques to compute fast approximate answers to queries. Currently, data summarization techniques are generally applied to databases in which all data values are precise and accurate. However, in many databases, the data values are often associated with uncertainty and follow a probabilistic distribution. The uncertainty is possibly originated from inherent measurement inaccuracies, sampling errors, or network latencies. Such databases are referred to as the probabilistic databases in the literature. Probabilistic databases have been recently studied in a number of domains, including database management, sensor networks, consumer behavior modeling, and bioinformatics. The primary objective of this project is to develop new data summarization techniques on probabilistic databases. These techniques will be evaluated through experiments on synthetic and real data sets. Results from this project will significantly contribute toward probabilistic database management.