Efficient Query Evaluation on Probabilistic XML Data

摘要: In many application scenarios, reliability and accuracy of data are great importance. Data is often uncertain or inconsistent because the exact state represented real world objects unknown. A number models have emerged to cope with imperfect in order guarantee a level accuracy. These include probabilistic XML (P-XML) –an semi-structured model– U-Rel table-structured model. used by MayBMS, an relational database management system (URDBMS) that provides scalable query evaluation. contrast U-Rel, there does not exist efficient evaluation mechanism for P-XML. In this thesis, we approach problem instructing MayBMS P-XML evaluate XPath queries on as SQL data. This entails two aspects: (1) mapping from ensures same information instances both structures, (2) question specified languages. We present specification corresponding mapping. Additionally, designs specification. The first design constructs such way traditional second differs sense component evaluated part process. offers advantage more efficient. allows optimizations affect performance However, process burdened extra task evaluating component. An extensive experimental synthetically generated sets real-world shows our implementation most scenarios. Not only executed efficient, also improved