In many emerging applications, such as sensor networks, location-based services,
and data integration, the database is inherently uncertain. To handle a large
amount of uncertain data, probabilistic databases have been recently proposed,
where probabilistic queries are enabled to provide answers with statistical guarantees.
In this thesis, we study the important issues of managing the quality of
a probabilistic database. We first address the problem of measuring the ambiguity,
or quality, of a probabilistic query. This is accomplished by computing the
PWS-quality score, a recently proposed measure for quantifying the ambiguity of
query answers under the possible world semantics. We study the computation of
the PWS-quality for the top-k query. This problem is not trivial, since directly
computing the top-k query score is computationally expensive. To tackle this
challenge, we propose efficient approximate algorithms for deriving the quality
score of a top-k query. We have performed experiments on both synthetic and
real data to validate their performance and accuracy.
Our second contribution is to study how to use the PWS-quality score to
coordinate the process of cleaning uncertain data. Removing ambiguous data
from a probabilistic database can often give us a higher-quality query result.
However, this operation requires some external knowledge (e.g., an updated value
from a sensor source), and is thus not without cost. It is important to choose the
correct object to clean, in order to (1) achieve a high quality gain, and (2) incur
a low cleaning cost. In this thesis, we examine different cleaning methods for a
probabilistic top-k query. We also study an interesting problem where different
query users have their own budgets available for cleaning. We demonstrate how
an optimal solution, in terms of the lowest cleaning costs, can be achieved, for
probabilistic range and maximum queries. An extensive evaluation reveals that
these solutions are highly efficient and accurate. / published_or_final_version / Computer Science / Master / Master of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/174493 |
| Date | January 2011 |
| Creators | Li, Xiang, 李想 |
| Contributors | Cheng, CK, Cheung, DWL |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B47753134 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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