7,481 research outputs found
Believe It or Not: Adding Belief Annotations to Databases
We propose a database model that allows users to annotate data with belief
statements. Our motivation comes from scientific database applications where a
community of users is working together to assemble, revise, and curate a shared
data repository. As the community accumulates knowledge and the database
content evolves over time, it may contain conflicting information and members
can disagree on the information it should store. For example, Alice may believe
that a tuple should be in the database, whereas Bob disagrees. He may also
insert the reason why he thinks Alice believes the tuple should be in the
database, and explain what he thinks the correct tuple should be instead.
We propose a formal model for Belief Databases that interprets users'
annotations as belief statements. These annotations can refer both to the base
data and to other annotations. We give a formal semantics based on a fragment
of multi-agent epistemic logic and define a query language over belief
databases. We then prove a key technical result, stating that every belief
database can be encoded as a canonical Kripke structure. We use this structure
to describe a relational representation of belief databases, and give an
algorithm for translating queries over the belief database into standard
relational queries. Finally, we report early experimental results with our
prototype implementation on synthetic data.Comment: 17 pages, 10 figure
Storing and Querying Probabilistic XML Using a Probabilistic Relational DBMS
This work explores the feasibility of storing and querying probabilistic XML in a probabilistic relational database. Our approach is to adapt known techniques for mapping XML to relational data such that the possible worlds are preserved. We show that this approach can work for any XML-to-relational technique by adapting a representative schema-based (inlining) as well as a representative schemaless technique (XPath Accelerator). We investigate the maturity of probabilistic rela- tional databases for this task with experiments with one of the state-of- the-art systems, called Trio
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