Data Cleaning publication categorizer

An important reality when integrating scientific
data is the fact that data may often
be “missing”, partially specified, or conflicting.
Therefore, in this paper, we present
an assertion-based data model that captures
both value-based and structure-based “nulls”
in data. We also introduce the QUEST system,
which leverages the proposed model for
Query-driven Exploration of Semistructured
data with conflicT s and partial knowledge.
Our approach to integration lies in enabling
researchers to observe and resolve conflicts in
the data by considering the context provided
by the data requirements of a given research
question. In particular, we discuss how pathcompatibility
can be leveraged, within the context
of a query, to develop a high-level understanding
of conflicts and nulls in data.