  Analytical processing on XML repositories is usually enabled by designing
complex data transformations that shred the documents into a common data
warehousing schema. This can be very time-consuming and costly, especially if
the underlying XML data has a lot of variety in structure, and only a subset of
attributes constitutes meaningful dimensions and facts. Today, there is no tool
to explore an XML data set, discover interesting attributes, dimensions and
facts, and rapidly prototype an OLAP solution.
  In this paper, we propose a system, called SEDA that enables users to start
with simple keyword-style querying, and interactively refine the query based on
result summaries. SEDA then maps query results onto a set of known, or newly
created, facts and dimensions, and derives a star schema and its instantiation
to be fed into an off-the-shelf OLAP tool, for further analysis.
