  Keyword search in relational databases has been widely studied in recent
years because it does not require users neither to master a certain structured
query language nor to know the complex underlying database schemas. Most of
existing methods focus on answering snapshot keyword queries in static
databases. In practice, however, databases are updated frequently, and users
may have long-term interests on specific topics. To deal with such a situation,
it is necessary to build effective and efficient facility in a database system
to support continual keyword queries.
  In this paper, we propose an efficient method for answering continual top-$k$
keyword queries over relational databases. The proposed method is built on an
existing scheme of keyword search on relational data streams, but incorporates
the ranking mechanisms into the query processing methods and makes two
improvements to support efficient top-$k$ keyword search in relational
databases. Compared to the existing methods, our method is more efficient both
in computing the top-$k$ results in a static database and in maintaining the
top-$k$ results when the database continually being updated. Experimental
results validate the effectiveness and efficiency of the proposed method.
