Database Reference
In-Depth Information
Figure 7. Execution time for Q
1
to Q
4
to their cost in pre-processing phrase and makes twice greedy selection in processing phrase, so that it
can obtain the near-globally optimization solution.
Performance Report
Figure 7 report the tree construction time of our algorithm for the 5 test queries (since the execution time
of
Q
5
is much longer than the first 4 queries, we do not show its histogram in the figure). Our algorithm
took no more than 2.4 second for the first 4 queries queries that returned several hundred results. It
took about 4 seconds for the 5
th
query that returned 16,213 tuples. Thus our algorithm can be used in an
interactive environment.
CONCLUSION
This chapter proposed a categorization approach to address diverse user preferences, which can help
users navigate many query results. This approach first summarized preferences of all users in the system
by clustering the query history, and then divided tuples into clusters using the different kinds of user
preferences. When a specific user issues a query, our approach create a category tree over the clusters
appearing in the results of the query to help users navigate these results. Our approach differs from the
several existing approaches in two aspects: (i) our approach does not require a user profile or a meaning-
ful query when deciding the user preferences for a specific user, and (ii) the category tree construction
algorithm proposed in this chapter considers both the cost of visiting intermediate nodes (including the
cost of visiting the tuples in intermediate nodes) and the cost of visiting the tuples in leaf nodes. In the
future, we will investigate how to accommodate the dynamic nature of user preferences and how to
integrate the ranking approach into our approach.
