| dc.description.abstract | Content providers, such as enterprises and organizations who publish their content
on the Internet, aim at making their content visible and easily accessible
to the users. The vast amount of data contained in databases impedes their
e orts, as users often nd it challenging to navigate through the available data
and nd the items that best suit their needs. It is therefore necessary for content
providers to motivate users to explore the available data and assist them in
nding items that are interesting to them. State-of-the-art approaches such as
top-k queries are not appropriate for data exploration as they require the users
to be aware of the database structure and the content they are exploring.
In this thesis, we study the problem of enhancing the visibility of database
content through exploratory search and analysis. We propose exploratory algorithms
that return to the user a small number of results, which at the same time
provide a wide overview of the available content. In addition, we present algorithms
that identify items that are appealing to users and can be exploited for
o ering users an insight of the available items and motivating them to explore
the database. In particular, the main contributions of the thesis are:
We develop a framework for organizing and summarizing keyword search
results based on their textual content and temporal data.
We introduce a new type of query, the eXploratory Top-k Join (XTJk)
query, which creates object combinations that are better suited to user
preferences than single objects, and we present algorithms for the e cient
processing of XTJk queries.
We introduce the continuous in
uential query, which returns objects that
are continuously attractive to a large number of users for long periods, and
we present algorithms for the e cient retrieval of continuous in
uential
objects.
We model the diversity of database objects based on user preferences, and
we propose e cient algorithms for selecting products that are attractive
to a wide range of users with diverse preferences.
We describe the Best-terms problem which is the problem of increasing
the rank of a spatio-textual object through the enhancement of its textual
description. We show that the problem is NP-hard and we present
approximate algorithms that retrieve high quality results.
The proposed approaches have been evaluated through extensive experimental
evaluation. The experiments were conducted using both synthetic and real
datasets and demonstrate the e ciency of the proposed methods. | nb_NO |
