=Paper=
{{Paper
|id=None
|storemode=property
|title=Exploiting Tag Clouds for Database Browsing and Querying
|pdfUrl=https://ceur-ws.org/Vol-592/PaperDemo01.pdf
|volume=Vol-592
|dblpUrl=https://dblp.org/rec/conf/caise/LeoneGN10a
}}
==Exploiting Tag Clouds for Database Browsing and Querying==
https://ceur-ws.org/Vol-592/PaperDemo01.pdf
Exploiting Tag Clouds
for Database Browsing and Querying
Stefania Leone, Matthias Geel, and Moira C. Norrie
Institute for Information Systems, ETH Zurich
CH-8092 Zurich, Switzerland
{leone|geel|norrie}@inf.ethz.ch
Abstract. We show how tag clouds can be used alongside more tradi-
tional query languages and data visualisation techniques as a means for
browsing and querying databases. Our approach is based on a general,
extensible framework that supports different modes of visualisation as
well as different database systems. A number of demonstrator databases
and interfaces will be used to show how tag clouds can be used to vi-
sualise and browse data or metadata and even a mix of both in object
databases and relational databases. Further, we will demonstrate syn-
chronised browsing based on tag clouds as well as ways in which tag
clouds can be combined with other forms of querying and data visuali-
sation.
Keywords: tag cloud, data visualisation, database interface
1 Introduction
Tag clouds are widely used in Web 2.0 applications for visualising user-generated
tags and folksonomies of specific web sites such as Flickr1 . The presentation and
layout of tags can be controlled so that features such as the size, font and colour
can be used to give some measure of the importance of a given tag, while the
positioning of tags may be based on pure aesthetics or some form of relationship
between tags.
Given the flexibility of tag clouds in terms of information representation
together with the simplicity of the associated style of navigation, it is natural
that database researchers should consider exploiting the concept of tag clouds to
address the longstanding problems of database usability [1]. The use of a query
language requires the user to master not only the query language but also the
database schema. To allow users to view the data in a natural way, a higher-level
presentation of the database content such as a visual schema browser and query
interface is needed. Another approach is to focus on the data rather than the
schema as supported in keyword search interfaces to databases. Tag clouds have
been proposed as a means of summarising and refining the results of keyword
searches as presented in [2, 3]. In this case, the term data cloud is used to refer
to their particular adaptation of tag clouds for this purpose. An interesting
1
http://www.flickr.com
2 S. Leone et al.
feature of their approach is that since it was developed for relational databases,
the developer of a data cloud application specifies how application entities can
be composed from the relations in the database in order that keyword search
can be applied to entities rather than simple attributes or tuples. The keyword
search is based on a traditional information retrieval approach where entities
are considered as documents and attribute values as weighted terms. Another
project that uses tag clouds for summarising query results is PubCloud [4] for
searching the PubMed biomedical literature database. In this case, the tag clouds
are generated from words extracted from the abstracts returned by the query.
Our goal was to investigate the extent to which tag clouds could be exploited
to support more traditional forms of database browsing and querying, either
replacing existing query languages and other modes of data visualisation or be-
ing used alongside them. Our tag clouds therefore mainly represent data and
metadata values rather than terms occurring within them. To support our in-
vestigations, we have developed a general, extensible framework that supports
different modes of data visualisation, including customisable tag clouds. We have
also designed it so that different types of databases can be accessed and currently
have implementations for both object databases and relational databases.
A key advantage of the tag cloud approach is that it is data-driven rather
than schema-driven which is particularly beneficial to users with no experience
of databases and query languages. Our initial user studies have shown that even
users with low computer literacy and no previous experience of tag clouds were
able to find the results of non-trivial queries using our system. At the same time,
expert users also gave favourable feedback about the system and particularly
liked the fact that it could be combined with query expressions.
Our contributions include:
– A data browser that allows any data source to be browsed and queried using
tag clouds.
– Experimentation with text and position features of tags in a tag cloud to
make clouds more informative.
– A tool that serves different purposes: Novice users are able to access struc-
tured data sources without knowing the query language and schema, while
expert users can browse a data source in order to get to know the schema
and thus be enable to express complex queries over the data source.
– An extensible and flexible platform for experimentation where new data
sources and new visualisation techniques can be added.
In the following sections, we provide an overview of the data browser, the
architecture and also the demonstration.
2 Data Browser
As highlighted in [5], tag clouds serve multiple purposes. They can be used for
searching for specific information, browsing a data collection without a specific
target, as a tool for impression formation and gisting, and to recognise what a
Exploiting Tag Clouds for Database Browsing and Querying 3
data collection is about. In the Web, tags of a tag cloud are usually hyperlinks
that lead to a collection of items that are associated with a tag. Tag clouds are
graphically appealing due to different visualisation features. Tag cloud features
include text features, such as the tag content, the size, font style and colour
as well as the positioning and order of tags in a cloud. A lot of studies, such
as [5–7] have experimented with tag cloud features and positioning and their
impact on users. According to [5, 6], font size, font weight and intensity are the
most important features. While topic-based layouts of tags can improve search
performance for specific search tasks compared to random arrangements, they
still perform worse than alphabetic layouts according to [7].
We adapted these concepts to browse structured data where tags represent
attribute values. Clicking on a tag initiates a selection for data items with the
corresponding attribute value. In the case of object databases, the result would
be a collection of objects, while in the case of a relational database it would
be a collection of tuples, i.e. a relation. We note that concepts similar to those
proposed for data clouds in [2, 3] could be adopted to return entities rather than
tuples for specific applications. Similarly, it is possible to mix different attribute
values in a single tag cloud or to form tag clouds from combined attribute val-
ues. In addition, we use these concepts to also browse metadata and have even
experimented with a mix of metadata and data within tag clouds.
We now explain these concepts further by means of an example based on
a database with information about contacts and their locations. Generally, we
define a data source to be a set of data collections, where each collection contains
data items of a specific type. These collections are either class extents or sets of
objects of a specific type in object databases, while they are relations in relational
databases.
Fig. 1. Schema and Data Browsing
The metadata that defines the schema of a database can itself be represented
by a tag cloud as shown in Figure 1. On the lefthand side of this figure, the tag
4 S. Leone et al.
cloud gives the names of the various collections of data items within the database.
The default is to have the size of the tags represent the relative cardinality of
the collection.
A user can start browsing a database either by entering a query expression
in the window below the tag clouds or by selecting one or more of the tags
in the schema tag cloud. Each collection can have a default attribute or set of
attributes specified for its visualisation as a tag cloud. However, the user can
also specify this by means of a simple selection of attributes through checkboxes.
Alternatively, one can display the attributes themselves as a tag cloud in the
lefthand window and allow the users to select one or more attributes as tags.
In this way, we support synchronised browsing across the metadata and data
through the adjacent tag clouds.
In the example of Figure 1, the attribute lastname is displayed in the tag
cloud on the right as indicated by the navigation path shown on top of the cloud
window. The size of the tags in this tag cloud represents how many data items
have that attribute value. In this way, the tag cloud can be considered as a
visualistaion of the attribute value frequency. The user can now click on a tag
and further refine their selection. When hovering over a lastname tag, a user
gets detailed information about the number of objects that have this attribute
value, or in the case of only a single object, we get the set of attribute values.
Fig. 2. Exploiting tag cloud features
We offer different modes for visualisation, as depicted in Figure 2. In the
tag cloud in the upper-left corner, the contacts are displayed by lastnames. In
the upper-right corner, two attributes are bound to the tag content feature,
namely the attribute lastname of contacts as well as the attribute city of
the associated location objects. The tags thus represent the number of contacts
with a given name that live in the same city. In this example data set, the
tag Froidvaux-Zurich represents the set of contacts with lastname ‘Froidvaux’
who live in ‘Zurich’. As one can see in this example, more people with the name
‘Froidvaux’ live in ‘Uster’, than in ‘Zurich’. In the lower-left corner of Figure 2, we
added colour as an additional visualisation dimension: The attribute lastname is
Exploiting Tag Clouds for Database Browsing and Querying 5
bound to the tag content, while the attribute city from the associated location
is bound to the colour feature. As one can see from the index on the righthand
side of the figure, each distinct attribute value of the city attribute is assigned
a specific colour. We have experimented with these different tag features in a
user study. Care has to be taken in choosing the right attributes to bind to the
colour feature. It only makes sense, if the distinct set of values is not too large,
since otherwise the index becomes very large and the tag colours are not very
informative.
3 Architecture
Data Browser User Interface
Visualisation Library
Visualisation
Manager
Manager
Text
Database Adapter
Relational
OO Database ...
Database
Fig. 3. System Architecture
Figure 3 gives an overview of the system architecture. The manager compo-
nent is the heart of the system and responsible for handling requests from the
user interface, forwarding these to the database through the database adapter
and invoking the visualisation manager to transform the results into the appro-
priate visual elements to be returned and displayed in the GUI. Our framework
is extensible in multiple ways. Firstly, we provide a data adapter interface which
can be implemented for any data source. At the moment, we have an implemen-
tation for the object databases db4objects2 , OMS Avon3 and OMSPro4 as well
as a MySQL implementation. Secondly, the visualisation manager can manage
different kinds of visualisation techniques. Therefore, we provide a visualisation
2
http://www.db4o.com/
3
http://maven.globis.ethz.ch/projects/avon/
4
http://www.globis.ethz.ch/research/oms/platforms/omspro
6 S. Leone et al.
interface which has to be implemented to add a new technique to the visualisa-
tion library. We currently provide a tag cloud visualisation, and are working on
a bubble chart visualisation. Our data browser application is flexible and con-
figurable and is currently used as a platform for experimentation in our research
group.
4 Demonstration
In our demonstration, we will show how users can browse both relational and
object databases using our data browser. The demonstration will include showing
tag clouds over data, metadata and a mix of data and metadata. We will provide
a set of demonstrator databases including a contacts database and a publications
database implemented using both relational and object databases. Visitors will
be able to freely browse these databases, pose queries and exploratively get an
impression of the schema and the data. We will also provide a list of query
tasks from our user study so that users can experience how query results can
be obtained using only the bowser, using only query expressions and using the
browser in conjunction with query expressions.
References
1. Jagadish, H.V., Chapman, A., Elkiss, A., Jayapandian, M., Li, Y., Nandi, A., Yu,
C.: Making Database Systems Usable. In: Proc. ACM SIGMOD’07. (2007)
2. Koutrika, G., Zadeh, Z.M., Garcia-Molina, H.: Data Clouds: Summarizing Keyword
Search Results over Structured Data. In: Proc. EDBT’09. (2009)
3. Koutrika, G., Zadeh, Z.M., Garcia-Molina, H.: CourseCloud: Summarizing and
Refining Keyword Searches over Structured Data. In: Demo Proc. EDBT’09. (2009)
4. Kuo, B.Y.L., Hentrich, T., Good, B.M.., Wilkinson, M.D.: Tag Clouds for Summa-
rizing Web Search Results. In: Proc. WWW’07. (2007)
5. Rivadeneira, A.W., Gruen, D.M., Muller, M.J., Millen, D.R.: Getting our Head in
the Clouds: Toward Evaluation Studies of Tag Clouds. In: Proc. CHI ’07. (2007)
6. Bateman, S., Gutwin, C., Nacenta, M.: Seeing Things in the Clouds: The Effect of
Visual Features on Tag Cloud Selections. In: Proc. 19th ACM Conf. on Hypertext
and Hypermedia. (2008)
7. Schrammel, J., Leitner, M., Tscheligi, M.: Semantically Structured Tag Clouds:
An Empirical Evaluation of Clustered Presentation Approaches. In: Proc. CHI’09.
(2009)