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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
Querying by Example Astronomical Archives
F. Pasian and R. Smareglia
Osservatorio Astronomico di Trieste, 34131 Trieste, Italy
Email: pasian@ts.astro.it
Abstract. In this paper, a project aiming to allow access to data archives
by means of a query­by­example mechanism is described. The basic user
query would simply be the submission of an input image, either 1­D or
2­D; the system should allow retrieval of archived images similar to an
input one on the basis of some ``resemblance parameter''.
1. Introduction
With the exponential expansion of information systems, accessing data archives
plays a role which is becoming increasingly important for the dissemination of
scientific knowledge at all levels. The need is felt to make access to data archives
simpler for the community of users at large. Queries on the data, regardless of
their complexity, should be easier to specify, more intuitive, simplified with
respect to those currently in use. In other words, users should be able to express
queries in an intuitive way, without knowing the detailed physical structure
and naming of data. Content­based information retrieval is receiving increasing
attention from scientists, e.g., see Ardizzone et al. (1996), and Lesteven et al.
(1996).
This project aims to allow access to data archives by means of a query­by­
example mechanism: the user should be put in a position to provide a ``user
object'', a template of what he/she wishes to retrieve from the archive. The
system managing the archive should provide the user with archived data resem­
bling the template on the basis of some desired characteristic, and with some
``resemblance parameter''. Data understanding (classification and recognition of
descriptive features) is deemed to be an essential step of the query­by­example
mechanism.
2. Project Objectives
The objective of this project is to integrate database, mass storage, classification
­ feature recognition, and networking aspects into a unique system allowing a
``query­by­example'' approach to the retrieval of data from remotely­accessible
large image archives exploiting the algorithms up to their performance limits.
The basic idea, from the user's point of view, is to be able to submit a query
to a remote archive in the form of an image (2­D or 1­D), telling the system:
``get me all the images/plots in the archive looking like this one, or having this
429

430 Pasian and Smareglia
specific feature''. The basic user query would therefore simply be the submission
of an image, either previously extracted from the very same archive, or owned
by the user, or built via a modelling software. This operation must be feasible
while connected remotely to the archive.
Several problems have been already identified. In the ``global'' approach to
the query­by­example mechanism, the various characteristics of images have the
same weight. Di#culties here include coping with di#erent image resolutions,
being able to search for images having a specific feature in them by just sub­
mitting a small image template containing the feature itself, building models
compatible with the archived images, etc. A more sophisticated approach would
be to add a level of interaction with the user, allowing him/her to know which
are the features of the images the system is able to recognize, and allow a choice
of the features to be searched for.
3. QUBE ­ Overall Features
A system called QUBE is currently being designed to support the QUery­By­
Example paradigm in accessing astronomical data archives. A preliminary list
of features is as follows:
. Network­based interface, for backward­compatibility with already existing
image archives: standard queries on metadata (data descriptions) should
always be possible through the known interface.
. Extensions to standard SQL, to allow handling of image templates by a
relational database: in principle, specifying a query in this extended SQL
should always be possible for an expert user. As a first order approxima­
tion, SQL extensions can be handled by an interpreter.
. Ingestion of ``user objects'' in the system in di#erent formats, with prior­
ity given to astronomy­specific and commercial standards (FITS, but also
GIF, JPEG, etc.).
. ``Global'' approach to query­by­example based on classification methods
such as artificial neural networks: unsupervised in the general case, super­
vised for specific applications.
. Feature recognition algorithms may be used, able to give di#erent weights
to di#erent characteristics of the ``user object'' (the template being com­
pared with the archived data).
. Transmission of data (both ``user objects'' and retrieved data) via the
standard TCP/IP mechanisms.
. An updating capability should be available: in the case classification is
required on an image subset (e.g., wavelength range in a spectral archive)
the system should be able to re­compute classification parameters for all
relevant image subsets in the archive.
. A feedback mechanism should be built in the system, to allow detection
and correction of misclassified data.

Querying by Example Astronomical Archives 431
flow of bulk data
flow of commands and information
c)
DATABASE
DATA STORE
CLASSIFIER
populate the Data Store
populate the Database
archived images
a)
b)
Figure 1. Operational scenario for the data ingest phase of the
QUBE system
flow of bulk data
flow of commands and information
d)
DATABASE
DATA STORE
CLASSIFIER
QUBE
Interface
QUBE
Engine
template image
output image(s)
retrieval request retrieved image(s)
proximity search
archived images
a)
b)
c)
d)
Figure 2. Operational scenario for the data retrieval phase of the
QUBE system
4. QUBE ­ Structure and Operational scenario
The QUBE system is composed of:

432 Pasian and Smareglia
. the Archive (a Data Store containing the images, using optical media and
a jukebox system; a Database containing their descriptions, managed by
a relational DBMS);
. a Classifier (parametric or based on Artificial Neural Networks);
. the QUBE Search Engine;
. the QUBE User Interface.
The following operational scenario is envisaged, and is represented graphi­
cally in Figures 1 and 2, respectively:
Data ingest phase : When ingested in the Data Store (step a), the images
are also analyzed by a Classifier, either parametric or ANN­based, as in
Pasian et al. (1997) (step b). The result of the classification phase is
ingested in the Database (step c), together with the image parameters,
e.g., extracted from the FITS headers.
Data retrieval phase : A template image is given by the user as input to
the system (step a). The QUBE Search Engine re­scales it according to
the resolution of the archived images, possibly subsets it, and submits it to
the Classifier, obtaining the classification parameters (step b). A proximity
search is then performed on the Database (step c); the image(s) satisfying
the desired conditions are identified, retrieved from the Data Store and fed
to the user as output (step d).
More than one classifier may be envisaged to make the mechanism more flexible:
in this case, more than one table containing the classification parameters for the
archived images will be stored in the Database.
Acknowledgments. The authors are grateful to H.M.Adorf, O.Yu.Malkov,
J.D.Ponz and M.Pucillo for having discussions about the concepts of querying
image archives by example. The images used for the figures in this paper are
thumbnails of Hubble Space Telescope PR images.
References
Ardizzone, E., Di Ges‘u, V., & Maccarone, M. C. 1996, in Strategies and Tech­
niques of Information in Astronomy, Vistas in Astronomy, 40, 401
Lesteven, S., Poin›cot, P., & Murtagh, F. 1996, in Strategies and Techniques of
Information in Astronomy, Vistas in Astronomy, 40, 395
Pasian, F., Smareglia, R., Hantzios, P., Dapergolas, A., & Bellas­Velidis, I. 1997,
in: Wide­Field Spectroscopy, E.Kontizas, M.Kontizas, D.H.Morgan, G.
Vettolani eds., Kluwer Academic Publishers, 103