SEKE 2012 Proceedings - Knowledge Systems Institute

SciprovMiner: Provenance Capture using the OPM
Model
Tatiane O. M. Alves, Wander Gaspar, Regina M. M.
Braga, Fernanda Campos
Master's Program in Computer Science
Department of Computer Science
Federal University of Juiz de Fora, Brazil
{tatiane.ornelas, regina.braga,
fernanda.campos}@ufjf.edu.br, andergaspar@gmail.com
Abstract—Provide historical scientific information to deal with
loss of knowledge about scientific experiment has been the focus
of several researches. However, the computational support for
scientific experiment on a large scale is still incipient and is
considered one of the challenges set by the Brazilian Computer
Society for the period 2006 to 2016. This work aims to contribute
in this area, presenting the SciProvMiner architecture, which
main objective is to collect prospective and retrospective
provenance of scientific experiments as well as apply data mining
techniques in the collected provenance data to enable the
discovery of unknown patterns in these data.
Keywords-web services; ontology; provenance; OPM; OPMO;
data mining.
I. INTRODUCTION
The large-scale computing has been widely used as a
methodology for scientific research: there are several success
stories in many domains, including physics, bioinformatics,
engineering and geosciences [1].
Although scientific knowledge continue to be generated in
a traditional way, in-vivo and in-vitro, in recent decades
scientific experiments began to use computational procedures
to simulate their own execution environments, giving origin to
in-virtuo scientific experimentation. Moreover, even the
objects and participants in an experiment can be simulated,
resulting in in-silico experimentation category. These largescale
computations, which support a scientific process, are
generally referred to as e-Science [1].
The work presented in this article, SciProvMiner, is part of
a project developed in Federal University of Juiz de Fora
(UFJF), Brazil, with emphasis on studies to building an
infrastructure to support e-Science projects and has as its main
goal the specification of an architecture for the collection and
management of provenance data and processes in the context
of scientific experiments processed through computer
simulations in collaborative research environments
geographically dispersed and interconnected through a
computational grid. The proposed architecture must provides
an interoperability layer that can interact with SWfMS
(Scientific Workflows Management Systems) and aims to
capture retrospective and prospective provenance information
generated from scientific workflows and provides a layer that
Marco Antonio Machado, Wagner Arbex
National Center for Research in Dairy Cattle
Brazilian Enterprise for Agricultural Research -
EMBRAPA
Juiz de Fora, Brazil
{arbex, machado}@cnpgl.embrapa.br
allows the use of data mining techniques to discover important
patterns in provenance data.
The rest of the paper is structured as follows: In Section 2
we present the theoretical foundations that underpin this work.
Section 3 describes some related work. Section 4 presents the
contributions proposed in this work, the architecture of
SciprovMiner is detailed, showing each of its layers and
features. Finally, section 5 presents final considerations and
future work.
II. CONCEPTUAL BACKGROUND
Considering scientific experimentation, data provenance
can be defined as information that helps to determine the
historical derivation of a data product with regards to its
origins. In this context, data provenance is being considered an
essential component to allow reproducibility of results, sharing
and reuse of knowledge by the scientific community [2].
Considering the necessity of providing interoperability in
order to exchange provenance data, there is an initiative to
provide a standard to facilitate this interoperability. The Open
Provenance Model (OPM) [3] defines a generic representation
for provenance data. The OPM assumes that the object (digital
or not) provenance can be represented by an annotated
causality graph, which is a directed acyclic graph, enriched
with notes captured from other information related to the
execution [3].
In the OPM model, provenance graphs are composed of
three types of nodes [3]:
• Artifacts: represent an immutable state data, which
may have a physical existence, or have a digital
representation in a computer system.
• Processes: represent actions performed or caused by
artifacts, and results in new artifacts.
• Agents: represent entities acting as a catalyst of a
process, enabling, facilitating, controlling, or affecting
its performance.
Figure 1 adapted from [3], illustrates these three entities
and their possible relationships, also called dependencies. In
Figure 1, the first and second edge in the right column express
that a process used an artifact and that an artifact was
generated by a process. These two relationships represent data
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