=Paper= {{Paper |id=None |storemode=property |title=Combining Process and Ontological Modeling |pdfUrl=https://ceur-ws.org/Vol-926/paper9.pdf |volume=Vol-926 |dblpUrl=https://dblp.org/rec/conf/aiia/SolomakhinTM12 }} ==Combining Process and Ontological Modeling== https://ceur-ws.org/Vol-926/paper9.pdf

Combining Process and Ontological Modeling

Dmitry Solomakhin
Supervisors: Sergio Tessaris, Marco Montali

Faculty of Computer Science, Free University of Bozen-Bolzano
Piazza Domenicani 3 – 39100 Bolzano, Italy
solomakhin@inf.unibz.it

1 Introduction and Motivation
Recent development of information technology has significantly affected the way
how an enterprize operate. Nowadays in corporate information systems not only
process automation but also dealing with all stages of the business process life-
cycle becomes increasingly important. Relevant tasks cover not only issues to
be tackled on the design phase (e.g. process modeling, simulation, verification of
different properties of a process, etc) but also problems arising in the phases of
execution and analysis (such as process mining, monitoring, etc). All these tasks
are the central issues of Business Process Management (BPM).
In order to enable automated reasoning support for processes along their
entire lifecycle, several mathematical formalisms have been adopted to repre-
sent processes as formal models, including transition systems, process algebras
and, finally, Petri nets. However, the well-known and avowed disadvantage of
the family of these approaches, often referred to as process-centric, is that al-
though they capture the workflow of the process itself, most of them abstract
away from the semantics of data which a process might operate with. Nowa-
days such data is usually of a very complex structure due to the nature of
information to be described (e.g. logistics, sales, etc.). Therefore, dealing with
such data requires powerful tools even for static analysis. Moreover, in real life
business processes usually require data integration because data may originate
from heterogeneous sources. With respect to the importance of data integration
many process modeling methodologies do not provide appropriate conceptual
paradigms for specifying and enacting these kinds of tasks [7].
In response to such a drawback of traditional process-centric BPM tech-
niques, a data-centric business modeling has recently emerged as a methodology
in which processes are considered to be driven with the possible changes and
evolutions of business data objects, called artifacts. This approach has become
an area of growing interest, since it has been argued that considering data-
centric perspective in business modeling can lead to substantial cost savings in
the design and deployment of business processes [2].
Following the current trend of knowledge-aware business process modeling,
in our research we address a problem of merging the process-related model-
ing techniques with ontological modeling and semantic technologies in general.
The final goal would be providing a logical/formal framework which allows for
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modeling of business processes tightly coupled with the manipulated dynamic
data, as well as for reasoning about and verification of different logical proper-
ties of such system. Such synthesis of models incorporating both a static and
dynamic perspective, if exists, will require a very challenging task on defining
algorithms for reasoning tasks, e.g. model checking, since in general that might
lead to infinite-state models. Hence, not only new model checking algorithms
have to be invented, but also decidable fragments of this combination should be
investigated, mediating between relevance in practice and tractability [4].

2 The context of the research

The problems that are to be tackled along the research line can be considered
relevant and useful in the context of the ACSI project [2], which is devoted to
investigation on how the artifact-based approach may be used to optimize the
business process management in the enterprize. The paradigm adopted there
is presented in Figure 1 and consists of three layers: realization, artifact and
semantics. The planned PhD research shares this paradigms and is supposed
to focus on the semantic layer, i.e. to investigate the integration between the
knowledge base describing the semantics of the data (ontologies) and high-level
description of the business processes.

Fig. 1. ACSI Artifact Paradigm [2]

3 Current work

The recently introduced Guard-State-Milestone (GSM) artifact modeling lan-
guage [6] provides means for specifying business artifacts lifecycles in a declar-
ative manner, using intuitively natural constructs that correspond closely to
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how business-level stakeholders think about their business. The corresponding
constructs are:
– Information model for modeling relevant data domain.
– Milestones, which naturally correspond to business operational objectives
and are achieved based on triggering events and/or conditions over the in-
formation model.
– Stages, which correspond to clusters of activities intended to achieve mile-
stones and which can have a hierarchical structure.
– Guards, which control when a stage can be activated.
As an example, let’s consider
√ a process F unc which is as simple as calculating a
square root of a sum a + b, given that a 6= b and a + b ≥ 0. The GSM concrete
model of such process is represented on the Figure 2.

√
Fig. 2. GSM model of a+b

Both milestones and guards are controlled in a declarative manner and corre-
sponding definitions will have the following form:

ge1 : on x.F unccall (a, b) if a 6= b ge2 : on + x.m1
return
m
e 1 : on x.Sum (c) if c ≥ 0 m
e 2 : if c < 0
return
m
e 3 : on x.Sq (d)

Despite having a formally specified operational semantics for GSM models [3],
the verification of different properties of such models (e.g. existence of complete
execution, safety properties) is still an open problem. In order to solve this
problem, one should define a particular formalism that captures the intended
operational semantics of the business artifacts and provides mechanisms to solve
different verification tasks.
One of the most promising candidates for such a formalism is a data-centric
dynamic system (DCDS) together with its general verification framework pre-
sented in [5]. A DCDS is a pair S = hD, Pi, where D is a data layer and P is a
process layer over the former.
The data layer D models the relevant database schema together with its set
of integrity constraints, while the process layer P is a tuple P = hF , A, ̺i, where
– F is a finite set of functions representing interfaces to external services.
46

– A is a set of actions of the specific form:

α(p1 , ..., pn ) : {e1 , ..., em }, where p1 , ..., pn are input parameters of an action
and ei = qi+ ∧ Q−
i Ei are effects of an action of a particular form.

– ̺ is a process which is a finite set of condition-action rules of the form
Q 7→ α, where α is an action and Q is a FO query over R.
The decidability problem in the context of data-centric dynamic systems is
one of the main challenges being investigated at the moment. However, unlike
GSM, which has emerged to satisfy practical needs, DCDS benefits from having
purely formal foundations, which provide instruments to approach and solve the
challenge. Several decidability results have been obtained by Calvanese et. al [1]
during their ongoing research. Therefore, it becomes of a particular interest to
investigate the possibility to transfer these decidability results on GSM models.
Having a formal definition of an artifact and its lifecycle as a GSM concrete
model, we aim to define a mapping which maps the artifact’s relational schema
into the data layer of DCDS and the set of ECA-like rules describing its behavior
into the set of condition-action rules of the process layer of DCDS, where service
calls are modeled by a finite set of functions F .
Along the process of constructing the mapping we need to insure that the
resulting DCDS model mimics the operational semantics of the initial GSM
model. In particular, one would want to preserve the semantics of so-called B-
Steps, which focus on what happens to a snapshot (i.e., description of all relevant
aspects of a GSM system at a given moment of time) when a single incoming
event is incorporated into it. In order to capture the semantics of B-Steps, we
construct a so-called conditional dependency graph, which is then used to enforce
the shape of the resulting condition-action rules in such a way that the final
DCDS formalization may be, in fact, considered as an execution engine for the
initial GSM model.
For example, assume a stage sj and some guard gje = on ξ(x) if φ(x) which
opens the stage. Then activating the stage by validating gje can be modeled by
the following condition-action rule:

∃a, s, m Ratt (x, a, s, m) ∧ sj = f alse ∧ RBlocked (x, f alse) 7→
αActivate
M,sj (idR , a′1 , ..., a′m ) : {
Ratt (idR , a, s, m) Ratt (idR , a, s, m)[sj /true, a1 /f M (1), ..., ak /f M (k)]
Ratt (idR , a, s, m) RM (idR , f M (1), ..., f M (k))
Ratt (idR , a, s, m) RBlock (idR , true)
Ratt (x, a, s, m) RsStateChanged
j
(x, true)}

4 Future work and concluding remarks
The results of the ongoing research are at the moment considered to be prelim-
inary and subject to further investigation. In particular, one of the main future
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tasks is verifying that the introduced translation from GSM model specification
into DCDS specification is consistent with respect to a certain family of the pro-
cess properties. This is going to be done by attempting to define a bisimulation
relation between two transition systems, inferred by the semantics of GSM and
DCDS respectively. Another task is devoted to investigating the possibility to
transfer the existing decidability results for DCDS [1] to GSM and to determine
expressivity restrictions corresponding to those defined for DCDS.
Other future tasks in the context of the ACSI project include: a) determining
the use cases for the semantic layer in the ACSI Artifact paradigm, which tasks
can be (or should be) dealt with on this layer; b) attempting to define a ”semantic
concrete model” which would be an ”implementation” of a semantic layer of the
ACSI Artifact Abstract Model, or more specifically, how to complement a GSM
Concrete Model with some notion representing the semantic layer.
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