Information Technology Reference
In-Depth Information
commonly used BP models and notations focus on the control flow and the tim-
ing of activities in the BP. As a consequence, in most BP models, data (e.g.,
documents, reports, invoices, emails and the like) play a secondary role, just as
inputs or outputs of the activities of the process.
Nevertheless, understanding and analysing how data is modified during the ex-
ecution of a BP is getting an increased interest from both industry and academy.
For instance, BPMN, the de-facto standard for BP modelling, has incorporated
more advanced constructs for data management in its last version [1]. In addi-
tion, there is an increasing number of research proposals to analyse the way data
is used in a BP to detect anomalies [2,3,4] and to define data-aware compliance
rules [5] for BPs. Therefore, providing a mechanism to transform from the usual
activity-centered view of a BP to a data-centered view that focuses on the data
handled during the process is very appealing to this goal of understanding and
analysing how data is modified during the execution of a BP.
In this paper we describe a model-driven procedure based on Petri nets for
carrying out this transformation automatically. In particular, the input of the
procedure is a BP diagram expressed in BPMN 2.0 (cf. Figure 1). We use this
notation because it is the de-facto standard for BP modelling. Such diagrams
represent data objects connected to the BP activities that use them either to
read them or write them, or for both things. A data object has a
type
and can
have one or more
states
along the execution of a process. For instance, in the BP
of opening a bank account, the data object
application
filled by the new customer
could go through states
sent
,
accepted
and
stored
. The output of the procedure
is a data-centered view composed of the set of object life cycles (OLCs) of all the
data objects that are involved in a BP. They represent the allowed transitions
between the states of the data object according to the BP diagram. In addition,
these transitions also include information about the activities of the BP that
are executed in the transition between states of the data object (cf. Figure 2).
Furthermore our procedure also deals with some data anomalies that may appear
in a BP model (cf. Section 4 for more details).
Our approach has the following advantages: (i) it is fully automated; (ii) it
is based on Petri nets, which allows us to use ecient and well-tested Petri
net algorithms; (iii) since it includes information about the activities that are
executed in each transition, it provides the same full information required to
understand BP execution as activity-centered process diagrams; and (iv) it is
robust in the sense that it provides an accurate data-centered view despite having
a BP with data anomalies as input. Moreover, it informs the user about these
data anomalies.
The remaining of the paper is organised as follows. Section 2 introduces a
use case used to exemplify the output produced by the procedure. Section 3
contains the description of the whole procedure for OLC generation. In Section
4 the detection and handling of data anomalies is introduced. Section 5 contains
a summary of related work and in Section 6 we draw a set of conclusions and
outline some future work.
