![](bgb.png)
To overcome these issues, this work proposes a
combination of event models that takes advantage of
two complementary approaches. The iWISE
(Costello, 2008) event model features cross-
functional event sequences and permits the
framework to be a non-BPEL exclusive dependent
system. The BPAF model (WfMC, 2009), in
contrast, provides powerful capabilities for enabling
the analysis of business processes
behaviour.
In the absence of standards for querying business
processes, a query language has been proposed. The
successful implementation and evaluation of the
prototype has demonstrated that it is possible to
monitor and query the structural and behavioural
properties of business processes through the
construct of a general purpose event model.
Moreover, the business data can be unified and
centralized seamlessly regardless of the underlying
source systems.
In future works, the BPEQL grammar will be
extended to improve its expressive power.
Additionally, its usability will also be improved by
incorporating references to business data without
using identifiers, so that query construction will be
significantly eased.
The framework is sufficiently flexible to
incorporate easily the extensions mentioned above.
There are plenty of possibilities for incorporating
metrics and key performance indicators (KPI)
without affecting the normal functionality of the
existing system. Consequently, the BPEQL grammar
can also be improved by incorporating these new
elements gradually, thus improving the power and
expressiveness of the language.
Other potential further research using the
framework includes support for predictive analysis
and integration with simulation and optimisation
techniques and systems. This would pave the way
for enabling the user to augment existing data with
hypothetical information in order to perform what-if
analysis over simulated scenarios.
Behavioural patterns recognition is another
technique that could be leveraged by the proposed
system in order to detect undesirable business
process behaviours that are experienced frequently
or on a continuous basis.
On a final note, it worth noting that event data
centralization is not the only option to store and
analyse distributed business data. Handling
collaborative analytics on a fully distributed BI
environment is a challenging task. Nonetheless, this
work could be complemented with the federative
approach, presented in (Rizzi, 2012), in terms of
data warehousing and distributed query processing.
The BI subsystem component presented in this paper
could be attached to every operational business
system along with their own local event repository.
The event-based model presented herein represents
the global schema proposed by Rizzi’s approach.
Thus, business process analytics could be carried out
collaboratively in each organization independently
by performing distributed queries along the
collaborative network.
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