Data-Driven Processes: Case Handling:

Case handling aims at balancing process

orientation with data orientation to
control the execution of business processes.

The motivation can be derived
from business process reengineering, because

one of its main goals is to overcome
the fragmentation of the work in

organizations.
The introduction of this fragmentation of work

was useful in manufacturing
since the early days of industrialization,

where it led to massive increases in
productivity, because highly specialized

workers perform isolated pieces of
work with high efficiency. Once the worker has

finished a piece of work, the
manufactured artefact is handed over to the

next worker in line.
The fragmentation of work has been transferred

to the information society.
Workers are expected to conduct a single piece

of work in a highly efficient
manner, without a complete picture on the

contribution of the work to the company�s

goals. To control the combination of the

fragmented work, complex
organizational structures have been invented.
With the presence of information technology,

the role of workers has
changed. Now the knowledge worker is at the

centre, responsible for conducting
and organizing her work. The knowledge worker

is highly skilled, so
she can conduct a broad range of activities

required to fulfil business goals of
the company. An insurance claim, for example,

can be processed by a single
person, so that handover of work can be

avoided. Only in specific, seldom
occurring cases is expert support required.
Case handling takes into account this active

role of the knowledge worker
by accepting her expertise and experience to

drive and control the case. Since
traditional workflow technology prescribes the

activities and their execution
ordering, there is little room for knowledge

workers to deviate from the prescribed
process. As a result, traditional workflow

technology appears too restrictive
in these settings.
However, there is still support that flexible

business process management
systems can provide. Since knowledge-intensive

business processes typically
are centred on data processed in the context

of a particular case, the handling
of data requires specific attention.
A case is a product that is manufactured, and

at any time knowledge workers
should be aware of the overall case. Examples

of cases are the evaluation
of a job application, the verdict on a traffic

violation, the outcome of a tax
assessment, and the ruling for an insurance

claim. To illustrate the basic ideas of case

handling, consider the activities A and
B of a business process that are ordered by

control flow A ! B. As a result,
B can only be enabled (and therefore can only

start) after A has terminated.
This type of ordering constraint is a key

ingredient of business process
management in general and workflow management

in particular. While in
many business process scenarios this

traditional workflow approach is adequate,
in knowledge-intensive domains, where an

active role of the knowledge
worker drives the process, more flexible

approaches are required.
For instance, assume that A does not create

its data on termination, but
while it runs. Assume further that B can start

working once data values
created by A are available. Then, B can start

working on these data, while
A creates the remaining data values. In this

case, the control flow constraint
between A and B restricts a useful execution

ordering, in which B starts
working before A completes.
One could argue that the level of granularity

of the modelled activities
might not be adequate. If the generation of

each data value is represented by
a single activity in a business process, then

the same process instances can
be achieved. However, since the number of

activities would become very high,
complex process models that are hard to

understand and maintain would
result.