| description |
In order to optimize their revenues and profits, an increasing
number of businesses organize their business activities in terms of
business processes. Typically, they automate important business
tasks by orchestrating a number of applications and data stores.
Obviously, the performance of a business process is directly
dependent on the efficiency of data access, data processing, and
data management.
In this paper, we propose a framework for the optimization of data
processing in business processes. We introduce a set of rewrite
rules that transform a business process in such a way that an
improved execution with respect to data management can be achieved
without changing the semantics of the original process. These
rewrite rules are based on a semi-procedural process graph model
that externalizes data dependencies as well as control flow
dependencies of a business process. Furthermore, we present a
multi-stage control strategy for the optimization process. We
illustrate the benefits and opportunities of our approach through a
prototype implementation. Our experimental results demonstrate that
independent of the underlying database system performance gains of
orders of magnitude are achievable by reasoning about data and
control in a unified framework.
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