Cost-Based Optimization of Integration Flows - Datenbanken ...

2 Preliminaries and Existing Techniques
As preliminaries, in this chapter, we survey existing techniques in order to give a comprehensive
overview of the state-of-the-art of optimizing integration flows. We start with a
classification of integration approaches. Subsequently, we generalize the system architecture
of typical integration platforms and review the modeling, execution and optimization
of integration flows. Moreover, we also classify approaches of cost-based optimization and
adaptive query processing in different system categories. Furthermore, we introduce the
used notation of integration flows including their transactional requirements and we define
the running example integration flows of this thesis.
2.1 Integration Flows
Integration (from lat. integer = complete) refers to the assembly of many parts to a
single composite. In computer science, integration is used in the sense of combining local
integration objects (systems, applications, data or functions) with a certain integration
technology. For several reasons, which we will reveal in this section, the integration and
interoperability of heterogeneous and distributed components, applications, and systems
is one of the broadest and most important research areas in computer science.
Historically, database technology itself was introduced with the goal of integrated enterprise
data management in the sense of so-called enterprise databases [Bit05]. Unfortunately,
the comprehensive and future-oriented database design over multiple application
programs has not been achieved. As a result, the current situation is that enterprise data
is inherently distributed across many different systems and applications. In this context,
we often observe the problems of (1) non-disjointly distributed data across these systems,
(2) heterogeneous data representations, and (3) data propagation workflows that are implicitly
defined by the applications. Despite the permanent goal of homogeneity and the
trend towards homogeneous services and exchange formats, those problems will also remain
in the future due to the diversity of application requirements, performance overheads
for ensuring homogeneity (e.g., XML exchange formats), continuous development of new
technologies that always cause heterogeneity with regard to legacy technologies, as well
as organizational aspects such as autonomy and privacy. In consequence, data must be
synchronized across those distributed and heterogeneous applications and systems.
This historically reasoned situation of enterprise data management by itself leads to
the major goals of integration. First, the distributed and heterogeneous data causes the
requirement of data consolidation and homogeneity in order to provide a consistent global
view over all operational systems. Second, non-disjointly distributed data and implicit
data propagation workflows reason the need for interoperability in terms of interactions
and data synchronization between autonomous systems and applications. Third, in order
to achieve availability (high availability and disaster recovery) as well as performance
and scalability (location-based access, load balancing, and virtualization) technically distributed
subsystems must be integrated and synchronized as well.
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