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

2.1 Integration Flows
efficient processing of simple integration tasks, complex integration tasks require many
indirections and thus, cannot be realized efficiently or not modeled at all. In addition,
from the perspective of flow semantics, we distinguish between data-flow- and controlflow-oriented
modeling [MMLW05].
Example 2.2. Figure 2.5 shows an example integration flow with both different flow semantics
(data flow and control flow): Figure 2.5(a) illustrates an example plan with dataflow
semantics, where we receive a message, execute two filters and finally, send the result
to two external systems. Here, the edges describe data dependencies between operators,
while temporal dependencies cannot be specified. For example, we cannot specify in which
temporal order to execute the two final writes. A specific characteristic is that if two or
more operators require a certain intermediate result, this intermediate result must be copied
in order to send it to both operators. In contrast, Figure 2.5(b) illustrates the same example
plan using the control-flow semantics. Here, the edges describe temporal dependencies,
while data dependencies are implicitly given by input and output variables (for clarity, illustrated
as dashed edges). Thus, the semantic of this integration flow additionally includes
the execution order of operators and does not require copy operations.
Write
Receive Selection Selection Copy
Receive Selection Selection Write Write
Write
var1
var2
var3
(a) Data-Flow Semantics
(b) Control-Flow Semantics
Figure 2.5: Integration Flow Modeling with Directed Graphs
Examples of integration flow modeling with directed graphs and control-flow semantics
are UML activity diagrams [OMG03] and BPMN process specifications [BMI06]. In
contrast, directed graphs in combination with data-flow semantics are commonly used by
traditional ETL tools. To summarize, control-flow semantics specify an integration flow
more precisely than pure data-flow semantics because the control-flow includes the data
flow and additional temporal dependencies. However, note that the implicit data flow
specification (beside the primary temporal dependencies) can cause semantic data flow
modeling errors such as lost or inconsistent data [TvdAS09].
In addition to this classification, further aspects of modeling integration flows—that
we will reveal in the following—are currently discussed in the literature. This includes
(1) the combination of control-flow and data-flow modeling (hybrid flow semantic), (2)
the combination of hierarchical and source code structure (hybrid flow structure), (3) the
model-driven development of integration flows, and (4) the declarative flow modeling.
A. Hybrid Flow Semantic
The strict distinction between data-flow and control-flow semantics has been considered
as a problem, especially, in the context of data-intensive integration flows that also require
rather complex procedural aspects. In consequence, two projects have addressed the combination
of data flow and control flow using hybrid modeling semantics, where the data
flow is modeled explicitly rather than only by input and output variables.
First, there is the concept of BPEL/SQL, where specific SQL activities can be used
within BPEL process specifications in order to combine the advantages of data-flow and
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