Data-flow graph

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Data-flow graph (DFG) is a graphical representation that models the flow of data dependencies within a program or system. It consists of nodes representing operations or computations and edges representing the flow of data between these operations.

In a DFG, each node typically performs a specific computation or transformation on input data and produces output data. The edges indicate the dependencies between operations, showing how data produced by one operation is used as input for another operation. DFG's are commonly used in various fields, including compiler optimization, program analysis, parallel computing, and machine learning. They provide a visual way to understand the flow of data through a system, which can aid in optimizing performance, identifying dependencies, and parallelizing computations.

Technical details

Data Movement: The DFG illustrates the pathways through which data flows within the system. This highlights the connections between different processing elements and the direction of data propagation.

Process Inputs and Outputs: The DFG clarifies the inputs required by each process within the system and the outputs it produces. This understanding of data exchange is crucial for analyzing system behavior.

Absence of Control Flow

A defining characteristic of DFGs is the absence of control flow. This signifies that DFGs do not explicitly represent decision-making logic or loops within the system. They focus solely on the movement of data between processes. For depicting control flow aspects, techniques like flowcharts are more suitable.

Notation and History

There are various notations employed for constructing DFGs. A widely used notation was introduced by Tom DeMarco in 1979 as part of the structured analysis methodology. This notation employs specific symbols to represent processes, data stores, and data flows, enabling a clear visual representation of the system's data flow.

Data Flow Constraints

Endpoint Requirement: Within a DFG, for every data flow depicted, at least one of its endpoints (source or destination) must reside within a process. This ensures that data flows originate from or terminate at a processing element within the system.

Process Refinement: DFGs offer a hierarchical representation possibility. A complex process within a DFG can be further detailed by creating a separate, nested DFG that decomposes the process into its constituent sub-processes. This allows for a more granular view of data flow within that specific process.

Applications and Relationships

Structured Analysis and Data Modeling: DFGs play a significant role in structured analysis, a methodology for developing information systems. They also serve as valuable tools in data modeling, where they aid in visualizing the relationships between data entities and their attributes.

Unified Modeling Language (UML): In the context of UML, a standardized language for software modeling, the activity diagram often takes precedence over the DFG. Activity diagrams offer a more comprehensive view that incorporates both data flow and control flow aspects.

Site-Oriented Data-Flow Plans: A specific type of DFG known as a site-oriented data-flow plan caters to the modeling needs of geographically dispersed systems.

Relationship to Petri Nets

An interesting connection exists between DFGs and Petri nets, a mathematical tool for modeling concurrent systems. DFGs can be viewed as inverted Petri nets. In this analogy, places within a Petri net correspond semantically to data stores in a DFG, while transitions in a Petri net and data flows/functions in a DFG can be considered functionally equivalent.

In essence, data-flow graphs offer a valuable visual aid for understanding and communicating the flow of data within a system. Their focus on data movement and their ability to be hierarchically decomposed make them a versatile tool in system modeling.

See also

Control-flow graph