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CHAPTER 5 DATA FLOW STRUCTURE - Coggle Diagram
CHAPTER 5 DATA FLOW STRUCTURE
5.1 DEFINITION
System decomposed into modules.
Whole system as transformation of successive sets of data.
2 type :
5.2Batch-Sequential
Define
: each data transformation subsystem or module cannot start its process until its previous subsystem completes its computation.
Applicable domains of batch sequential architecture:
■ Data are batched.
■ Intermediate file is a sequential access file.
■ Each subsystem reads related input files and writes output files
Benefits
■ Simple divisions on subsystems.
■ Each subsystem can be a stand-alone program working on input
data and producing output data.
Limitations:
■ Implementation requires external control.
■ It does not provide interactive interface.
■ Concurrency is not supported and hence throughput remains low
■ High latency.
5.3Pipe and filter
There are three ways to make the data flow:
Push only (Write only) A data source may push data in a downstream.A filter may push data in a downstream.
Pull only (Read only) A data sink may pull data from an upstream.A filter may pull data from an upstream.
Pull/Push (Read/Write)
A filter may pull data from an upstream and push transformed data in a downstream.
There are two types of filters: active and passive.
■ An active filter pulls in data and pushes out the transformed data (pull/push); it works with a passive pipe that provides read/write mechanisms for pulling and pushing. The pipe and filter mechanism in Unix adopts this mode. The PipedWriter and PipedReader pipe classes in Java are also passive pipes that active filters must work with to drive the data stream forward
A passive filter lets connected pipes push data in and pull data out. It works with active pipes that pull data out from a filter and push data into the next filter. The filter must provide the read/write mechanisms in this case. This is very similar to data flow architecture
Applicable domains of pipe and filter architecture:
The system can be broken into a series of processing steps overdata streams, and at each step filters consume and move data incrementally.
The data format on the data streams is simple, stable, and adaptable if necessary.
Significant work can be pipelined to gain increased performance.
Producer or consumer-related problems are being addressed.
Benefits:
Concurrency: It provides high overall throughput for excessive data
processing
Modifiability: It features low coupling between filters, less impact from adding new filters, and modifying the implementation of any existing filters as long as the I/O interfaces are unchanged.
Simplicity: It offers clear division between any two filters connected by a pipe.
Flexibility: It supports both sequential and parallel execution.
Limitations:
It is not suitable for dynamic interactions.
A low common denominator is required for data transmission in
different formats, such as record type or XML type rather than
character type.
the ASCII formats since filters may need to handle data streams in
■ Overhead of data transformation among filters such as parsing is
repeated in two consecutive filters.
5.4 Process Control Architecture
suitable for the embedded system
software design where the system is manipulated by a process control variable data.
A process control system must have the following process control data:
Controlled variable: a target controlled variable such as speed in a cruise control system or the temperature in an auto H/A system. It has a set point goal to reach. The controlled variable data should be measured by sensors as a feedback reference to recalculate manipulated variables.
Input variable: a measured input data such as the temperature of
return air in a temperature control system.
Manipulated variable: can be adjusted by the controller.
Applicable domains of process control architecture
Embedded software systems involving continuing actions
Systems that need to maintain an output data at a stable level
The system can have a set point—the goal the system will reach at
its operational level