Dataflow Visual Programming Languages Research Articles

VisualTPL: A visual dataflow language for report data transformation

Data transformation, an important part of report generation, converts the layout of source data into a new layout suitable for presentation. Many report tools have been developed for end-users to specify data transformation. However, current report tools only support a limited set of report layouts. This paper proposes a visual dataflow programming language, called VisualTPL, to resolve this problem. Data transformation is accomplished by writing graphical dataflow programs, which manipulate tables as first-class objects with a set of extendable table operations. A report tool, called VisualTPS, has been developed to offer an easy and intuitive end-user programming environment. Reports with sophisticated layouts can be created through top-down decomposition and incremental development. An evaluation has been conducted to assess end-users' performance with VisualTPL. The results indicated that end-users could learn VisualTPL in a short time and create complicated report layouts all by themselves. And, in comparison with a commercial report tool, VisualTPL offered end-users similar performances and was preferred over the commercial tool.

An automated refactoring tool for dataflow visual programming language

Refactoring tools are extremely important to prevent errors and reduce consumed time. But these tools only support text-based languages, especially in Object Oriented languages. In order to accommodate the situation that Dataflow Visual Programming Languages (DFVPLs) are more and more popular, an automated refactoring tool for DFVPL was proposed. DFVPL was divided into two layers---visual layer and dataflow layer. Two layers mutually evolved, which ensured correctness of visual elements and dataflow semantic during refactoring. Some algorithms like middle graph generation; terminal auto-generation and terminal auto-modification were proposed to implement the changes of two layers. This tool has been proved correct and efficient by some refactoring instances. In addition, we discussed some famous DFVPL like LabVIEW to expose lacking of automated refactoring tool forced refactoring by hand to be time-consuming and error-prone.

Advances in dataflow programming languages

Many developments have taken place within dataflow programming languages in the past decade. In particular, there has been a great deal of activity and advancement in the field of dataflow visual programming languages. The motivation for this article is to review the content of these recent developments and how they came about. It is supported by an initial review of dataflow programming in the 1970s and 1980s that led to current topics of research. It then discusses how dataflow programming evolved toward a hybrid von Neumann dataflow formulation, and adopted a more coarse-grained approach. Recent trends toward dataflow visual programming languages are then discussed with reference to key graphical dataflow languages and their development environments. Finally, the article details four key open topics in dataflow programming languages.

Iteration constructs in data-flow visual programming languages

Many visual programming languages (VPLs) rely on the data-flow paradigm, probably because of its simple and intuitive functioning mechanism. However, there are cases where more powerful programming constructs are needed to deal with complex problems. For example, iteration is undoubtedly an important aspect of programming, and should allow repetitive behaviors to be specified in compact and easy ways. Most existing data-flow VPLs provide special constructs to implement iterations, therefore infringing the pure data-flow paradigm in favor of program simplicity. This paper has three main purposes: (1) To provide a survey of the mechanisms used by some representative data-flow VPLs to carry out iterations; (2) To investigate, given a pure data-flow VPL, what should be the minimum set of characteristics which, after being added to the VPL, allow iterations to be implemented; and (3) To show real data-flow iteration implementations which rely on the characteristics pertaining to such a minimum set.