This paper concerns the relationship between rough sets and flow graphs. It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure. This description is employed next for finding patterns in data. To this end decision algorithm induced by the flow graph is defined and studied.

When analyzing programs with parallel imperative constructs (e.g., cobegin/coend), standard computer intermediate representations are inadequate. This paper introduces a new relation called the precedence relation and new data structures called the Pamllel Control Flow Graph and Parallel Precedence Graphs for programs with parallel constructs. We show how to report anomalies in parallel program...

This paper presents a solution to the Flowgraphs case study for the Transformation Tool Contest 2013 (TTC 2013). Starting from Java source code, we execute a chain of model transformations to derive a simplified model of the program, its control flow graph and its data flow graph. Finally we develop a model transformation that validates the program flow by comparing it with a set of flow specif...

In this paper we propose a new approach to data mining and knowledge discovery based on information flow distribution in a flow graph. Flow graphs introduced in this paper are different from those proposed by Ford and Fulkerson for optimal flow analysis and they model flow distribution in a network rather than the optimal flow which is used for information flow examination in decision algorithm...

We define a graph-based visual query language for object databases, called QUIVER. Most graph-based query languages proposed in the literature view queries either as constraint (pattern) graphs or as data-flow graphs. QUIVER, however, allows constraints and data-flow to be combined in queries. This combination of constraints and data-flow is motivated by the need to support comprehensive object...

Abstract — Digital signal processing algorithms are repetitive in nature. These algorithms are described by iterative data-flow graphs where nodes represent computations and edges represent communications. For all data-flow graphs, there exists a fundamental lower bound on the iteration period referred to as the iteration bound. Determining the iteration bound for signal processing algorithms d...

Abstract. Digital signal processing algorithms are repetitive in nature. These algorithms are described by iterative data-flow graphs where nodes represent computations and edges represent communications. For all data-flow graphs, there exists a fundamental lower bound on the iteration period referred to as the iteration bound. Determining the iteration bound for signal processing algorithms de...

A control flow graph represents all the flows of control that may arise during program execution. Since exception mechanism in Java induces flows of control, exception induced control flow have to be incorporated in control flow graph. In the previous research to construct control flow graph, they compute exception flow and normal flow at the same time while computing control flow information. ...

Aggressive compilers employ a larger number of well understood optimizations in the hope of improving compiled code quality. Unfortunately, these optimizations require a variety of intermediate program representations. A first step towards unifying these optimizations to a common intermediate representation is described. The representation chosen is the program dependence graph, which captures ...