Erkki Mäkinen

Automatic synthesis of state machines from trace diagrams

The automatic synthesis of state machines describing the behaviour of a class of objects in object‐oriented software modelling is studied. It is shown that the synthesis can be carried out on the basis of trace diagrams giving possible sequences of events during the execution of the system. An algorithm originally developed for the automatic construction of programs on the basis of their execution traces is applied to the problem, and an experimental state machine synthesizer is implemented. It is demonstrated that such a synthesizer is a highly useful component in a practical object‐oriented CASE system.

Constructing and reconstructing the reorderable matrix

We consider the backgrounds, applications, implementations, and user interfaces of the reorderable matrix originally introduced by Jacques Bertin. As a new tool for handling the matrix, we propose a new kind of interface for interactive cluster analysis. As the main tool to order the rows and columns, we use the well-known barycenter heuristic. Two user tests are performed to verify the usefulness of the automatic tools.

How to draw a hypergraph

There is an increasing amount of applications in computer science and other fields in which hypergraphs are used. This paper shows that in many cases the problem of drawing a hypergraph can be reduced to the problem of drawing normal graphs. This holds true especially when considering hypergraphs drawing in the edge standard, i.e. when the hyperedges connecting the vertices are drawn as curves.

Reordering the Reorderable Matrix as an Algorithmic Problem

The Reorderable Matrix is a visualization method for tabular data. This paper deals with the algorithmic problems related to ordering the rows and columns in a Reorderable Matrix. We establish links between ordering the matrix and the well-known and much studied problem of drawing graphs. First, we show that, as in graph drawing, our problem allows different aesthetic criterions which reduce to known NP-complete problems. Second, we apply and compare two simple heuristics to the problem of reordering the Reorderable Matrix: a two-dimensional sort and a graph drawing algorithm.

Genetic Synthesis of Software Architecture

Design of software architecture is intellectually one of the most demanding tasks in software engineering. This paper proposes an approach to automatically synthesize software architecture using genetic algorithms. The technique applies architectural patterns for mutations and quality metrics for evaluation, producing a proposal for a software architecture on the basis of functional requirements given as a graph of functional responsibilities. Two quality attributes, modifiability and efficiency, are considered. The behavior of the genetic synthesis process is analyzed with respect to quality improve ment speed, the effect of dynamic mutation, and the effect of quality attribute prioritization. Our tests show that it is possible to genetically synthesize architectures that achieve a high fitness value early on.

A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks

A wireless ad hoc sensor network consists of a number of sensors spreading across a geographical area. The performance of the network suffers as the number of nodes grows, and a large sensor network quickly becomes difficult to manage. Thus, it is essential that the network be able to self-organize. Clustering is an efficient approach to simplify the network structure and to alleviate the scalability problem. One method to create clusters is to use weakly connected dominating sets (WCDSs). Finding the minimum WCDS in an arbitrary graph is an NP-complete problem. We propose a neural network model to find the minimum WCDS in a wireless sensor network. We present a directed convergence algorithm. The new algorithm outperforms the normal convergence algorithm both in efficiency and in the quality of solutions. Moreover, it is shown that the neural network is robust. We investigate the scalability of the neural network model by testing it on a range of sized graphs and on a range of transmission radii. Compared with Guha and Khullers centralized algorithm, the proposed neural network with directed convergency achieves better results when the transmission radius is short, and equal performance when the transmission radius becomes larger. The parallel version of the neural network model takes time O(d) , where d is the maximal degree in the graph corresponding to the sensor network, while the centralized algorithm takes O(n 2). We also investigate the effect of the transmission radius on the size of WCDS. The results show that it is important to select a suitable transmission radius to make the network stable and to extend the lifespan of the network. The proposed model can be used on sink nodes in sensor networks, so that a sink node can inform the nodes to be a coordinator (clusterhead) in the WCDS obtained by the algorithm. Thus, the message overhead is O(M), where M is the size of the WCDS.

On the subtree isomorphism problem for ordered trees

This note deals with the subtree isomorphism problem: Given two rooted trees T 1 and T 2 , decide whether T 1 is isomorphic to any subtree of T 2 . We show that a O(m+n) time algorithm can be obtained when restricting ourselves to ordered trees only. Our algorithm is based on tree encoding and on string pattern matching

Learning deterministic even linear languages from positive examples

Abstract We consider the problem of learning deterministic even linear languages from positive examples. We show that, for any nonnegative integer k , the class of LR ( k ) even linear languages is not learnable from positive examples while there is a subclass called LRS ( k ), which is a natural subclass of LR ( k ) in the strong sense , learnable from positive examples. Our learning algorithm identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even linear grammars, we have a learning algorithm for k -reversible languages that is more efficient than the one proposed by Angluin.

On circular layouts

This paper deals with the circular versions of the linear cutwidth and linear dilation minimization problems. We show that also the circular variants of the problems are NP-complete. Moreover, we propose a method for drawing graphs. This method is based on the use of circular layouts.

Genetic algorithms for drawing bipartite graphs

This paper introduces genetic algorithms for the level permutation problem (LPP). The problem is to minimize the number of edge crossings in a bipartite graph when the order of vertices in one of the two vertex subsets is fixed. We show that genetic algorithms outperform the previously known heuristics especially when applied to low density graphs. Values for various parameters of genetic LPP algorithms are tested.