Softwarekartographie

Zusammenfassung  Anwendungslandschaften in Unternehmen sind langlebige hoch-komplexe Strukturen bestehend aus hunderten bis tausenden von miteinander vernetzten betrieblichen Informationssystemen, die von Personen mit sehr unterschiedlichen Interessen und Erfahrungshintergrund konzipiert, erstellt, modifiziert, betrieben, genutzt und finanziert werden. Die Softwarekartographie zielt darauf ab, die Kommunikation zwischen diesen Personen durch zielgruppenspezifische verst?ndliche graphische Visualisierungen zu unterstützen, die Gesch?fts- und Informatik-Aspekte gleicherma?en berücksichtigen, und die speziell für langfristige und strategische Management-Betrachtungen geeignet sind. In diesem Beitrag fassen wir zun?chst die in der betrieblichen Praxis wohlbekannten Probleme beim Management von Anwendungslandschaften zusammen und diagnostizieren erhebliche Kommunikationsdefizite. Da Anwendungslandschaften als Systeme von Systemen mit Menschen als integrale Systembestandteile zu verstehen sind, werden Karten als attraktiver L?sungsansatz identifiziert und die wesentlichen Konzepte der Softwarekartographie anhand von Beispielen vorgestellt. Dabei werden Querbezüge zu anderen Fachgebieten der Informatik und Wirtschaftsinformatik hergestellt, und die bisherige Nutzung der Forschungsergebnisse in der Praxis gezeigt.     

Self-adjusting the intensity of assortative mating in genetic algorithms

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.     

Analysis of the Datagram Congestion Control Protocol’s connection management procedures using the sweep-line method

State space explosion is a key problem in the analysis of finite state systems. The sweep-line method is a state exploration method which uses a notion of progress to allow states to be deleted from memory when they are no longer required. This reduces the peak number of states that need to be stored, while still exploring the full state space. The technique shows promise but has never achieved reductions greater than about a factor of 10 in the number of states stored in memory for industrially relevant examples. This paper discusses sweep-line analysis of the connection management procedures of a new Internet standard, the Datagram Congestion Control Protocol (DCCP). As the intuitive approaches to sweep-line analysis are not effective, we introduce new variables to track progress. This creates further state explosion. However, when used with the sweep-line, the peak number of states is reduced by over two orders of magnitude compared with the original. Importantly, this allows DCCP to be analysed for larger parameter values. Somsak Vanit-Anunchai was partially supported by an Australian Research Council Discovery Grant (DP0559927) and Suranaree University of Technology. Guy Edward Gallasch was supported by an Australian Research Council Discovery Grant (DP0559927).     

Real-time measurement of pointing action by using DSP

This paper presents an approach of measuring in real-time the vector of finger that is pointing to an object. DSP is used in the operation processing unit in order to do the real-time processing. The steps include the extraction of flesh-colored regions from an image, the labeling of the flesh-colored regions, and the detection of two characteristic positions on the finger so that the direction that the finger is pointing at will be calculated. The entire process takes about 29 msec, which makes it possible to have the frame rate of 34 fps. With this frame rate, this measurement approach is considered real-time and promising to be merged into other application systems. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008