Docencia práctica en los laboratorios de las ingenierías de informática apoyada en herramientas CASE.

Aceptado para su publicación en las actas del Segundo Taller de trabajo en Ingeniería del Software basada en componentes distribuidos ISCDISï01. En colaboración con VI Jornadas de Ingeniería de Software y Bases de Datos (Almagro, Ciudad Real - 2 de noviembre de 2001)

Mallinnusinformaation hyödyntäminen ohjelmistokehityksessä

Ohjelmistoprosesseissa kulkee käytännössä sama tieto muuntuen eri vaiheissa käyttökohteensa mukaan. Tätä mallinnusinformaatiota on mahdollista siirtää ja käyttää uudelleen, mikä säästää resursseja ja vähentää riskejä kaikissa projektin vaiheissa. Projektin alussa ohjelmiston toimintoja suunnitellaan ja niitä mallinnetaan esim. UML-malleilla. Tätä mallinnusinformaatiota hallitaan erilaisilla CASE-työkaluilla, joiden avullamalleja on helppo konvertoida toteutusvaihetta varten lähdekoodiksi. Lähdekoodivoidaan tuoda takaisin malliksi jatkosuunnittelua varten, jos työkalu tukee ominaisuutta. Testausvaiheessa lähdekoodi voidaan parsia, jotta siitä saadaan esille olennainen mallinnusinformaatio testejä varten. Lopulta dokumentaatiota voidaan generoida automaattisesti esim. Javadocilla. Mallinnusinformaation hyödyntäminen onnistuu hyvin teoriassa, mutta se ei ole niin suoraviivaista käytännössä. Tämänhetkiset työkalut eivät ole tarpeeksi joustavia mallinnusinformaation palauttamiseksi edellisiin vaiheisiin, joten ne ajavat toteuttamaan projekteja lineaarisesti. Keskikokoisessakin ohjelmistoprojektissa on suuri määrä mallinnusinformaatiota ja se lisää haasteita. Vaikka työkalut ovat kankeita, mallinnusinformaation hyödyntämisen on koettu tehostavan ohjelmistoprosesseja. Siksi sen keinoja tutkitaan ahkerasti.

CoObRA: Eine Plattform zur Verteilung und Replikation komplexer Objektstrukturen mit optimistischen Sperrkonzepten

In der vorliegenden Arbeit wird die Konzeption und Realisierung der Persistenz-, Verteilungs- und Versionierungsbibliothek CoObRA 2 vorgestellt. Es werden zunächst die Anforderungen an ein solches Rahmenwerk aufgenommen und vorhandene Technologien für dieses Anwendungsgebiet vorgestellt. Das in der neuen Bibliothek eingesetzte Verfahren setzt Änderungsprotokolle beziehungsweise -listen ein, um Persistenzdaten für Dokumente und Versionen zu definieren. Dieses Konzept wird dabei durch eine Abbildung auf Kontrukte aus der Graphentheorie gestützt, um die Semantik von Modell, Änderungen und deren Anwendung zu definieren. Bei der Umsetzung werden insbesondere das Design der Bibliothek und die Entscheidungen, die zu der gewählten Softwarearchitektur führten, eingehend erläutert. Dies ist zentraler Aspekt der Arbeit, da die Flexibilität des Rahmenwerks eine wichtige Anforderung darstellt. Abschließend werden die Einsatzmöglichkeiten an konkreten Beispielanwendungen erläutert und bereits gemachte Erfahrungen beim Einsatz in CASE-Tools, Forschungsanwendungen und Echtzeit-Simulationsumgebungen präsentiert.

An empirical study of process-related attributes in segmented software cost-estimation relationships

Parametric software effort estimation models consisting on a single mathematical relationship suffer from poor adjustment and predictive characteristics in cases in which the historical database considered contains data coming from projects of a heterogeneous nature. The segmentation of the input domain according to clusters obtained from the database of historical projects serves as a tool for more realistic models that use several local estimation relationships. Nonetheless, it may be hypothesized that using clustering algorithms without previous consideration of the influence of well-known project attributes misses the opportunity to obtain more realistic segments. In this paper, we describe the results of an empirical study using the ISBSG-8 database and the EM clustering algorithm that studies the influence of the consideration of two process-related attributes as drivers of the clustering process: the use of engineering methodologies and the use of CASE tools. The results provide evidence that such consideration conditions significantly the final model obtained, even though the resulting predictive quality is of a similar magnitude.

Object modeling for user-centered development and user interface design: the wisdom approach

João Bernardo de Sena Esteves Falcão e Cunha

Touch ‘n’ sketch: pen and fingers on a multi-touch sketch application for tablet PC’s

In many creative and technical areas, professionals make use of paper sketches for developing and expressing concepts and models. Paper offers an almost constraint free environment where they have as much freedom to express themselves as they need. However, paper does have some disadvantages, such as size and not being able to manipulate the content (other than remove it or scratch it), which can be overcome by creating systems that can offer the same freedom people have from paper but none of the disadvantages and limitations. Only in recent years has the technology become massively available that allows doing precisely that, with the development in touch‐sensitive screens that also have the ability to interact with a stylus. In this project a prototype was created with the objective of finding a set of the most useful and usable interactions, which are composed of combinations of multi‐touch and pen. The project selected Computer Aided Software Engineering (CASE) tools as its application domain, because it addresses a solid and well‐defined discipline with still sufficient room for new developments. This was the result from the area research conducted to find an application domain, which involved analyzing sketching tools from several possible areas and domains. User studies were conducted using Model Driven Inquiry (MDI) to have a better understanding of the human sketch creation activities and concepts devised. Then the prototype was implemented, through which it was possible to execute user evaluations of the interaction concepts created. Results validated most interactions, in the face of limited testing only being possible at the time. Users had more problems using the pen, however handwriting and ink recognition were very effective, and users quickly learned the manipulations and gestures from the Natural User Interface (NUI).

Automatic code generation for microcontrollers from place-transition Petri net models

This paper describes a program for the automatic generation of code for Intel's 8051 microcontroller. The code is generated from a place-transition Petri net specification. Our goal is to minimize programming time. The code generated by our program has been observed to exactly match the net model. It has also been observed that no change is needed to be made to the generated code for its compilation to the target architecture. © 2011 IFAC.

Creating Responsive Information Systems with the Help of SSADM

In this paper, a program for a research is outlined. Firstly, the concept of responsive information systems is defined and then the notion of the capacity planning and software performance engineering is clarified. Secondly, the purpose of the proposed methodology of capacity planning, the interface to information systems analysis and development methodologies (SSADM), the advantage of knowledge-based approach is discussed. The interfaces to CASE tools more precisely to data dictionaries or repositories (IRDS) are examined in the context of a certain systems analysis and design methodology (e.g. SSADM).

“Confessionários or Manuals of Confession: Missionary tools and their colonial uses – The case study of Goa

Existem referências aos manuais de bem confessar que os Jesuitas utilizaram na Índia desde os inícios da sua actividade missionária, mas até agora não se tinha publicado nenhum para os séculos XVI-XVII. Encontrei alguns na British Library em Londres em 1994, e estão aqui analisados, dando a conhecer como a nova religião ajudava a criar cidadãos responsáveis do império colonial e a cumprir as suas leis. Para além de ajudar-nos a compreender o vocabulário e o estilo da língua vernácula destes tempos, alguém que evitasse pagar impostos ao Estado ou manipulasse os livros de contas da aldeia encorria em pecados a confessar.

Introducing numerical analysis tools in engineering: a Scilab user case in electronics course

Software tools in education became popular since the widespread of personal computers. Engineering courses lead the way in this development and these tools became almost a standard. Engineering graduates are familiar with numerical analysis tools but also with simulators (e.g. electronic circuits), computer assisted design tools and others, depending on the degree. One of the main problems with these tools is when and how to start use them so that they can be beneficial to students and not mere substitutes for potentially difficult calculations or design. In this paper a software tool to be used by first year students in electronics/electricity courses is presented. The growing acknowledgement and acceptance of open source software lead to the choice of an open source software tool – Scilab, which is a numerical analysis tool – to develop a toolbox. The toolbox was developed to be used as standalone or integrated in an e-learning platform. The e-learning platform used was Moodle. The first approach was to assess the mathematical skills necessary to solve all the problems related to electronics and electricity courses. Analysing the existing circuit simulators software tools, it is clear that even though they are very helpful by showing the end result they are not so effective in the process of the students studying and self learning since they show results but not intermediate steps which are crucial in problems that involve derivatives or integrals. Also, they are not very effective in obtaining graphical results that could be used to elaborate reports and for an overall better comprehension of the results. The developed tool was based on the numerical analysis software Scilab and is a toolbox that gives their users the opportunity to obtain the end results of a circuit analysis but also the expressions obtained when derivative and integrals calculations, plot signals, obtain vector diagrams, etc. The toolbox runs entirely in the Moodle web platform and provides the same results as the standalone application. The students can use the toolbox through the web platform (in computers where they don't have installation privileges) or in their personal computers by installing both the Scilab software and the toolbox. This approach was designed for first year students from all engineering degrees that have electronics/electricity courses in their curricula.

Process simulation support in BPM tools: The case of BPMN

Due to the increasing acceptance of BPM, nowadays BPM tools are extensively used in organizations. Core to BPM are the process modeling languages, of which BPMN is the one that has been receiving most attention these days. Once a business process is described using BPMN, one can use a process simulation approach in order to find its optimized form. In this context, the simulation of business processes, such as those defined in BPMN, appears as an obvious way of improving processes. This paper analyzes the business process modeling and simulation areas, identifying the elements that must be present in the BPMN language in order to allow processes described in BPMN to be simulated. During this analysis a set of existing BPM tools, which support BPMN, are compared regarding their limitations in terms of simulation support.

Disaster Risk, Livelihoods and Natural Barriers, Strengthening Decision-Making Tools for Disaster Risk Reduction: A case study from northern Pakistan

The goal of this interdisciplinary study is to better understand the land use factors that increase vulnerability of mountain areas in northern Pakistan. The study will identify and analyse the damages and losses caused by the October 2005 earthquake in two areas of the same valley: one "low-risk" watershed with sound natural resources management, the other, "high-risk" in an ecologically degraded watershed. Secondly, the study will examine natural and man-made causes of secondary hazards in the study area, especially landslides; and third it will evaluate the cost of the earthquake damage in the study areas on the livelihoods of local communities and the sub-regional economy. There are few interdisciplinary studies to have correlated community land use practices, resources management, and disaster risk reduction in high-risk mountain areas. By better understanding these linkages, development- humanitarian- and donor agencies focused on disaster reduction can improve their risk reduction programs for mountainous regions.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Development of e-Service tools for customer services and processes, Case: KONE Oyj Finland

Tutkimuksen tavoitteena oli selvittää miten kehittää yrityksen nykyistä e-palvelujärjestelmää, Internet -teknologiaan perustuvaa sähköisiä kommunikaatio- ja tiedonjakojärjestelmää, yrityksen business-to-business asiakkuuksien johtamisessa. Tavoitteena oli myös luoda ehdotukset uusista e-palvelusopimusmalleista. Tutkimuksen teoriaosuudessa pyrittiin kehittämään aikaisempiin tutkimuksiin, tietokirjallisuuteen ja asiantuntijoihin perustuva viitekehysmalli. Empiirisessä osassa tutkimuksen tavoitteisiin pyrittiin haastattelemalla yrityksen asiakkaita ja henkilöstöä, sekä tarkastelemalla asiakaskontaktien nykyistä tilaa ja kehittymistä. Näiden tietojen perusteella selvitettiin e-palvelun käyttäjien tarpeita, profiilia ja valmiuksia palvelun käyttöön sekä palvelun nykyistä houkuttelevuutta. Tutkimuksen teoriaosan lähdeaineistona käytettiin kirjallisuutta, artikkeleita ja tilastoja asiakashallinnasta sekä e-palveluiden, erityisesti Internet ja verkkopalveluiden markkinoinnista, nykytilasta sekä palveluiden kehittämisestä. Lisäksi tutkittiin kirjallisuutta arvoverkostoanalyysistä, asiakkaan arvosta, informaatioteknologiasta, palvelun laadusta ja asiakastyytyväisyydestä. Tutkimuksen empiirinen osa perustuu yrityksen henkilöstöltä sekä asiakkailta haastatteluissa kerättyihin tietoihin, yrityksen ennalta keräämiin materiaaleihin sekä Taloustutkimuksen keräämiin tietoihin. Tutkimuksessa käytettiin case -menetelmää, joka oli yhdistelmä sekä kvalitatiivista että kvantitatiivista tutkimusta. Casen tarkoituksena oli testata mallin paikkansapitävyyttä ja käyttökelpoisuutta, sekä selvittää onko olemassa vielä muita tekijöitä, jotka vaikuttavat asiakkaan saamaan arvoon. Kvalitatiivinen aineisto perustuu teemahaastattelumenetelmää soveltaen haastateltuihin asiakkaisiin ja yrityksen työntekijöihin. Kvantitatiivinen tutkimus perustuu Taloustutkimuksen tutkimukseen ja yrityksen asiakaskontakteista kerättyyn tietoon. Haastatteluiden perusteella e-palvelut nähtiin hyödyllisinä ja tulevaisuudessa erittäin tärkeinä. E-palvelut nähdään yhtenä tärkeänä kanavana, perinteisten kanavien rinnalla, tehostaa business-to-business -asiakkuuksien johtamista. Tutkimuksen antamien tulosten mukaan asiakkaiden palveluun liittyvän tieto-, taito-, tarpeellisuus- ja kiinnostavuustasojen vaihtelevaisuus osoittaa selvän tarpeen eritasoisille e-palvelupaketti ratkaisuille. Tuloksista muodostettu ratkaisuehdotus käsittää neljän eri e-palvelupaketin rakentamisen asiakkaiden eri tarpeita mukaillen.