Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

A teaching/learning support tool for introductory programming courses

In This work we present a Web-based tool developed with the aim of reinforcing teaching and learning of introductory programming courses. This tool provides support for teaching and learning. From the teacher's perspective the system introduces important gains with respect to the classical teaching methodology. It reinforces lecture and laboratory sessions, makes it possible to give personalized attention to the student, assesses the degree of participation of the students and most importantly, performs a continuous assessment of the student's progress. From the student's perspective it provides a learning framework, consisting in a help environment and a correction environment, which facilitates their personal work. With this tool students are more motivated to do programming

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

The development and pilot-testing of a training curriculum in adolescent medicine and health.

PURPOSE: To select and propose a set of knowledge, attitudes, and skills essential for the care of adolescents; to encourage the development of adolescent health multidisciplinary networks; and to set up training programs in as many European countries as possible. METHODS: The curriculum was developed by 16 physicians from 11 European countries with various professional specializations. In line with modern guidelines in medical education, it is a modular, flexible instrument which covers the main teaching areas in the field, such as basic skills (i.e. setting, rights and confidentiality, gender and cultural issues) as well as specific themes (i.e. sexual and reproductive health, eating disorders, chronic conditions). It consists of 17 thematic modules, each containing detailed objectives, learning approaches, examples, and evaluation methods. RESULT: Two international one-week summer schools were used to assess the feasibility and appropriateness of the curriculum. The overall evaluation was good, with most of the items surpassing three on a four-point Likert scale. However, it pointed to several aspects (process and content) which will need to be refined in the future, such as an increase in interactive sessions (role playing), and a better mix of clinical and public health issues.

Learning in Product Development Projects - Responding to challenges in global industrial company

Verkostoitunut kansainvälinen tuotekehitys on tärkeä osa menestystä nykypäivän muuttuvassa yritysmaailmassa. Toimintojen tehostamiseksi myös projektitoiminnot on sopeutettava kansainväliseen toimintaympäristöön. Kilpailukyvyn säilyttämiseksi projektitoimintoja on lisäksi jatkuvasti tehostettava. Yhtenäkeinona nähdään projektioppiminen, jota voidaan edistää monin eri tavoin. Tässätyössä keskitytään projektitiedonhallinnan kehittämisen tuomiin oppimismahdollisuuksiin. Kirjallisuudessa kerrotaan, että projektitiedon jakaminen ja sen hyödyntäminen seuraavissa projekteissa on eräs projektioppimisen edellytyksistä. Tämäon otettu keskeiseksi näkökulmaksi tässä tutkimuksessa. Lisäksi tutkimusalueen rajaamiseksi työ tarkastelee erityisesti projektioppimista kansainvälisten tuotekehitysprojektien välillä. Työn tavoitteena on esitellä keskeisiä projektioppimisen haasteita ja etsiä konkreettinen ratkaisu vastaamaan näihin haasteisiin. Tuotekehitystoiminnot ja kansainvälinen hajautettu projektiorganisaatio kohtaavat lisäksi erityisiä haasteita, kuten tiedon hajautuneisuus, projektihenkilöstön vaihtuvuus, tiedon luottamuksellisuus ja maantieteelliset haasteet (esim. aikavyöhykkeet ja toimipisteen sijainti). Nämä erityishaasteet on otettu huomioon ratkaisua etsittäessä. Haasteisiin päädyttiin vastaamaan tietotekniikkapohjaisella ratkaisulla, joka suunniteltiin erityisesti huomioiden esimerkkiorganisaation tarpeet ja haasteet. Työssä tarkastellaan suunnitellun ratkaisun vaikutusta projektioppimiseen ja kuinka se vastaa havaittuihin haasteisiin. Tuloksissa huomattiin, että projektioppimista tapahtui, vaikka oppimista oli vaikea suoranaisesti huomata tutkimusorganisaation jäsenten keskuudessa. Projektioppimista voidaan kuitenkin sanoa tapahtuvan, jos projektitieto on helposti koko projektiryhmän saatavilla ja se on hyvin järjesteltyä. Muun muassa nämä ehdot täyttyivät. Projektioppiminen nähdään yleisesti haastavana kehitysalueena esimerkkiorganisaatiossa. Suuri osa tietämyksestä on niin sanottua hiljaistatietoa, jota on hankala tai mahdoton saattaa kirjalliseen muotoon. Näin olleen tiedon siirtäminen jää suurelta osin henkilökohtaisen vuorovaikutuksen varaan. Siitä huolimatta projektioppimista on mahdollista kehittää erilaisin toimintamallein ja menetelmin. Kehitys vaatii kuitenkin resursseja, pitkäjänteisyyttä ja aikaa. Monet muutokset voivat vaatia myös organisaatiokulttuurin muutoksen ja vaikuttamista organisaation jäseniin. Motivaatio, positiiviset mielikuvat ja selkeät strategiset tavoitteet luovat vakaan pohjan projektioppimisen kehittämiselle.

Time patterns and perceptions of online learning success factors

Online learning provides the opportunity to work on academic tasks at any time at the same time as doing other activities, such as using in web 2.0 tools. This study identifies factors that contribute to success in online learning from the students¿ perspective and their relationship with time patterns. A survey of learning outputs was used to find relationships between students¿ satisfaction, knowledge acquisition and knowledge transfer with time for working on academic tasks. In this study, 199 students from a university in Mexico completed the survey. Findings suggest that knowledge transfer has a significant association with the number of hours online per day, hours spent on social networks and the use made of e-learning during working hours. Learner satisfaction has a strong relationship with the time in years a learner has been using the Internet and the number of hours devoted to the course per week. The findings of this research will be helpful for faculty and instructional designers for implementing learning strategies.

Lukion opettajien ääni - Aineenopettajien käsityksiä muutoksista ja visioista lukiossa

Tulevaisuusorientaatio on tullut entistä tärkeämmäksi myös koulumaailmassa johtuen yhteiskunnassa nopeasti eteen tulevista muutoksista. On myös esitetty epäilyjä, että tulevaisuuteen reagoimisessa heikoin tilanne olisikin juuri kuntatason päätöksenteossa. Ymmärrys ja tieto opettajan työstä nimenomaan opettajan omasta perspektiivistä tarkasteltuna mahdollistavat lähtökohdan ja edellytykset todelliselle koulun uudistamiselle. Peruskysymys, johon tässä tutkimuksessa etsittiin vastausta oli: Millaisia ovat lukion aineenopettajien käsitykset lukion muutosprosesseista ja miten he visioivat oman lukionsa ja yleensä lukioiden tulevaisuutta? Aineiston keruu osui ajankohtaan, joka oli hyvin otollinen tulevaisuuden tarkasteluun, sillä uusi opetussuunnitelma otettiin käyttöön kaikissa Suomen lukioissa viimeistään lukuvuonna 2005-2006. Tutkimuksessa oli mukana kaksi hyvin erilaista lukiota Länsi-Suomen läänistä eli pieni maaseudun lukio ja hyvin suuri kaupunkilukio. Tutkimusaineiston keruu eteni kaksivaiheisesti: informoitu kysely ja teemahaastattelu. Tutkimusjoukon suuruus oli yhteensä 20, joista puolet oli miehiä ja puolet naisia. Aineenopettajien käsityksiä muutoksista ja visioista tutkittiin fenomenografisen tutkimusotteen avulla. Fenomenografiassa kiinnostuksen kohteena ovat ihmisten erilaiset käsitykset todellisuudesta ja näin saatava ymmärrys tavoista, joilla ihmiset kokevat tilanteita ja maailmaa. Organisaation muutosprosesseja voidaan kutsua myös oppimiseksi. Tutkimuksen oppivan organisaation näkökulmat perustuivat juuri yhteistyössä tapahtuvaan yhteisen toiminnan kehittämiseen. Aineenopettajien käsityksiä työyhteisöstään tarkasteltiin seuraavista oppivan organisaatiomallin näkökulmista: vuorovaikutus, päätöksenteko sekä rehtorin ja aineenopettajan rooli ja asema työyhteisössä. Aineenopettajien keskeisimmät käsitykset muutoksista lukiossa viime vuosina kohdistuivat aineenopettajan ammattirooliin ja lukio-opiskelijaan sekä opiskelijalta vaadittaviin lukio-opintoihin. Muutokset ammattiroolissa korostavat tutkimustulosten perusteella aineenopettajilta vaadittavia muitakin kuin opetettavien aineiden hallintataitoja. Suoranaista ammattitaidon puutetta opettajat kokivat varsinkin ryhmänohjaustehtävien yhteydessä, osittain myös uusien oppimisympäristöjen, esimerkiksi verkkopedagogisten taitojen, yhteydessä. Opettajien lisäkoulutuksen tarve koetaan konkreettisena, mutta sekä koulutusten sisältöihin, järjestelyihin ja ajankohtiin että koulun sijais- ym. järjestelyihin kaivattaisiin parannuksia. Verrattuna aikaisempiin tutkimuksiin näyttäisi siltä, että luokaton lukio on saanut opettajat enenevässä määrin huolestumaan opiskelijoiden syrjäytymisriskistä ja hyvinvoinnista. Opiskelijoiden syrjäytymisriskin kasvu lukio-opintojen aikana nouseekin yhdeksi lukion pessimistiseksi skenaarioksi. Muista pessimistisistä skenaarioista lukiolle, jotka saattoi johtaa tutkimustuloksista, voidaan mainita työyhteisön demokratiavajeen syveneminen sekä opetussuunnitelmasisältöjen ja ylioppilastutkintovaatimusten välisen kuilun syveneminen. Aineenopettajien käsitykset oman lukionsa visioista olivat sisällöiltään pääosin välineellisiä ja ne kohdentuivat kaikki opiskelijoihin. Esimerkiksi työyhteisöllisiä kehittämisajatuksia ei visioissa ilmennyt. Myöskään visioinnin dynaamisuus ei aineistossa korostunut. Aineenopettajien käsitykset visioiden arvopohjasta heijastivat perinteistä suomalaista arvomaailmaa eli itsekuria, velvollisuudentuntoa, kuuliaisuutta esivaltaa kohtaan ja perinteisten arvojen kunnioittamista. Sen sijaan antiikista perityviä Sokrateen edustamia keskustelua ja auktoriteettien kyseenalaistamista ei arvoissa ilmennyt, eikä myöskään uusliberalistista individualismia. Käsitykset visioiden synnystä näyttävät parhaiten selittävän opettajan muita käsityksiä liittyen visioon, visiointiin ja työyhteisöllisiin vaikutusmahdollisuuksiin sekä opettajan tulevaisuusorientoitumiseen.. Käsitykset vision syntytaustasta voidaan jakaa seuraaviin pää- ja alakategorioihin: 1. auktoriteettikeskeinen visiointi: johdon linjaus tai valtakunnallinen linjaus, 2. yhteisökeskeinen visiointi: yhteisöllinen linjaus tai toiminnallinen linjaus ja 3. yksilökeskeinen visiointi. Pessimistisimmiksi eli vähiten tulevaisuusorientoituneiksi opettajiksi työyhteisössä osoittautuivat ne opettajat, jotka pitivät oman lukionsa visiota koulun johdon sanelemana. Monet teoriat oppivasta organisaatiosta korostavat johtajuuden merkitystä työyhteisöä kehitettäessä. Johtajuuden merkitys nousi tämänkin tutkimuksen aineistosta keskeisesti esiin. Pyrkimystä kohti oppivaa organisaatiota opettajien puheista löytyy paljonkin, esimerkkeinä viittaukset johtajuuden ja vuorovaikutustapojen kehittämistarpeisiin. Sen sijaan opettajien puheet omista työyhteisöllisistä kehittymistarpeistaan, ns. alaistaidot, jäivät vähäisiksi. Tutkimustuloksista on luotu sovellusmalli kouluyhteisöjen visioinnin ja muun kehittämistyön tueksi.

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (2040 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (48 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions.

Learning through role-playing games: an approach for active learning and teaching

This study evaluates the use of role-playing games (RPGs) as a methodological approach for teaching cellular biology, assessing student satisfaction, learning outcomes, and retention of acquired knowledge. First-year undergraduate medical students at two Brazilian public universities attended either an RPG-based class (RPG group) or a lecture (lecture-based group) on topics related to cellular biology. Pre- and post-RPG-based class questionnaires were compared to scores in regular exams and in an unannounced test one year later to assess students' attitudes and learning. From the 230 students that attended the RPG classes, 78.4% responded that the RPG-based classes were an effective tool for learning; 55.4% thought that such classes were better than lectures but did not replace them; and 81% responded that they would use this method. The lecture-based group achieved a higher grade in 1 of 14 regular exam questions. In the medium-term evaluation (one year later), the RPG group scored higher in 2 of 12 questions. RPG classes are thus quantitatively as effective as formal lectures, are well accepted by students, and may serve as educational tools, giving students the chance to learn actively and potentially retain the acquired knowledge more efficiently.

A multimethod examination of contributors to successful on-the-job learning for vocational students

Rapid changes in working life and competence requirements of different professions have increased interest in workplace learning. It is considered an effective way to learn and update professional skills by performing daily tasks in an authentic environment. Especially, ensuring a supply of skilled future workers is a crucial issue for firms facing tight competition and a shortage of competent employees due to the retirement of current professionals. In order to develop and make the most of workplace learning, it is important to focus on workplace learning environments and the individual characteristics of those participating in workplace learning. The literature has suggested various factors that influence adults' and professionals’ workplace learning of profession-related skills, but lacks empirical studies on contextual and individual-related factors that positively affect students' workplace learning. Workers with vocational education form a large group in modern firms. Therefore, elements of vocational students’ successful workplace learning during their studies, before starting their career paths, need to be examined. To fill this gap in the literature, this dissertation examines contributors to vocational students’ workplace learning in Finland, where students’ workplace learning is included in the vocational education and training system. The study is divided into two parts: the introduction, comprised of the overview of the relevant literature and the conclusion of the entire study, and five separate articles. Three of the articles utilize quantitative methods and two use qualitative methods to examine factors that contribute to vocational students’ workplace learning. The results show that, from the students’ perspective, attitudinal, motivational, and organizationrelated factors enhance the student’s development of professionalism during the on-the-job learning period. Specifically, the organization-related factors such as innovative climate, guidance, and interactions with seniors have a strong positive impact on the students’ perceived development of professional skills because, for example, the seniors’ guidance and provision of new viewpoints for the tasks helps the vocational students to gain autonomy at work performance. A multilevel analysis shows that of those factors enhancing workplace learning from the student perspective, innovative climate, knowledge transfer accuracy, and the students’ performance orientation were significantly related to the workplace instructors’ assessment regarding the students’ professional performance. Furthermore, support from senior colleagues and the students’ self-efficacy were both significantly associated with the formal grades measuring how well the students managed to learn necessary professional skills. In addition, the results suggest that the students’ on-the-job learning can be divided into three main phases, of which two require efforts from both the student and the on-the-job learning organization. The first phase includes the student’s application of basic professional skills, demonstration of potential in performing daily tasks, and orientation provided by the organization at the beginning of the on-the-job learning period. In the second phase, the student actively develops profession-related skills by performing daily tasks, thus learning a fluent working style while observing the seniors’ performance. The organization offers relevant tasks and follows the student’s development. The third level indicates a student who has reached the professional level described as a full occupation. The results suggest that constructing the vocational students’ successful on-the-job learning period requires feedback from seniors, opportunities to learn to manage entire work processes, self-efficacy on the part of the students, proactive behavior, and initiative in learning. The study contributes to research on workplace learning in three ways: firstly, it identifies the key individual- and organization-based factors that influence the vocational students’ successful on-the-job learning from their perspective and examines mutual relationships between these factors. Second, the study provides knowledge of how the factors related to the students’ view of successful workplace learning are associated with the workplace instructors’ perspective and the formal grades. Third, the present study finds elements needed to construct a successful on-the-job learning for the students.

Informal learning and competence development during a management trainee program

The purpose of this research was to study how management trainee program participants experienced the program with respect to their learning and competence development. Additionally, the purpose was also to examine what the trainees learned and how the learning occurred. Furthermore, factors affecting learning in the workplace were examined. The theoretical framework of this research was formed utilizing individual competence and informal learning frameworks. Research was conducted as a single case study and data was gathered by thematic interviews. The results of this research indicate that the trainees experienced the program as a good method for learning the overall picture of the organization and its business. Regarding competence development, especially knowledge- and cognitive competence categories were developed during the program. The best learning outcomes were achieved through learning by doing, in co-operation with others, and learning from others. The results indicate that the planning of the program and its structure have a significant effect on learning. Furthermore, a sufficient level of challenge was experienced as being important for the quality of the learning as well.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Self-directed learning, lifelong learning and transformative learning in the Society for Creative Anachronism

This study examines the connection between leisure group participation and learning activities undertaken by participants in the Society for Creative Anachronism (SCA), a medieval recreationist group. The thesis of this connection was developed through the researcher's observations during SCA participation. The intent of this study is to understand adult learning from the self-directed learning, lifelong learning, and -transformative learning components derived from participant's SCA experiences. This qualitative study was conducted by interviewing eight active SCA participants, two in each participation theme of historical research, artistic representation, performance, and martial skills. Informants' responses demonstrated an integration of their leisure activity with learning. The contextualization of learning a s both a primary activity and a necessary support to participation, places learning a t the heart of participants' SCA related activities. The positive descriptions of learning activities, descriptive terms of ownership, and situating learning as an enjoyable activity engaged for the pleasure of the experience, provides adult educators with a fascinating glimpse of willing and engaged adult learners pursuing lifelong learning outside of the traditional educational structure. Two themes emerged during the interviews. First, bonding with others provided the motivation to continue their activities. Secondly, a feeling of commitment and helonging defined their enjoyment and satisfaction with SCA participation. The clear implications are that adult educators can create effective learning communities by developing educational structures that engage adult learners wi th meaningful social interaction.

Improving automation in model-driven engineering using examples

Cette thèse a pour but d’améliorer l’automatisation dans l’ingénierie dirigée par les modèles (MDE pour Model Driven Engineering). MDE est un paradigme qui promet de réduire la complexité du logiciel par l’utilisation intensive de modèles et des transformations automatiques entre modèles (TM). D’une façon simplifiée, dans la vision du MDE, les spécialistes utilisent plusieurs modèles pour représenter un logiciel, et ils produisent le code source en transformant automatiquement ces modèles. Conséquemment, l’automatisation est un facteur clé et un principe fondateur de MDE. En plus des TM, d’autres activités ont besoin d’automatisation, e.g. la définition des langages de modélisation et la migration de logiciels. Dans ce contexte, la contribution principale de cette thèse est de proposer une approche générale pour améliorer l’automatisation du MDE. Notre approche est basée sur la recherche méta-heuristique guidée par les exemples. Nous appliquons cette approche sur deux problèmes importants de MDE, (1) la transformation des modèles et (2) la définition précise de langages de modélisation. Pour le premier problème, nous distinguons entre la transformation dans le contexte de la migration et les transformations générales entre modèles. Dans le cas de la migration, nous proposons une méthode de regroupement logiciel (Software Clustering) basée sur une méta-heuristique guidée par des exemples de regroupement. De la même façon, pour les transformations générales, nous apprenons des transformations entre modèles en utilisant un algorithme de programmation génétique qui s’inspire des exemples des transformations passées. Pour la définition précise de langages de modélisation, nous proposons une méthode basée sur une recherche méta-heuristique, qui dérive des règles de bonne formation pour les méta-modèles, avec l’objectif de bien discriminer entre modèles valides et invalides. Les études empiriques que nous avons menées, montrent que les approches proposées obtiennent des bons résultats tant quantitatifs que qualitatifs. Ceux-ci nous permettent de conclure que l’amélioration de l’automatisation du MDE en utilisant des méthodes de recherche méta-heuristique et des exemples peut contribuer à l’adoption plus large de MDE dans l’industrie à là venir.

Une approche heuristique pour l’apprentissage de transformations de modèles complexes à partir d’exemples

L’ingénierie dirigée par les modèles (IDM) est un paradigme d’ingénierie du logiciel bien établi, qui préconise l’utilisation de modèles comme artéfacts de premier ordre dans les activités de développement et de maintenance du logiciel. La manipulation de plusieurs modèles durant le cycle de vie du logiciel motive l’usage de transformations de modèles (TM) afin d’automatiser les opérations de génération et de mise à jour des modèles lorsque cela est possible. L’écriture de transformations de modèles demeure cependant une tâche ardue, qui requiert à la fois beaucoup de connaissances et d’efforts, remettant ainsi en question les avantages apportés par l’IDM. Afin de faire face à cette problématique, de nombreux travaux de recherche se sont intéressés à l’automatisation des TM. L’apprentissage de transformations de modèles par l’exemple (TMPE) constitue, à cet égard, une approche prometteuse. La TMPE a pour objectif d’apprendre des programmes de transformation de modèles à partir d’un ensemble de paires de modèles sources et cibles fournis en guise d’exemples. Dans ce travail, nous proposons un processus d’apprentissage de transformations de modèles par l’exemple. Ce dernier vise à apprendre des transformations de modèles complexes en s’attaquant à trois exigences constatées, à savoir, l’exploration du contexte dans le modèle source, la vérification de valeurs d’attributs sources et la dérivation d’attributs cibles complexes. Nous validons notre approche de manière expérimentale sur 7 cas de transformations de modèles. Trois des sept transformations apprises permettent d’obtenir des modèles cibles parfaits. De plus, une précision et un rappel supérieurs à 90% sont enregistrés au niveau des modèles cibles obtenus par les quatre transformations restantes.