Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.

Using ecological niche models to support tree species selection for forest restoration planning in largely deforested regions

Species selection for forest restoration is often supported by expert knowledge on local distribution patterns of native tree species. This approach is not applicable to largely deforested regions unless enough data on pre-human tree species distribution is available. In such regions, ecological niche models may provide essential information to support species selection in the framework of forest restoration planning. In this study we used ecological niche models to predict habitat suitability for native tree species in "Tierra de Campos" region, an almost totally deforested area of the Duero Basin (Spain). Previously available models provide habitat suitability predictions for dominant native tree species, but including non-dominant tree species in the forest restoration planning may be desirable to promote biodiversity, specially in largely deforested areas were near seed sources are not expected. We used the Forest Map of Spain as species occurrence data source to maximize the number of modeled tree species. Penalized logistic regression was used to train models using climate and lithological predictors. Using model predictions a set of tools were developed to support species selection in forest restoration planning. Model predictions were used to build ordered lists of suitable species for each cell of the study area. The suitable species lists were summarized drawing maps that showed the two most suitable species for each cell. Additionally, potential distribution maps of the suitable species for the study area were drawn. For a scenario with two dominant species, the models predicted a mixed forest (Quercus ilex and a coniferous tree species) for almost one half of the study area. According to the models, 22 non-dominant native tree species are suitable for the study area, with up to six suitable species per cell. The model predictions pointed to Crataegus monogyna, Juniperus communis, J.oxycedrus and J.phoenicea as the most suitable non-dominant native tree species in the study area. Our results encourage further use of ecological niche models for forest restoration planning in largely deforested regions.

Modelo de dirección de proyectos complejos: aplicación a la gestión integrada de la Comunidad de Regantes LASESA (Huesca)

La complejidad en los proyectos y la complejidad de la dirección de proyectos son conceptos cuyo interés va en aumento. En el ámbito de los proyectos, dado que la gestión de recursos y las actividades suceden en interacción con las personas, la complejidad en dirección de proyectos resulta ser una práctica inherentemente social. Esto ha hecho que en la actualidad se haya convertido en una necesidad profundizar en el concepto. En el primer capítulo de esta investigación, se hace una revisión del estado del arte del concepto y se construye un marco teórico que permite establecer y justificar las dimensiones de la complejidad en esta disciplina. De igual manera, con una revisión similar de los estándares internacionales existentes, se descubren las herramientas y modelos para el análisis de la complejidad en dirección de proyectos, con la que se pretende, a través de la práctica profesional desde organismos y alianzas internacionales, ayudar a contrastar la utilidad e interés de los conceptos propuestos. En el segundo capítulo, se presenta un marco conceptual sobre la certificación de competencias, se analizan y comparan los principales sistemas de certificación de competencias vigentes en el ámbito de la dirección de proyectos. En el tercer capítulo, se presenta un proceso metodológico novedoso para valorar, desde la integración del conocimiento experto y experimentado, dos aspectos complementarios de la complejidad de la dirección de proyectos: (a) la valoración del nivel de complejidad del proyecto; y (b), la valoración de los efectos en el desarrollo de las competencias de comportamiento de las partes implicadas. La metodología propuesta se aplica a la Comunidad de Regantes LASESA en Huesca (España), proceso que se presenta en el cuarto capítulo. LASESA es un proyecto complejo que gestiona los recursos hídricos de 10.000 hectáreas y con más de 600 propietarios implicados. Los resultados evidencian como la gestión de un proyecto complejo genera efectos positivos en el desarrollo de las competencias de comportamiento de las personas que se implican y participan en los trabajos….. ABSTRACT The complexity of the projects and the complexity of project management are concepts whose interest is increasing. In terms of projects, since the management of resources and activities occur in interaction with people, the project management complexity makes an inherently social practice. This has now has become a necessity to deepen the concept. In the first chapter of this study, we review the state of art of the concept and builds a theoretical framework that allows to establish and justify the dimensions of the complexity in this discipline. Likewise, a similar review of existing international standards, we discover the tools and models for the analysis of complexity in project management, with which it is intended, through professional practice from agencies and international alliances, help to compare the usefulness and interest of the proposed concepts. In the second chapter, we present a conceptual framework for the certification of competences, are analyzed and compared the main skills certification systems in force in the field of project management. In the third chapter, we present a novel methodological process to assess, since the integration of expert knowledge and experienced, complementary aspects of the complexity of project management: (a) the assessment of the level of complexity of the project, and (b ), the assessment of the effects on the development of behavioral competencies of the parties involved. The proposed methodology is applied to the Community Irrigation LASESA in Huesca (Spain), a process that is presented in the fourth chapter. LASESA is a complex project that manages the water resources of 10,000 acres and more than 600 landowners involved. The results show as managing a complex project generates positive effects on the development of behavioral competencies of people who are involved and participate in the work.

An interactive method of fuzzy probability elicitation in risk analysis

Expert knowledge is used to assign probabilities to events in many risk analysis models. However, experts sometimes find it hard to provide specific values for these probabilities, preferring to express vague or imprecise terms that are mapped using a previously defined fuzzy number scale. The rigidity of these scales generates bias in the probability elicitation process and does not allow experts to adequately express their probabilistic judgments. We present an interactive method for extracting a fuzzy number from experts that represents their probabilistic judgments for a given event, along with a quality measure of the probabilistic judgments, useful in a final information filtering and analysis sensitivity process.

La evaluación de la competencia comunicativa específica en el ámbito de la ingeniería y la arquitectura

La progresiva internacionalización de las universidades españolas convierte a estas organizaciones en escenarios plurilingües. El español convive en ellos con otras lenguas, en especial el inglés, como vehículo de acceso y transmisión de conocimiento especializado. Esto requiere un proceso de alfabetización académica en lengua extranjera que tendrían que asumir las universidades de acogida, con objeto de preservar a los alumnos de los fracasos en los programas internacionales. Por el momento, en España, los programas de grado o de posgrado no establecen filtros con umbrales lingüísticos mínimos de acceso, a excepción de algunas universidades que se limitan a requerir certificados de grado de dominio del español general. No existen exámenes públicos de ingreso, o exámenes propios de postadmisión, que evalúen la habilidad lingüística comunicativa en contextos académicos. En este trabajo, se parte de la hipótesis de que los exámenes que certifican un grado de dominio de español general no sirven al propósito de discriminar a los alumnos capaces de seguir con éxito los programas de las universidades. Para verificarla, se desarrolla una prueba de examen específica que mida la capacidad de emplear el español en contextos académicos. La prueba se centra en las tareas que se revelan, en una primera fase exploratoria de la investigación, como más necesarias en lo que se refiere al uso del español como lengua vehicular: las clases magistrales. Una vez pilotada, se administró junto con otras destinadas a evaluar el grado de dominio de la lengua en contextos generales. Los resultados obtenidos del contraste de estas mediciones y de diversos análisis de los datos arrojan evidencias de que este tipo de prueba mide un constructo específico: la habilidad de uso del español en contextos académicos. ABSTRACT The progressive internationalization of Spanish universities has transformed these organizations into plurilingual scenarios. Spanish lives in them sharing the stage with other languages, especially English, as a means of access and transmission of expert knowledge. This requires a process of academic literacy in foreign language that host universities should assume, in order to safeguard students from failures in international programs. At the moment, in Spain, undergraduate or graduate programs do not set filters with minimum language requirements to gain access, except for some universities that merely require certificates of general Spanish. There are no Spanish language public admission exams, or post-enrollment tests of their own, to assess the communicative language ability of foreign students in academic contexts. In this dissertation, we start from the hypothesis that those tests that certify the student degree of mastery of the Spanish language do not serve the purpose of discriminating against students capable of successfully pursuing university programs. To prove it, a specific test that measures the ability to use Spanish in academic contexts was developed. This language test focused on the tasks associated with the most common genre, which revealed prominent in a first exploratory phase of the investigation, related to the use of Spanish as a means of instruction: university lectures. Once piloted, the test was administered along with others designed to assess the degree of mastery of the language in general contexts. Contrast results of these measurements and various analyzes of the data showed evidence that this type of test measures a specific construct: the ability to use Spanish in academic contexts.

Professional Liability of the Construction Professional as an Expert Witness in the Spanish Legal Framework

Inside COBRA 2011 RICS International Research Conference, the present paper is linked to analyze the liability of the construction professional in his practice as a expert witness in the Spanish legal framework. In a large number of legal procedures related to the building it is necessary the intervention of the expert witness to report on the subject of litigation, and to give an opinion about possible causes and solutions. This field is increasingly importantly for the practice of construction professional that requires an important specialization. The expert provides his knowledge to the judge in the matter he is dealing with (construction, planning, assessment, legal, ...), providing arguments or reasons as the base for his case and acting as part of the evidence. Although the importance of expert intervention in the judicial process, the responsibilities arising from their activity is a slightly studied field. Therefore, the study has as purpose to think about the regulation of professional activities raising different aims. The first is to define the action of the construction professional-expert witness and the need for expert evidence, establishing the legal implications of this professional activity. The different types of responsibilities (the civil, criminal and administrative) have been established as well as the economic, penal or disciplinary damages that can be derived from the expert report

Adaptive fuzzy knowledge-based systems for control metabots' mobility on virtual environments

The confluence of three-dimensional (3D) virtual worlds with social networks imposes on software agents, in addition to conversational functions, the same behaviours as those common to human-driven avatars. In this paper, we explore the possibilities of the use of metabots (metaverse robots) with motion capabilities in complex virtual 3D worlds and we put forward a learning model based on the techniques used in evolutionary computation for optimizing the fuzzy controllers which will subsequently be used by metabots for moving around a virtual environment.

QL-CONST1: an expert system for quality level prediction in concrete structures

Current trends in the fields of artifical intelligence and expert systems are moving towards the exciting possibility of reproducing and simulating human expertise and expert behaviour into a knowledge base, coupled with an appropriate, partially ‘intelligent’, computer code. This paper deals with the quality level prediction in concrete structures using the helpful assistance of an expert system, QL-CONST1, which is able to reason about this specific field of structural engineering. Evidence, hypotheses and factors related to this human knowledge field have been codified into a knowledge base. This knowledge base has been prepared in terms of probabilities of the presence of either hypotheses or evidence and the conditional presence of both. Human experts in the fields of structural engineering and the safety of structures gave their invaluable knowledge and assistance to the construction of the knowledge base. Some illustrative examples for, the validation of the expert system behaviour are included.

Expert systems for quality prediction in structural engineering

This work deals with quality level prediction in concrete structures through the helpful assistance of an expert system wich is able to apply reasoning to this field of structural engineering. Evidences, hypotheses and factors related to this human knowledge field have been codified into a Knowledge Base in terms of probabilities for the presence of either hypotheses or evidences,and conditional presence of both. Human experts in structural engineering and safety of structures gave their invaluable knowledge and assistance necessary when constructing the "computer knowledge body".

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

A Knowledge Model for Automatic Configuration of Traffic Messages

This paper describes a knowledge model for a configuration problem in the do-main of traffic control. The goal of this model is to help traffic engineers in the dynamic selection of a set of messages to be presented to drivers on variable message signals. This selection is done in a real-time context using data recorded by traffic detectors on motorways. The system follows an advanced knowledge-based solution that implements two abstract problem solving methods according to a model-based approach recently proposed in the knowledge engineering field. Finally, the paper presents a discussion about the advantages and drawbacks found for this problem as a consequence of the applied knowledge modeling ap-proach.

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.