Aplicación de Process Mining para modelos de comportamiento en entornos de Ambient Intelligence

Esta memoria es el resultado de un proyecto cuyo objetivo ha sido realizar un análisis de la posible aplicación de técnicas relativas al Process Mining para entornos AmI (Ambient Intelligence). Dicho análisis tiene la facultad de presentar de forma clara los resultados extraídos de los procesos relativos a un caso de uso planteado, así como de aplicar dichos resultados a aplicaciones relativas a entornos AmI, como automatización de tareas o simulación social basada en agentes. Para que dicho análisis sea comprensible por el lector, se presentan detalladas explicaciones de los conceptos tratados y las técnicas empleadas. Además, se analizan exhaustivamente las dos herramientas software más utilizadas en cuanto a minería de procesos se refiere, ProM y Disco, presentando ventajas e inconvenientes de cada una, así como una comparación entre las dos. Posteriormente se ha desarrollado una metodología para el análisis de procesos con la herramienta ProM, anteriormente mencionada, explicando cuidadosamente cada uno de los pasos así como los fundamentos de los algoritmos utilizados. Por último, se han presentado las conclusiones extraídas del trabajo, así como las posibles líneas de continuación del proyecto.

Estudio de las posibilidades de aplicación de los conceptos de urban mining y landfill mining

Como la demanda de la sociedad de metal aumenta, la tasa de extracción de minerales hace lo mismo. Esto contribuye al aumento de las implicaciones ambientales en forma de emisiones y el agotamiento de los recursos naturales. El reciclaje es una fuente importante para satisfacer la demanda de metales; como mucho un 30% de la demanda de metal está cubierto por el reciclaje en algunos mercados. Otra forma de reciclaje es la práctica de Urban Mining. El presente trabajo estudia la potencialidad del Landfill Mining en los vertederos españoles. Este concepto denomina el proceso de recuperación de materiales residuales depositados en vertederos para su uso posterior como materiales secundarios y, cuando ello no es posible, para su reaprovechamiento energético. Como consecuencia esto implica el cumplimiento de un segundo objetivo: la reducción o mitigación de las emisiones de gases de efecto invernadero derivadas de la presencia de residuos en vertederos.

Incremental lines of von Ebner in dinosaurs and the assessment of tooth replacement rates using growth line counts

Dinosaur dentine exhibits growth lines that are tens of micrometers in width. These laminations are homologous to incremental lines of von Ebner found in extant mammal and crocodilian teeth (i.e., those of amniotes). The lines likely reflect daily dentine formation, and they were used to infer tooth development and replacement rates. In general, dinosaur tooth formation rates negatively correlated with tooth size. Theropod tooth replacement rates negatively correlated with tooth size, which was due to limitations in the dentine formation rates of their odontoblasts. Derived ceratopsian and hadrosaurian dinosaurs retained relatively rapid tooth replacement rates through ontogeny. The evolution of dental batteries in hadrosaurs and ceratopsians can be explained by dentine formation constraints and rapid tooth wear. In combination with counts of shed dinosaur teeth, tooth replacement rate data can be used to assess population demographics of Mesozoic ecosystems. Finally, it is of historic importance to note that Richard Owen appears to have been the first to observe incremental lines of von Ebner in dinosaurs more than 150 years ago.

Rapid generation of incremental truncation libraries for protein engineering using α-phosphothioate nucleotides

Incremental truncation for the creation of hybrid enzymes (ITCHY) is a novel tool for the generation of combinatorial libraries of hybrid proteins independent of DNA sequence homology. We herein report a fundamentally different methodology for creating incremental truncation libraries using nucleotide triphosphate analogs. Central to the method is the polymerase catalyzed, low frequency, random incorporation of α-phosphothioate dNTPs into the region of DNA targeted for truncation. The resulting phosphothioate internucleotide linkages are resistant to 3′→5′ exonuclease hydrolysis, rendering the target DNA resistant to degradation in a subsequent exonuclease III treatment. From an experimental perspective the protocol reported here to create incremental truncation libraries is simpler and less time consuming than previous approaches by combining the two gene fragments in a single vector and eliminating additional purification steps. As proof of principle, an incremental truncation library of fusions between the N-terminal fragment of Escherichia coli glycinamide ribonucleotide formyltransferase (PurN) and the C-terminal fragment of human glycinamide ribonucleotide formyltransferase (hGART) was prepared and successfully tested for functional hybrids in an auxotrophic E.coli host strain. Multiple active hybrid enzymes were identified, including ones fused in regions of low sequence homology.

Uso de Data Mining e do ECR para incremento da competitividade de pequenas e médias empresas: um estudo multicaso sobre varejo e indústria alimentícia

O setor supermercadista sofreu grandes alterações nos últimos anos, principalmente com o avanço das tecnologias, a competição, a concentração e algumas insuficiências em seus processos. Estes e outros fatores favoreceram ao surgimento do movimento de ECR (Resposta de Consumidor Eficiente) que procura criar um relacionamento mais forte entre indústria e varejo através de novas visões para suas estratégias operacionais. A evolução das tecnologias de informação permitiram ao setor varejista gerar uma maior volume de dados a partir, principalmente, de seus check-outs. Entretanto, estes dados nem sempre são armazenados de forma correta ou utilizados de forma a se aproveitar a plenitude das informações neles contidas. O processo de transformar os dados em informação e conhecimento vem evoluindo constantemente. Uma das atuais metodologias de trabalhar dados é o Data Mining ou Mineração de Dados, que pode ser descrito como sendo uma variedade de ferramentas e estratégias que processam dados aumentando a utilidade destes em bancos de dados. Este trabalho analisa através de um estudo multicaso exploratório na região de Ribeirão Preto, no interior de São Paulo, a avaliação da capacidade do uso da tecnologia Data Mining para o fortalecimento do movimento ECR, principalmente em pequenos e médios varejistas e indústrias alimentícias, no sentido de oferecer a estes um diferencial de negociação para formação de alianças estratégias.

Diseño y desarrollo de un servicio big data en la nube para búsqueda, compartición de ficheros y data mining

En esta memoria se presenta el diseño y desarrollo de una aplicación en la nube destinada a la compartición de objetos y servicios. El desarrollo de esta aplicación surge dentro del proyecto de I+D+i, SITAC: Social Internet of Things – Apps by and for the Crowd ITEA 2 11020, que trata de crear una arquitectura integradora y un “ecosistema” que incluya plataformas, herramientas y metodologías para facilitar la conexión y cooperación de entidades de distinto tipo conectadas a la red bien sean sistemas, máquinas, dispositivos o personas con dispositivos móviles personales como tabletas o teléfonos móviles. El proyecto innovará mediante la utilización de un modelo inspirado en las redes sociales para facilitar y unificar las interacciones tanto entre personas como entre personas y dispositivos. En este contexto surge la necesidad de desarrollar una aplicación destinada a la compartición de recursos en la nube que pueden ser tanto lógicos como físicos, y que esté orientada al big data. Ésta será la aplicación presentada en este trabajo, el “Resource Sharing Center”, que ofrece un servicio web para el intercambio y compartición de contenido, y un motor de recomendaciones basado en las preferencias de los usuarios. Con este objetivo, se han usado tecnologías de despliegue en la nube, como Elastic Beanstalk (el PaaS de Amazon Web Services), S3 (el sistema de almacenamiento de Amazon Web Services), SimpleDB (base de datos NoSQL) y HTML5 con JavaScript y Twitter Bootstrap para el desarrollo del front-end, siendo Python y Node.js las tecnologías usadas en el back end, y habiendo contribuido a la mejora de herramientas de clustering sobre big data. Por último, y de cara a realizar el estudio sobre las pruebas de carga de la aplicación se ha usado la herramienta ApacheJMeter.

The Exploration and Development of Alternative Management Strategies for Mining and Recreational Activities Atop Mount Antero and Mount White; Chaffee County, Colorado

The Mount Antero/White area is a popular prospecting area. Recent expansions in the recreation economy is drawing more visitors to the area. Consequently, visitors may be placing unsustainable pressures on the landscape. In order to help rectify this, the legal, ecological, geologic, aesthetic, recreational, historic, social, and economic character of the Antero/White area has been identified. Four feasible management alternatives have also been recognized. They are a) take no new management actions, b) prohibit motorized activities in the area, c) develop a mineralogical park, and d) a combination of options b and c. Option C has been defended, as it best balances the desires of area users with the underlying ecological and geological character of the area.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Forensic analysis of buildings affected by mining subsidence based on Differential Interferometry (Part III)

This work presents a forensic analysis of buildings affected by mining subsidence, which is based on deformation data obtained by Differential Interferometry (DInSAR). The proposed test site is La Union village (Murcia, SE Spain) where subsidence was triggered in an industrial area due to the collapse of abandoned underground mining labours occurred in 1998. In the first part of this work the study area was introduced, describing the spatial and temporal evolution of ground subsidence, through the elaboration of a cracks map on the buildings located within the affected area. In the second part, the evolution of the most significant cracks found in the most damaged buildings was monitored using biaxial extensometric units and inclinometers. This article describes the work performed in the third part, where DInSAR processing of satellite radar data, available between 1998 and 2008, has permitted to determine the spatial and temporal evolution of the deformation of all the buildings of the study area in a period when no continuous in situ instrumental data is available. Additionally, the comparison of these results with the forensic data gathered in the 2005–2008 period, reveal that there is a coincidence between damaged buildings, buildings where extensometers register significant movements of cracks, and buildings deformation estimated from radar data. As a result, it has been demonstrated that the integration of DInSAR data into forensic analysis methodologies contributes to improve significantly the assessment of the damages of buildings affected by mining subsidence.

Evaluating the robustness of EmotiBlog for sentiment analysis and opinion mining

Preliminary research demonstrated the EmotiBlog annotated corpus relevance as a Machine Learning resource to detect subjective data. In this paper we compare EmotiBlog with the JRC Quotes corpus in order to check the robustness of its annotation. We concentrate on its coarse-grained labels and carry out a deep Machine Learning experimentation also with the inclusion of lexical resources. The results obtained show a similarity with the ones obtained with the JRC Quotes corpus demonstrating the EmotiBlog validity as a resource for the SA task.

EmotiBlog: towards a finer-grained sentiment analysis and its application to opinion mining

Comunicación presentada en las IV Jornadas TIMM, Torres (Jaén), 7-8 abril 2011.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.