Assembling models of embryo development: Image analysis and the construction of digital atlases

Digital atlases of animal development provide a quantitative description of morphogenesis, opening the path toward processes modeling. Prototypic atlases offer a data integration framework where to gather information from cohorts of individuals with phenotypic variability. Relevant information for further theoretical reconstruction includes measurements in time and space for cell behaviors and gene expression. The latter as well as data integration in a prototypic model, rely on image processing strategies. Developing the tools to integrate and analyze biological multidimensional data are highly relevant for assessing chemical toxicity or performing drugs preclinical testing. This article surveys some of the most prominent efforts to assemble these prototypes, categorizes them according to salient criteria and discusses the key questions in the field and the future challenges toward the reconstruction of multiscale dynamics in model organisms.

MEDVIR: 3D visual interface applied to gene profile analisys.

The origins for this work arise in response to the increasing need for biologists and doctors to obtain tools for visual analysis of data. When dealing with multidimensional data, such as medical data, the traditional data mining techniques can be a tedious and complex task, even to some medical experts. Therefore, it is necessary to develop useful visualization techniques that can complement the expert’s criterion, and at the same time visually stimulate and make easier the process of obtaining knowledge from a dataset. Thus, the process of interpretation and understanding of the data can be greatly enriched. Multidimensionality is inherent to any medical data, requiring a time-consuming effort to get a clinical useful outcome. Unfortunately, both clinicians and biologists are not trained in managing more than four dimensions. Specifically, we were aimed to design a 3D visual interface for gene profile analysis easy in order to be used both by medical and biologist experts. In this way, a new analysis method is proposed: MedVir. This is a simple and intuitive analysis mechanism based on the visualization of any multidimensional medical data in a three dimensional space that allows interaction with experts in order to collaborate and enrich this representation. In other words, MedVir makes a powerful reduction in data dimensionality in order to represent the original information into a three dimensional environment. The experts can interact with the data and draw conclusions in a visual and quickly way.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Data Processing Modeling in Decision Support Systems.

Due to the advancement of both, information technology in general, and databases in particular; data storage devices are becoming cheaper and data processing speed is increasing. As result of this, organizations tend to store large volumes of data holding great potential information. Decision Support Systems, DSS try to use the stored data to obtain valuable information for organizations. In this paper, we use both data models and use cases to represent the functionality of data processing in DSS following Software Engineering processes. We propose a methodology to develop DSS in the Analysis phase, respective of data processing modeling. We have used, as a starting point, a data model adapted to the semantics involved in multidimensional databases or data warehouses, DW. Also, we have taken an algorithm that provides us with all the possible ways to automatically cross check multidimensional model data. Using the aforementioned, we propose diagrams and descriptions of use cases, which can be considered as patterns representing the DSS functionality, in regard to DW data processing, DW on which DSS are based. We highlight the reusability and automation benefits that this can be achieved, and we think this study can serve as a guide in the development of DSS.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Optimization of multidimensional cross-section tables for few-group core calculations

Multigroup diffusion codes for three dimensional LWR core analysis use as input data pre-generated homogenized few group cross sections and discontinuity factors for certain combinations of state variables, such as temperatures or densities. The simplest way of compiling those data are tabulated libraries, where a grid covering the domain of state variables is defined and the homogenized cross sections are computed at the grid points. Then, during the core calculation, an interpolation algorithm is used to compute the cross sections from the table values. Since interpolation errors depend on the distance between the grid points, a determined refinement of the mesh is required to reach a target accuracy, which could lead to large data storage volume and a large number of lattice transport calculations. In this paper, a simple and effective procedure to optimize the distribution of grid points for tabulated libraries is presented. Optimality is considered in the sense of building a non-uniform point distribution with the minimum number of grid points for each state variable satisfying a given target accuracy in k-effective. The procedure consists of determining the sensitivity coefficients of k-effective to cross sections using perturbation theory; and estimating the interpolation errors committed with different mesh steps for each state variable. These results allow evaluating the influence of interpolation errors of each cross section on k-effective for any combination of state variables, and estimating the optimal distance between grid points.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Modelo multidimensional de los factores que afectan la interacción entre marketing y ventas en el lanzamiento de nuevos productos en Latino América

Esta tesis se centra en la identificación y análisis de los factores que pueden favorecer o actuar como barreras del éxito de la implementación de la innovación y las relaciones entre sí, desde el enfoque de la interface marketing-ventas. El trabajo empírico se enmarca en el vacío de investigación existente en el campo del proceso de lanzamiento de nuevos productos en los mercados donde operan subsidiarias de empresas multinacionales de consumo masivo (FMCG). Las empresas FMCG son altamente dependientes de la innovación como proceso clave determinante del crecimiento competitivo de mediano y largo plazo. En un contexto de acortamiento del ciclo de vida de los productos, como resultado del desarrollo tecnológico y científico que impactan en el comportamiento de los consumidores, las empresas invierten un mayor nivel de recursos en el desarrollo de nuevos productos, reingeniería y programas de innovación (Mundra, Gulati y Gupta, 2013). Sin embargo, a pesar del aumento en la inversión, las tasas de éxito de la innovación reportadas son inferiores al 25% (Evanschitzky, Eisend, Calantone y Jiang, 2012). Aumentar las tasas de éxito de los proyectos de innovación es reconocida en la literatura como un elemento clave para la supervivencia y competitividad de las empresas, para ser superiores a su competencia y desarrollar nuevos modelos de negocios. A pesar de la existencia de estudios que intentan comprender el proceso de lanzamiento de nuevos productos, no se ha identificado un claro prototipo de gestión de la innovación (Gupta et al, 2007). Profundizando en los factores de éxito, los autores Keupp, Palmié y Gassman (2012) reconocen que la innovación exitosa no depende solamente de la estrategia de selección de los proyectos de innovación, sino también la forma en que los mismos son implementados (Klein and Sorra, 1996; Repenning, 2002; Keupp, Palmié y Gassmann, 2012). Al analizar la implementación de los proyectos de lanzamiento de nuevos productos al mercado, en empresas FMCG, dicho proceso es responsabilidad principalmente de las funciones de marketing y ventas a través de la comunicación con los consumidores y los clientes respectivamente (Ernst, Hoyer y Rubsaamen, 2010). Es decir que el éxito en la implementación de la innovación requiere la gestión efectiva de la relación inter-funcional entre marketing y ventas (Ernst, Hoyer y Rubsaamen, 2010; Hughes, Le Bon y Malshe, 2012). A pesar de la importancia de la integración entre marketing y ventas en la conceptualización e implementación de la innovación, este tema no ha sido estudiado en profundidad (Hughes, Le Bon y Malshe, 2012; Keupp, Palmié y Gassmann, 2012). En las empresas multinacionales, está demostrado que el desempeño de las subsidiarias determinan el éxito competitivo de la empresa a nivel global. El desafío de dichas subsidiarias es conjugar el desarrollo global de innovación y comunicación con las características locales de comportamiento del consumidor y el mercado. Por lo tanto, esta investigación empírica responde a la pregunta académica y de gestión acerca de cómo mejorar las tasas de éxito de lanzamiento de nuevos productos al mercado en subsidiarias de empresas de consumo masivo, desde la perspectiva de la relación entre marketing y ventas. En particular analiza cómo afectan la formalización de los procesos y los mecanismos de comunicación a la confianza interpersonal y a la efectividad de la interface marketing-ventas y dichos factores a su vez sobre la planificación integrada de la implementación de la innovación. La determinación de los factores o ítems que conforman cada uno de los constructos del proceso de ejecución de la innovación, se llevó a cabo a partir de una revisión exhaustiva del estado del arte de la literatura sobre las interfaces funcionales y el proceso de innovación. Posteriormente, los ítems seleccionados (más de 50 en total) fueron validados por referentes de marketing y ventas de Argentina y Uruguay a través de entrevistas en profundidad. A partir de los factores identificados se construyeron dos modelos teóricos: • (1) relativo a la influencia de las dimensiones de confianza interpersonal sobre la efectividad de las uniones inter-funcionales y como los mecanismos organizacionales, tales como la frecuencia y la calidad de la comunicación entre las áreas, afectan la confianza y la relación entre ellas; • (2) relativo a la dimensión planificación integrada de la implementación de la innovación, ya que durante el lanzamiento de nuevos productos al mercado, marketing y ventas utilizan procesos formales que facilitan la comunicación frecuente y efectiva, desarrollando confianza inter-personal que no solamente afecta la efectividad de su relación sino también el desarrollo de planes integrados entre ambas áreas. El estudio fue llevado a cabo en una empresa multinacional de consumo masivo que integra la lista Global 500 (Fortune, 2015), presente en todo el mundo con más de 25 marcas participantes en más de 15 categorías, implementando 150 proyectos de innovación en el último año. El grupo de subsidiarias en estudio fue reconocido a nivel mundial por su desempeño en crecimiento competitivo y su alta contribución al crecimiento total. El modelo analizado en esta tesis fue expandido al resto de América Latina, tratándose entonces de un caso ejemplar que representa una práctica de excelencia en la implementación de la innovación en subsidiarias de una empresa multinacional. La recolección de los datos fue llevado a cabo a través de un cuestionario estructurado y confidencial, enviado a la base de datos de todo el universo de directores y gerentes de marketing y ventas. El nivel de respuesta fue muy elevado (70%), logrando 152 casos válidos. El análisis de datos comprendió el análisis descriptivo de los mismos, estimación de fiabilidad y análisis factorial exploratorio a través del software SPSS v.20. El análisis factorial confirmatorio y el análisis de senderos para examinar las relaciones entre los factores se estudiaron mediante el software R (Package 2.15.1., R Core Team, 2012) (Fox, 2006). Finalmente se llevaron a cabo entrevistas en profundidad a gerentes de marketing y ventas de cada uno de los seis países con el fin de profundizar en los constructos y sus relaciones. Los resultados de los modelos demuestran que la frecuencia de comunicación impacta positivamente en la calidad de la misma, que a su vez afecta directamente la relación entre marketing y ventas. Adicionalmente, la calidad de la comunicación impacta sobre la confianza cognitiva, que a su vez se relaciona no solamente con la confianza afectiva sino también con la relación entre ambas áreas. Esto significa que para mejorar la implementación de la innovación, los gerentes deberían orientarse a reforzar la relación entre marketing y ventas facilitando la construcción de confianza interpersonal primero cognitiva y luego afectiva, incrementando la frecuencia de la comunicación que alimenta la calidad de la comunicación entre ambas áreas. A través del segundo modelo se demuestra que durante el lanzamiento de nuevos productos al mercado, marketing y ventas necesitan emplear procesos formales que faciliten la comunicación frecuente y efectiva. De esta forma se contrarresta el efecto negativo de la formalización sobre la planificación integrada entre ambas áreas. Adicionalmente, los gerentes de ambos departamentos deberían promover la construcción de confianza interpersonal, no solamente para mejorar la efectividad de la relación, sino también para desarrollar planes integrados de implementación de nuevos productos. Finalmente, se valida que la frecuencia de la comunicación, la confianza afectiva y la relación marketing-ventas, se relacionan positivamente con la planificación integrada en la implementación de la innovación. El estudio contribuye a la comprensión de los factores que las empresas pueden emplear para mejorar la relación inter-funcional entre marketing y ventas y la implementación de la innovación en empresas de consumo masivo. El aporte de esta investigación puede ser valorado de dos maneras, los aportes a la gestión y a la academia. Desde el punto de vista empresarial, provee a los líderes al frente de empresas de consumo masivo, del conocimiento sobre los factores que afectan la implementación de la innovación y en definitiva el éxito del negocio a mediano y largo plazo. Desde el punto de vista académico aporta al conocimiento del proceso de implementación de la innovación y en la efectividad de la interface marketing y ventas en un caso de buenas prácticas en el mercado de consumo masivo. A su vez incorpora por primera vez un estudio empírico en geografías emergentes capaces de recuperar el camino de crecimiento posterior a una profunda crisis económica a través de la exitosa implementación de la innovación en sus mercados. ABSTRACT This thesis is focused on the identification, analysis and relationship study of factors which may benefit or hinder the successful deployment of innovation, from a marketing-sales interface perspective. Considering the non-existent investigation dedicated to the study of new products launches into markets in which Fast Moving Consumer Goods (FMCG) companies’ subsidiaries operate, it is that this investigation has been carried out. FMCG companies rely on innovation as their key process for a competitive growth on a medium and long term basis. Nowadays, the life-cycle of products is getting shorter as a result of new technological and scientific development, having impact on consumer behavior, and therefore companies are forced to dedicating more resources to the development of new products, reengineering and innovation programs (Mundra, Gulati and Gupta, 2013). However, in spite of the investment increase, the innovation success rates have been reported to be lower than 25% (Evanschitzky, Eisend, Calantone y Jiang, 2012). Increasing success rates on innovation processes has been considered as a key element on the survival and competitiveness of companies, outperforming competitors and developing new business models. Despite new studies which try to comprehend the process of new products launch, a prototype of innovation management has not yet been identified (Gupta et al, 2007). Emphasizing on success factors, authors Keupp, Palmié and Gassman (2012) recognize that successful innovation does not solely depend on innovation processes’ selection strategy, but it is also based on the way in which these are implemented (Klein and Sorra, 1996; Repenning, 2002; Keupp, Palmié y Gassmann, 2012). While analyzing the implementation of projects for new products releases on massive consumption companies, the two departments in charge of taking this forward are marketing and sales, by focusing on communication strategies with consumers and clients respectively (Ernst, Hoyer y Rubsaamen, 2010). This means that having success on innovation implementation requires an effective management of inter-functional relationship among marketing and sales (Ernst, Hoyer y Rubsaamen, 2010; Hughes, Le Bon y Malshe, 2012). In spite of the importance on the integration between marketing and sales on the conceptualization and implementation of innovation, this subject has not been studied in depth (Hughes, Le Bon y Malshe, 2012; Keupp, Palmié y Gassmann, 2012). In multinational companies, previous research has confirmed that the performance of their subsidiaries determine the competitive success of the company on a global scale. The challenge of said subsidiaries is to conjugate the global innovation development and communication with the local consumer and market behavior. Therefore, this empirical study aims to respond to the academic and management question of how to improve the success rates of new product launches into MNE subsidiary’ markets, from a marketing-sales relationship perspective. Particularly, this investigation analyses how the formalization of products and communication mechanisms affect interpersonal trust and marketing-sales interface effectiveness and also on how these factors affect the overall planning of the implementation of innovation. The determination of which factors build the hypothesis of the innovation execution process was taken forward through an extensive research on the extant literature on functional interfaces and innovation processes. More than 50 items were selected which were in turn validated by marketing and sales referents on Uruguay and Argentina through in depth interviews. Based on the identified factors, two theoretical models were proposed: (1) Relative to the influence that interpersonal trust dimensions have on inter functional linkages effectiveness and how organizational mechanisms such as frequency and quality of communication between departments affect trust and their relationship. (2) Relative to the integrated planning and innovation implementation dimensions. Marketing and sales department use formal process thus allowing inter-personal trust, which affects positively their relationship and also enables the development of integrated planning between them. The study was performed within a massive consumption company which is part of the “Global 500” (Fortune, 2015), with subsidiaries all over the world and more than 25 participant brands in 15 categories, having implemented over 150 innovation projects in the year under study. The analyzed subsidiary group has been awarded worldwide for their performance in competitive growth and their high contribution to the total growth. The model being analyzed in this thesis was implemented throughout Latin America, representing a remarkable case of innovation implementation excellence for subsidiaries of multinational companies. Data recollection was carried out through a structured and confidential questionnaire, sent to the universe of marketing-sales directors and managers’ database available with a high level of responsiveness of 70%, resulting in 152 valid cases. Data exploration involved a descriptive analysis, followed by a reliability estimation and an exploratory factorial analysis carried out through SPSS v.20. Confirmatory factorial analysis and path analysis (to examine relations between the different study factors) were studied using “R” software (Package 2.15.1., R Core Team, 2012) (Fox, 2006). Finally, in depth interviews were carried out to several marketing and sales managers in each of the six countries so as to further confirm the hypothesis and their relations. The models results prove that communication frequency has a positive impact on the quality of the same, which in turn has direct effects on the marketing-sales relations. Additionally, communication quality has an impact on the cognitive trust, which also relates not only to affective trust, but also to the relation between both areas. This means that in order to improve the implementation of innovation, managers should strive to enforce marketing-sales relations, facilitating the interpersonal trust construction (firstly cognitive, followed by affective trust), increasing the communication frequency, and therefore nurturing the communication quality among both areas. Through the second model, the results confirm the importance of creating effective relationships between sales and marketing to facilitate planning integrated new product implementations. While formalized new product development processes provide opportunities for sales and marketing to communicate, this does not directly influence the planning of integrated new product implementations. By using these formal opportunities to communicate to create information quality, it is possible to improve sales and marketing’s ability to integrate information during the planning process. Further, communication quality creates inter-personal trust in the other party’s competences (cognitive-based trust), leading to affect-based trust. Affect-based inter-personal trust, not only to improve the overall effectiveness of the sales and marketing relationship, but also helps in planning integrated new product implementations. This study contributes to the understanding of factors which enterprises can use to improve the inter-functional relations between marketing and sales, and the implementation of innovation in FMCG companies. The contribution of this investigation can be measured in two ways: enrichment of management and contribution to the academic area. From a business perspective, it provides massive consumption businesses leaders with knowledge on which factors affect innovation implementation, which results on mid and long-term success for the company. From an academic point of view, it provides knowledge on a prototype of successful innovation implementation management based on the marketing-sales interface effectiveness through a case study in the FMCG consumption market. Last but not least, it incorporates for the first time an empiric study on emerging geographies capable of recovery post a deep economic crisis through successful innovation implementation on their markets.