Nuevos retos en clasificación asociativa: Big Data y aplicaciones

La clasificación asociativa surge como resultado de la unión de dos importantes ámbitos del aprendizaje automático. Por un lado la tarea descriptiva de extracción de reglas de asociación, como mecanismo para obtener información previamente desconocida e interesante de un conjunto de datos, combinado con una tarea predictiva, como es la clasificación, que permite en base a un conjunto de variables explicativas y previamente conocidas realizar una predicción sobre una variable de interés o predictiva. Los objetivos de esta tesis doctoral son los siguientes: 1) El estudio y el análisis del estado del arte de tanto la extracción de reglas de asociación como de la clasificación asociativa; 2) La propuesta de nuevos modelos de clasificación asociativa así como de extracción de reglas de asociación teniendo en cuenta la obtención de modelos que sean precisos, interpretables, eficientes así como flexibles para poder introducir conocimiento subjetivo en éstos. 3) Adicionalmente, y dado la gran cantidad de datos que cada día se genera en las últimas décadas, se prestará especial atención al tratamiento de grandes cantidades datos, también conocido como Big Data. En primer lugar, se ha analizado el estado del arte tanto de clasificación asociativa como de la extracción de reglas de asociación. En este sentido, se ha realizado un estudio y análisis exhaustivo de la bibliografía de los trabajos relacionados para poder conocer con gran nivel de detalle el estado del arte. Como resultado, se ha permitido sentar las bases para la consecución de los demás objetivos así como detectar que dentro de la clasificación asociativa se requería de algún mecanismo que facilitara la unificación de comparativas así como que fueran lo más completas posibles. Para tal fin, se ha propuesto una herramienta de software que cuenta con al menos un algoritmo de todas las categorías que componen la taxonomía actual. Esto permitirá dentro de las investigaciones del área, realizar comparaciones más diversas y completas que hasta el momento se consideraba una tarea en el mejor de los casos muy ardua, al no estar disponibles muchos de los algoritmos en un formato ejecutable ni mucho menos como código abierto. Además, esta herramienta también dispone de un conjunto muy diverso de métricas que permite cuantificar la calidad de los resultados desde diferentes perspectivas. Esto permite conseguir clasificadores lo más completos posibles, así como para unificar futuras comparaciones con otras propuestas. En segundo lugar, y como resultado del análisis previo, se ha detectado que las propuestas actuales no permiten escalar, ni horizontalmente, ni verticalmente, las metodologías sobre conjuntos de datos relativamente grandes. Dado el creciente interés, tanto del mundo académico como del industrial, de aumentar la capacidad de cómputo a ingentes cantidades de datos, se ha considerado interesante continuar esta tesis doctoral realizando un análisis de diferentes propuestas sobre Big Data. Para tal fin, se ha comenzado realizando un análisis pormenorizado de los últimos avances para el tratamiento de tal cantidad de datos. En este respecto, se ha prestado especial atención a la computación distribuida ya que ha demostrado ser el único procedimiento que permite el tratamiento de grandes cantidades de datos sin la realización de técnicas de muestreo. En concreto, se ha prestado especial atención a las metodologías basadas en MapReduce que permite la descomposición de problemas complejos en fracciones divisibles y paralelizables, que posteriormente pueden ser agrupadas para obtener el resultado final. Como resultado de este objetivo se han propuesto diferentes algoritmos que permiten el tratamiento de grandes cantidades de datos, sin la pérdida de precisión ni interpretabilidad. Todos los algoritmos propuestos se han diseñado para que puedan funcionar sobre las implementaciones de código abierto más conocidas de MapReduce. En tercer y último lugar, se ha considerado interesante realizar una propuesta que mejore el estado del arte de la clasificación asociativa. Para tal fin, y dado que las reglas de asociación son la base y factores determinantes para los clasificadores asociativos, se ha comenzado realizando una nueva propuesta para la extracción de reglas de asociación. En este aspecto, se ha combinado el uso de los últimos avances en computación distribuida, como MapReduce, con los algoritmos evolutivos que han demostrado obtener excelentes resultados en el área. En particular, se ha hecho uso de programación genética gramatical por su flexibilidad para codificar las soluciones, así como introducir conocimiento subjetivo en el proceso de búsqueda a la vez que permiten aliviar los requisitos computacionales y de memoria. Este nuevo algoritmo, supone una mejora significativa de la extracción de reglas de asociación ya que ha demostrado obtener mejores resultados que las propuestas existentes sobre diferentes tipos de datos así como sobre diferentes métricas de interés, es decir, no sólo obtiene mejores resultados sobre Big Data, sino que se ha comparado en su versión secuencial con los algoritmos existentes. Una vez que se ha conseguido este algoritmo que permite extraer excelentes reglas de asociación, se ha adaptado para la obtención de reglas de asociación de clase así como para obtener un clasificador a partir de tales reglas. De nuevo, se ha hecho uso de programación genética gramatical para la obtención del clasificador de forma que se permite al usuario no sólo introducir conocimiento subjetivo en las propias formas de las reglas, sino también en la forma final del clasificador. Esta nueva propuesta también se ha comparado con los algoritmos existentes de clasificación asociativa forma secuencial para garantizar que consigue diferencias significativas respecto a éstos en términos de exactitud, interpretabilidad y eficiencia. Adicionalmente, también se ha comparado con otras propuestas específicas de Big Data demostrado obtener excelentes resultados a la vez que mantiene un compromiso entre los objetivos conflictivos de interpretabilidad, exactitud y eficiencia. Esta tesis doctoral se ha desarrollado bajo un entorno experimental apropiado, haciendo uso de diversos conjunto de datos incluyendo tanto datos de pequeña dimensionalidad como Big Data. Además, todos los conjuntos de datos usados están publicados libremente y conforman un conglomerado de diversas dimensionalidades, número de instancias y de clases. Todos los resultados obtenidos se han comparado con el estado de arte correspondiente, y se ha hecho uso de tests estadísticos no paramétricos para comprobar que las diferencias encontradas son significativas desde un punto de vista estadístico, y no son fruto del azar. Adicionalmente, todas las comparaciones realizadas consideran diferentes perspectivas, es decir, se ha analizado rendimiento, eficiencia, precisión así como interpretabilidad en cada uno de los estudios.This Doctoral Thesis aims at solving the challenging problem of associative classification and its application on very large datasets. First, associative classification state-of-art has been studied and analyzed, and a new tool covering the whole taxonomy of algorithms as well as providing many different measures has been proposed. The goal of this tool is two-fold: 1) unification of comparisons, since existing works compare with very different measures; 2) providing a unique tool which has at least one algorithm of each category forming the taxonomy. This tool is a very important advancement in the field, since until the moment the whole taxonomy has not been covered due to that many algorithms have not been released as open source nor they were available to be run. Second, AC has been analyzed on very large quantities of data. In this regard, many different platforms for distributed computing have been studied and different proposals have been developed on them. These proposals enable to deal with very large data in a efficient way scaling up the load on very different compute nodes. Third, as one of the most important part of the associative classification is to extract high quality rules, it has been proposed a novel grammar-guided genetic programming algorithm which enables to obtain interesting association rules with regard to different metrics and in different kinds of data, including truly Big Data datasets. This proposal has proved to obtain very good results in terms of both quality and interpretability, at the same time of providing a very flexible way of representing the solutions and enabling to introduce subjective knowledge in the search process. Then, a novel algorithm has been proposed for associative classification using a non-trivial adaptation of the aforementioned algorithm to obtain the rules forming the classifier. This methodology is also based on grammar-guided genetic programming enabling user not only to constrain the form of the rules, but the final form of the classifier. Results have proved that this algorithm obtains very accurate classifiers at the same time of maintaining a good level of interpretability. All the methodologies proposed along this Thesis has been evaluated using a proper experimental framework, using a varied set of datasets including both classical and Big Data dataset, and analyzing different metrics to quantify the quality of the algorithms with regard to different perspectives. Results have been compared with state-of-the-art and they have been verified by means of non-parametric statistical tests proving that the proposed methods overcome to existing approaches

Semi-automatic exploratory data analytics for actionable discoveries through subgroup mining

People are born with the curiosity to see differences between groups. These differences are useful for understanding the root causes of certain discrepancies, such as populations and diseases. However, without prior knowledge of the data, it is extremely challenging to identify which groups differ most, let alone to discover what associations contribute to the differences. The challenges are mainly from the large searching space with complex data structure, as well as the lack of efficient quantitative measurements that are closely related to the meaning the differences. To tackle these issues, we developed a novel exploratory data mining method to identify ranked subgroups that are highly contrasted for further in-depth analyses. The underpinning components of this method include (1) a semi-greedy forward floating selection algorithm to reduce the search space, (2) a deep-exploring approach to aggregate a collection of sizable and creditable candidate feature sets for subgroups identification using in-memory computing techniques, (3) a G-index contrast measurement to guide the exploratory process and to evaluate the patterns of subgroup pairs, and (4) a ranking method to provide mined results from highly contrasted subgroups. Computational experiments were conducted on both synthesized and real data. The algorithm performed adequately in recognizing known subgroups and discovering new and unexpected subgroups. This exploratory data analysis method will provide a new paradigm to select data-driven hypotheses that will produce potentially successful actionable outcomes to tailor to subpopulations of individuals, such as consumers in E-commerce and patients in clinical trials.Includes biblographical reference

New internal and external validation indices for clustering in Big Data

Esta tesis, presentada como un compendio de artículos de investigación, analiza el concepto de índices de validación de clustering y aporta nuevas medidas de bondad para conjuntos de datos que podrían considerarse Big Data debido a su volumen. Además, estas medidas han sido aplicadas en proyectos reales y se propone su aplicación futura para mejorar algoritmos de clustering. El clustering es una de las técnicas de aprendizaje automático no supervisado más usada. Esta técnica nos permite agrupar datos en clusters de manera que, aquellos datos que pertenezcan al mismo cluster tienen características o atributos con valores similares, y a su vez esos datos son disimilares respecto a aquellos que pertenecen a los otros clusters. La similitud de los datos viene dada normalmente por la cercanía en el espacio, teniendo en cuenta una función de distancia. En la literatura existen los llamados índices de validación de clustering, los cuales podríamos definir como medidas para cuantificar la calidad de un resultado de clustering. Estos índices se dividen en dos tipos: índices de validación internos, que miden la calidad del clustering en base a los atributos con los que se han construido los clusters; e índices de validación externos, que son aquellos que cuantifican la calidad del clustering a partir de atributos que no han intervenido en la construcción de los clusters, y que normalmente son de tipo nominal o etiquetas. En esta memoria se proponen dos índices de validación internos para clustering basados en otros índices existentes en la literatura, que nos permiten trabajar con grandes cantidades de datos, ofreciéndonos los resultados en un tiempo razonable. Los índices propuestos han sido testeados en datasets sintéticos y comparados con otros índices de la literatura. Las conclusiones de este trabajo indican que estos índices ofrecen resultados muy prometedores frente a sus competidores. Por otro lado, se ha diseñado un nuevo índice de validación externo de clustering basado en el test estadístico chi cuadrado. Este índice permite medir la calidad del clustering basando el resultado en cómo han quedado distribuidos los clusters respecto a una etiqueta dada en la distribución. Los resultados de este índice muestran una mejora significativa frente a otros índices externos de la literatura y en datasets de diferentes dimensiones y características. Además, estos índices propuestos han sido aplicados en tres proyectos con datos reales cuyas publicaciones están incluidas en esta tesis doctoral. Para el primer proyecto se ha desarrollado una metodología para analizar el consumo eléctrico de los edificios de una smart city. Para ello, se ha realizado un análisis de clustering óptimo aplicando los índices internos mencionados anteriormente. En el segundo proyecto se ha trabajado tanto los índices internos como con los externos para realizar un análisis comparativo del mercado laboral español en dos periodos económicos distintos. Este análisis se realizó usando datos del Ministerio de Trabajo, Migraciones y Seguridad Social, y los resultados podrían tenerse en cuenta para ayudar a la toma de decisión en mejoras de políticas de empleo. En el tercer proyecto se ha trabajado con datos de los clientes de una compañía eléctrica para caracterizar los tipos de consumidores que existen. En este estudio se han analizado los patrones de consumo para que las compañías eléctricas puedan ofertar nuevas tarifas a los consumidores, y éstos puedan adaptarse a estas tarifas con el objetivo de optimizar la generación de energía eliminando los picos de consumo que existen la actualidad.This thesis, presented as a compendium of research articles, analyses the concept of clustering validation indices and provides new measures of goodness for datasets that could be considered Big Data. In addition, these measures have been applied in real projects and their future application is proposed for the improvement of clustering algorithms. Clustering is one of the most popular unsupervised machine learning techniques. This technique allows us to group data into clusters so that the instances that belong to the same cluster have characteristics or attributes with similar values, and are dissimilar to those that belong to the other clusters. The similarity of the data is normally given by the proximity in space, which is measured using a distance function. In the literature, there are so-called clustering validation indices, which can be defined as measures for the quantification of the quality of a clustering result. These indices are divided into two types: internal validation indices, which measure the quality of clustering based on the attributes with which the clusters have been built; and external validation indices, which are those that quantify the quality of clustering from attributes that have not intervened in the construction of the clusters, and that are normally of nominal type or labels. In this doctoral thesis, two internal validation indices are proposed for clustering based on other indices existing in the literature, which enable large amounts of data to be handled, and provide the results in a reasonable time. The proposed indices have been tested with synthetic datasets and compared with other indices in the literature. The conclusions of this work indicate that these indices offer very promising results in comparison with their competitors. On the other hand, a new external clustering validation index based on the chi-squared statistical test has been designed. This index enables the quality of the clustering to be measured by basing the result on how the clusters have been distributed with respect to a given label in the distribution. The results of this index show a significant improvement compared to other external indices in the literature when used with datasets of different dimensions and characteristics. In addition, these proposed indices have been applied in three projects with real data whose corresponding publications are included in this doctoral thesis. For the first project, a methodology has been developed to analyse the electrical consumption of buildings in a smart city. For this study, an optimal clustering analysis has been carried out by applying the aforementioned internal indices. In the second project, both internal and external indices have been applied in order to perform a comparative analysis of the Spanish labour market in two different economic periods. This analysis was carried out using data from the Ministry of Labour, Migration, and Social Security, and the results could be taken into account to help decision-making for the improvement of employment policies. In the third project, data from the customers of an electric company has been employed to characterise the different types of existing consumers. In this study, consumption patterns have been analysed so that electricity companies can offer new rates to consumers. Conclusions show that consumers could adapt their usage to these rates and hence the generation of energy could be optimised by eliminating the consumption peaks that currently exist

Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de big data y flujos continuos de datos

En esta tesis se analizan en profundidad las tareas de descubrimiento de subgrupos y minería de patrones emergentes enfocadas a la resolución de problemas complejos, como big data y flujos continuos de datos, entre otros. Además, se destacan diferentes problemas abiertos en este área. En particular, para descubrimiento de subgrupos se presenta un análisis de la influencia de ruido en los datos en los principales sistemas difusos evolutivos desarrollados; un paquete software para la plataforma R con los principales algoritmos basados en sistemas difusos evolutivos; y un análisis del comportamiento de los principales enfoques a problemas multi-instancia, mediante la realización de adaptaciones de los mismos. Con respecto a la minería de patrones emergentes, se presenta una revisión de los principales enfoques desarrollados en la tarea desde el punto de vista descriptivo y tres propuestas basadas en sistemas difusos evolutivos: una enfocada a mejorar la calidad del conocimiento extraído desde el punto de vista descriptivo; otra enfocada a realizar esta extracción en el ámbito big data y un último método enfocado al contexto de la minería de flujo de datos. Los resultados obtenidos muestran que los métodos propuestos permiten obtener conocimiento de calidad capaz de ayudar a la toma de decisiones por parte de los expertos en problemas complejos.In this thesis the subgroup discovery and emerging pattern mining tasks for the resolution of complex problems, such as big data and data stream mining, among others, are analysed in depth. Different methods and tools are proposed in order to extract descriptive knowledge from these types of environments. In addition, different open problems in this area are highlighted. In particular, for subgroup discovery an analysis of the influence of data noise on the main evolutionary fuzzy systems developed is presented; a software package for the R platform with the main algorithms based on evolutionary fuzzy systems is proposed; and an initial analysis of the behaviour of the main approaches adapted to multi-instance problems, a complex problem on the rise, is shown. With respect to emerging pattern mining, a review of the main approaches developed in the task from a descriptive point of view is presented, together with three developments based on evolutionary fuzzy systems: one focused on improving the quality of the extracted knowledge from a descriptive point of view; another focused on performing this extraction in the big data domain and a last method focused on the context of data stream mining.Tesis Univ. Jaén. Departamento de Informática. Leída el 28 de abril de 2020

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Toward a Collaborative Platform for Hybrid Designs Sharing a Common Cohort

This doctoral thesis binds together four included papers in a thematical whole and is simultaneously an independent work proposing a platform facilitating epidemiological research. Population-based prospective cohort studies typically recruit a relatively large group of participants representative of a studied population and follow them over years or decades. This group of participants is called a cohort. As part of the study, the participants may be asked to answer extensive questionnaires, undergo medical examinations, donate blood samples, and participate in several rounds of follow-ups. The collected data can also include information from other sources, such as health registers. In prospective cohort studies, the participants initially do not have the investigated diagnoses, but statistically, a certain percentage will be diagnosed with a disease yearly. The studies enable the researchers to investigate how those who got a disease differ from those who did not. Often, many new studies can be nested within a cohort study. Data for a subgroup of the cohort is then selected and analyzed. A new study combined with an existing cohort is said to have a hybrid design. When a research group uses the same cohort as a basis for multiple new studies, these studies often have similarities regarding the workflow for designing the study and analysis. The thesis shows the potential for developing a platform encouraging the reuse of work from previous studies and systematizing the study design workflows to enhance time efficiency and reduce the risk of errors. However, the study data are subject to strict acts and regulations pertaining to privacy and research ethics. Therefore, the data must be stored and accessed within a secured IT environment where researchers log in to conduct analyses, with minimal possibilities to install analytics software not already provided by default. Further, transferring the data from the secured IT environment to a local computer or a public cloud is prohibited. Nevertheless, researchers can usually upload and run script files, e.g., written in R and run in R-studio. A consequence is that researchers - often having limited software engineering skills - may rely mainly on self-written code for their analyses, possibly unsystematically developed with a high risk of errors and reinventing solutions solved in preceding studies within the group. The thesis makes a case for a platform providing a collaboration software as a service (SaaS) addressing the challenges of the described research context and proposes its architecture and design. Its main characteristic, and contribution, is the separation of concerns between the SaaS, which operates independently of the data, and a secured IT environment where data can be accessed and analyzed. The platform lets the researchers define the data analysis for the study using the cloud-based software, which is then automatically transformed into an executable version represented as source code in a scripting language already supported by the secure environment where the data resides. The author has not found systems solving the same problem similarly. However, the work is informed by cloud computing, workflow management systems, data analysis pipelines, low-code, no-code, and model-driven development

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud

Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing