Predictive Analytics and Decision Support for Heart Failure patients

Thesis (Master's)--University of Washington, 2016-08

Incorporación de un sistema de almacenamiento de experiencias a un ecosistema educativo para permitir el uso de técnicas de análisis de datos (Educational Analytics)

La Universidad EAFIT, en los últimos años, por medio de la realización de varias investigaciones, ha estado desarrollado una propuesta con la cual se busca definir los componentes tecnológicos que deben componer un ecosistema de aplicaciones educativas, con el fin de apalancar la adopción del modelo de ubicuidad en las instituciones de educación superior -- Por medio del grupo de investigación de desarrollo e innovación en Tecnologías de la Información y las Comunicaciones (GIDITIC) ha realizado la selección de los primeros componentes del ecosistema en trabajos de tesis de grado de anteriores investigaciones[1, 2] -- Adicionalmente, algunos trabajos realizados por el gobierno local de la Alcaldía de Medellín en su proyecto de Medellín Ciudad Inteligente[3], también realizó una selección de algunos componentes que son necesarios para la implementación del portal -- Ambas iniciativas coinciden en la inclusión de un componente de registro de actividades, conocido como \Sistema de almacenamiento de experiencias" (LRS) -- Dados estos antecedentes, se pretende realizar una implementación de un LRS que cumpla con los objetivos buscados en el proyecto de la Universidad, siguiendo estándares que permitan asegurar la interoperabilidad con los otros componentes del ecosistema de aplicaciones educativas

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.

Custom Analytics with Google Tag Manager: Assessing Usage Statistics on the MD-SOAR Platform

As usage metrics continue to attain an increasingly central role in library system assessment and analysis, librarians tasked with system selection, implementation, and support are driven to identify metric approaches that simultaneously require less technical complexity and greater levels of data granularity. Such approaches allow systems librarians to present evidence-based claims of platform usage behaviors while reducing the resources necessary to collect such information, thereby representing a novel approach to real-time user analysis as well as dual benefit in active and preventative cost reduction. As part of the DSpace implementation for the MD SOAR initiative, the Consortial Library Application Support (CLAS) division has begun test implementation of the Google Tag Manager analytic system in an attempt to collect custom analytical dimensions to track author- and university-specific download behaviors. Building on the work of Conrad , CLAS seeks to demonstrate that the GTM approach to custom analytics provides both granular metadata-based usage statistics in an approach that will prove extensible for additional statistical gathering in the future. This poster will discuss the methodology used to develop these custom tag approaches, the benefits of using the GTM model, and the risks and benefits associated with further implementation.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

Il passaggio dalla Business Intelligence all'Advanced Analytics: individuazione e analisi di un modello in merito all'influenza del lobbying nel sistema economico

I Big Data stanno guidando una rivoluzione globale. In tutti i settori, pubblici o privati, e le industrie quali Vendita al dettaglio, Sanità, Media e Trasporti, i Big Data stanno influenzando la vita di miliardi di persone. L’impatto dei Big Data è sostanziale, ma così discreto da passare inosservato alla maggior parte delle persone. Le applicazioni di Business Intelligence e Advanced Analytics vogliono studiare e trarre informazioni dai Big Data. Si studia il passaggio dalla prima alla seconda, mettendo in evidenza aspetti simili e differenze.

Visual analytics of medical and biological data

Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Habilitationsschrift, 2016

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

The practices of existential psychotherapists: Development and application of an observational grid

Within the major therapeutic paradigms, observational instruments have been developed to assess orientation-specific interventions or processes. However, to date, no such instrument exists to assess existential practices. Recent research indicates the key practices of existential therapists, and forms an empirical basis on which to develop an observatory grid. This paper describes the development of such a grid, and its exploratory testing with eight clients of four Portuguese existential psychotherapists. A total of 32 sessions were observed and both speaking turn and whole-session analysis showed that it was feasible to assess existential therapy using the instrument, although psychometric findings recommend further refinement of the tool. Session-rating data suggest that the chief practices applied by existential therapists were relational, followed by the use of hermeneutic interventions and reformulations. Interventions based on phenomenological and existential assumptions were observable in practice but limited in frequency. Further refinements and developments of the observational grid, together with additional research – using a range of therapists from different schools of existential therapy – are recommended.

The Role of Existential Beliefs Within the Relation of Centenarians’ Health and Well-Being

The purpose of this study was to explore the role of existential beliefs in mediating the influence of health on centenarians' well-being. A total of 80 centenarians (mean age 101.1; SD = 1.3; 81.3 % women) with no/minor cognitive impairment were included. The OARS questionnaire for diseases and functional capacity (ADL, IADL), the Satisfaction with Life Scale, and the existential beliefs subscale were used for data collection. The findings suggest that existential resources are a crucial element for mitigating the impact of health constraints in subjective well-being in this population. Appropriate models of intervention for very old age that recognize the importance of religion, spirituality, and meaning of life are to be considered.

Vihara architecture: defining the existential foothold of VIII century Buddhist monastery "Somapura Mahavihara" of ancien Bengal

Lost in history, the ruin of ‘Somapura Mahavihara’ was not recognized separated from its birthplace, i.e. nature, for more than 700 years. Yet, within its silent presence, the monument dominated the name of the region: ‘Paharpur’ (land of hillock), according to its appearance surrounds by its flat land topo¬graphy. Discovered in 1919, the single largest Buddhist Vihara (monas¬tery) of ancient Bengal came into light, pronouncing the flou¬rishing minute of Buddhist architecture, once dominant religious force of the subcontinent. The earliest historical monumental architecture of greater Asia, had long been deriving itself from the Buddhist monastic architecture as early as VI century BC. In line of history, the discovery of ‘Somapura Mahavihara’ contributed attesting the sensitivities of a highly sophisticated architectonic typology of Vihara Architecture in the land of ancient Bengal. The recovery of ‘Somapura Mahavihara’ was not only from its cradle of nature, but also from its remarkable existence imprinted in the reign of Pala dynasty (750 - 1155 AD) announcing the existential foothold of man in his nature. The existential foothold of ‘Somapura Mahavihara’ comprises the factors, responsible in shaping the anchorage of the mo¬nument since the birth of Vihara architecture, as early as 530 BC. These factors not only denote the building technology in response to its environment but also the amalgamation of be¬lief, upon which the dwellers transformed the site as a place announcing their existence on earth. This research paper aims at exploring the existential foothold of ‘Somapura Mahavihara’, in terms of its territorial, functional, structural, social, cultural, religious sym¬bolic hierarchies of human achievement while clarifying the architectonic typology that shaped ‘Somapura Mahavihara’ through evolution process of ‘Vihara Architecture’. This understanding intends to combine the archaeological knowledge with comparative architectural analysis of contem¬porary Viharas of ancient Bengal, to define the singularity of ‘Somapura Mahavihara’. In consequence, the glorious past of ‘Somapura Mahavihara’ is intended to portray through iden¬tifying the relation of religious and functional rationalism with the connotation of art, architecture and belief moulded within natural forces, as one complete entity; RESUMO: Vihara Arquitetura: Definindo a posição existencial do século VIII Budista mosteiro “Somapura Mahavihara” de Bengala antiga. Perdidas na História, as ruínas de ‘Somapura Mahavihara’ foram confundidas com uma montanha durante mais de setecentos anos. Contudo, no seu silêncio presente, o monumento marcou a toponímia da região; ‘Paharpur’ significa ‘a terra do outeiro’, evidenciando a singularidade deste monumento numa região dominada por uma extensa planície. Em 1919, foi descoberto o maior mosteiro budista da antiga região de Bengal, demonstrando a prosperidade da arquitectura budista. Tem¬poralmente, a descoberta de ‘Somapura Mahavihara’ contribuiu para atestar a evolução e a sofisticação da tipologia arquitectónica denominada ‘Arquitectura Vihara’, existente na antiga região de Bengal. A noção de pegada existencial de ‘Somapura Mahavihara’ compreende os factores responsáveis por moldar a ancoragem do monumento ao lugar em que se insere desde o início da arquitectura Vihara, que remonta a 530 a.C. Estes factores evidenciam a tecnologia construtiva empregue para responder ao ambiente envolvente mas também a evolução da religião, factores estes que os monges construtores consideraram ao transformar o lugar e anunciar a sua existência na Terra. Esta investigação tem por objectivo explorar a noção de pegada existencial de ‘Somapura Mahavihara’, nas suas dimensões territoriais, funcionais, estruturais, sociais, culturais e nas hierarquias simbólicas das realizações humanas para clarificar a tipologia arquitectónica que deu forma a ‘Somapura Mahavihara’ durante a evolução da arquitectura Vihara. Este entendimento pretende combinar/cruzar o conhecimen¬to arqueológico com estudos arquitectónicos comparativos de Viharas na antiga região de Bengal, com o objectivo de definir a singularidade de ‘Somapura Mahavihara’. Neste estudo estudar-se-á também o confronto entre a dimensão religiosa e a artística (divino vs. humano), integrados na arquitectura de ‘Somapura Mahavihara’ em perfeita harmonia.

Analytics and Methods for Logistic System Design and Operations Management

Analytics is the technology working with the manipulation of data to produce information able to change the world we live every day. Analytics have been largely used within the last decade to cluster people’s behaviour to predict their preferences of items to buy, music to listen, movies to watch and even electoral preference. The most advanced companies succeded in controlling people’s behaviour using analytics. Despite the evidence of the super-power of analytics, they are rarely applied to the big data collected within supply chain systems (i.e. distribution network, storage systems and production plants). This PhD thesis explores the fourth research paradigm (i.e. the generation of knowledge from data) applied to supply chain system design and operations management. An ontology defining the entities and the metrics of supply chain systems is used to design data structures for data collection in supply chain systems. The consistency of this data is provided by mathematical demonstrations inspired by the factory physics theory. The availability, quantity and quality of the data within these data structures define different decision patterns. Ten decision patterns are identified, and validated on-field, to address ten different class of design and control problems in the field of supply chain systems research.

Learning analytics from distance learning

Presentation at M25 Learning Technology Group, FutureLearn, 15 November 2017

Big data analytics and modeling for Human Mobility

The fast development of Information Communication Technologies (ICT) offers new opportunities to realize future smart cities. To understand, manage and forecast the city's behavior, it is necessary the analysis of different kinds of data from the most varied dataset acquisition systems. The aim of this research activity in the framework of Data Science and Complex Systems Physics is to provide stakeholders with new knowledge tools to improve the sustainability of mobility demand in future cities. Under this perspective, the governance of mobility demand generated by large tourist flows is becoming a vital issue for the quality of life in Italian cities' historical centers, which will worsen in the next future due to the continuous globalization process. Another critical theme is sustainable mobility, which aims to reduce private transportation means in the cities and improve multimodal mobility. We analyze the statistical properties of urban mobility of Venice, Rimini, and Bologna by using different datasets provided by companies and local authorities. We develop algorithms and tools for cartography extraction, trips reconstruction, multimodality classification, and mobility simulation. We show the existence of characteristic mobility paths and statistical properties depending on transport means and user's kinds. Finally, we use our results to model and simulate the overall behavior of the cars moving in the Emilia Romagna Region and the pedestrians moving in Venice with software able to replicate in silico the demand for mobility and its dynamic.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.