A service-oriented architecture for text analytics enabled business applications

Experiences showed that developing business applications that base on text analysis normally requires a lot of time and expertise in the field of computer linguistics. Several approaches of integrating text analysis systems with business applications have been proposed, but so far there has been no coordinated approach which would enable building scalable and flexible applications of text analysis in enterprise scenarios. In this paper, a service-oriented architecture for text processing applications in the business domain is introduced. It comprises various groups of processing components and knowledge resources. The architecture, created as a result of our experiences with building natural language processing applications in business scenarios, allows for the reuse of text analysis and other components, and facilitates the development of business applications. We verify our approach by showing how the proposed architecture can be applied to create a text analytics enabled business application that addresses a concrete business scenario. © 2010 IEEE.

Use of Freely-Available Weebly in Creating Quick and Easy Web Pages: poster presentation

Weebly is a freely-available software for creating Web pages without having to know HTML. It is easy to use, with its drag and drop editor, and offers the ability to add documents, Web links, videos, slideshows, audio, forms, polls, etc. It is hosted by Weebly and has no limits on storage space. Many templates are available for Web page design. One can publish and update almost immediately. Combined with usage of the freely-available Google Analytics, for example, it is possible to gather usage statistics. The site can be password protected, if need be. Weebly for Education is a special version for teachers and schools.

iLectures - Exploring the interactive lecture using web-based broadcasting technology

The continued use of traditional lecturing across Higher Education as the main teaching and learning approach in many disciplines must be challenged. An increasing number of studies suggest that this approach, compared to more active learning methods, is the least effective. In counterargument, the use of traditional lectures are often justified as necessary given a large student population. By analysing the implementation of a web based broadcasting approach which replaced the traditional lecture within a programming-based module, and thereby removed the student population rationale, it was hoped that the student learning experience would become more active and ultimately enhance learning on the module. The implemented model replaces the traditional approach of students attending an on-campus lecture theatre with a web-based live broadcast approach that focuses on students being active learners rather than passive recipients. Students ‘attend’ by viewing a live broadcast of the lecturer, presented as a talking head, and the lecturer’s desktop, via a web browser. Video and audio communication is primarily from tutor to students, with text-based comments used to provide communication from students to tutor. This approach promotes active learning by allowing student to perform activities on their own computer rather than the passive viewing and listening common encountered in large lecture classes. By analysing this approach over two years (n = 234 students) results indicate that 89.6% of students rated the approach as offering a highly positive learning experience. Comparing student performance across three academic years also indicates a positive change. A small data analytic analysis was conducted into student participation levels and suggests that the student cohort's willingness to engage with the broadcast lectures material is high.

Using Google Analytics to evaluate an email information literacy program for medical and dental students

Objectives: An email information literacy program has been effective for over a decade at Université de Montréal’s Health Library. Students periodically receive messages highlighting the content of guides on the library’s website. We wish to evaluate, using Google Analytics, the effects of the program on specific webpage statistics. Using the data collected, we may pinpoint popular guides as well as others that need improvement. Methods: In the program, first and second-year medical (MD) or dental (DMD) students receive eight bi-monthly email messages. The DMD mailing list also includes graduate students and professors. Enrollment to the program is optional for MDs, but mandatory for DMDs. Google Analytics (GA) profiles have been configured for the libraries websites to collect visitor statistics since June 2009. The GA Links Builder was used to design unique links specifically associated with the originating emails. This approach allowed us to gather information on guide usage, such as the visitor’s program of study, duration of page viewing, number of pages viewed per visit, as well as browsing data. We also followed the evolution of clicks on GA unique links over time, as we believed that users may keep the library's emails and refer to them to access specific information. Results: The proportion of students who actually clicked the email links was, on average, less than 5%. MD and DMD students behaved differently regarding guide views, number of pages visited and length of time on the site. The CINAHL guide was the most visited for DMD students whereas MD students consulted the Pharmaceutical information guide most often. We noted that some students visited referred guides several weeks after receiving messages, thus keeping them for future reference; browsing to additional pages on the library website was also frequent. Conclusion: The mitigated success of the program prompted us to directly survey students on the format, frequency and usefulness of messages. The information gathered from GA links as well as from the survey will allow us to redesign our web content and modify our email information literacy program so that messages are more attractive, timely and useful for students.

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

Análisis de software de analítica web para medir uso de recursos y servicios electrónicos de bibliotecas universitarias

Las bibliotecas universitarias recopilan, de manera rutinaria estadísticas sobre el uso de sus colecciones impresas y de la actividad in situ. Paralelamente y de manera sostenida, han ido incorporando recursos y servicios electrónicos, lo que ha motivado la elaboración de normas internacionales que definen indicadores que permiten medir su uso, no obstante contar con un software estándar es aún un asunto pendiente. Por otro lado, para medir la actividad de un sitio web existen varios programas gratuitos y de código abierto. Este trabajo tiene como objetivo determinar si los softwares de analítica web gratuitos para sitios web AWStats, Google Analytics y Piwik, pueden utilizarse para evaluar el uso de recursos y servicios electrónicos, conforme a los indicadores propuestos por las normas ANSI/NISO Z39.7-2013, ISO 2789:2003, ISO 20983:2003, BS ISO 11620:2008, EMIS, Counter e ICOLC. Para tales efectos, fueron utilizados para realizar el análisis de esta investigación sitio web y el catálogo en línea de la Biblioteca Florentino Ameghino, Biblioteca Central de la Facultad de Ciencias Naturales y Museo de la Universidad Nacional de la Plata, Argentina. Los resultados reflejan las características de los indicadores, el software y el caso de estudio. Estas características son abordadas en las conclusiones con el fin de darle contexto y perspectiva a la respuesta de la pregunta de si es viable medir el uso de recursos y servicios electrónicos de una biblioteca universitaria por medio de programas estadísticos para sitios web

Análisis de software de analítica web para medir uso de recursos y servicios electrónicos de bibliotecas universitarias

Las bibliotecas universitarias recopilan, de manera rutinaria estadísticas sobre el uso de sus colecciones impresas y de la actividad in situ. Paralelamente y de manera sostenida, han ido incorporando recursos y servicios electrónicos, lo que ha motivado la elaboración de normas internacionales que definen indicadores que permiten medir su uso, no obstante contar con un software estándar es aún un asunto pendiente. Por otro lado, para medir la actividad de un sitio web existen varios programas gratuitos y de código abierto. Este trabajo tiene como objetivo determinar si los softwares de analítica web gratuitos para sitios web AWStats, Google Analytics y Piwik, pueden utilizarse para evaluar el uso de recursos y servicios electrónicos, conforme a los indicadores propuestos por las normas ANSI/NISO Z39.7-2013, ISO 2789:2003, ISO 20983:2003, BS ISO 11620:2008, EMIS, Counter e ICOLC. Para tales efectos, fueron utilizados para realizar el análisis de esta investigación sitio web y el catálogo en línea de la Biblioteca Florentino Ameghino, Biblioteca Central de la Facultad de Ciencias Naturales y Museo de la Universidad Nacional de la Plata, Argentina. Los resultados reflejan las características de los indicadores, el software y el caso de estudio. Estas características son abordadas en las conclusiones con el fin de darle contexto y perspectiva a la respuesta de la pregunta de si es viable medir el uso de recursos y servicios electrónicos de una biblioteca universitaria por medio de programas estadísticos para sitios web

Eyetracking: analisi della user interface di una pagina web

Negli ultimi vent'anni con lo sviluppo di Internet, il modo di comunicare tra le persone �è totalmente cambiato. Grazie a Internet si sono ridotte le distanze e soprattutto tramite i siti web le aziende hanno una propria vetrina sul mondo sempre accessibile. Tutto ci�ò ha portato a nuovi comportamenti da parte dei consumatori che divengono sempre pi�u esigenti nella vastità di informazioni presenti sul Web. Perciò è necessario che le web companies riescano a produrre website efficienti e usabili per favorire l'interazione con l'utente. Inoltre il web ha avuto una rapida espansione per quanto concerne le metodologie di sviluppo e analisi del comportamento del consumatore. Si cercano sempre nuovi spunti per poter acquisire quello che �è il percorso di un utente affinché porti a termine una determinata azione nel proprio dominio. Per questo motivo, oltre agli strumenti gi�à consolidati come il riempimento di questionari o il tracking per mezzo di piattaforme come Google Analytics, si �è pensato di andare oltre e cercare di analizzare ancora pi�u a fondo il "consumAttore". Grazie ad un eye-tracker �è possibile riconoscere quelli che sono i modelli cognitivi che riguardano il percorso di ricerca, valutazione e acquisto di un prodotto o una call to action, e come i contenuti di una web application influenzano l'attenzione e la user experience. Pertanto l'obiettivo che si pone questo studio �è quello di poter misurare l'engagement della navigazione utente di una web application e, nel caso fosse necessario, ottimizzare i contenuti al suo interno. Per il rilevamento delle informazioni necessarie durante l'esperimento, mi sono servito di uno strumento a supporto delle decisioni, ovvero un eye-tracker e della successiva somministrazione di questionari.

Classificazione e selezione di tecniche di visualizzazione per Big Data Analytics

La tesi presenta uno studio della libreria grafica per web D3, sviluppata in javascript, e ne presenta una catalogazione dei grafici implementati e reperibili sul web. Lo scopo è quello di valutare la libreria e studiarne i pregi e difetti per capire se sia opportuno utilizzarla nell'ambito di un progetto Europeo. Per fare questo vengono studiati i metodi di classificazione dei grafici presenti in letteratura e viene esposto e descritto lo stato dell'arte del data visualization. Viene poi descritto il metodo di classificazione proposto dal team di progettazione e catalogata la galleria di grafici presente sul sito della libreria D3. Infine viene presentato e studiato in maniera formale un algoritmo per selezionare un grafico in base alle esigenze dell'utente.

Análisis de software de analítica web para medir uso de recursos y servicios electrónicos de bibliotecas universitarias

Las bibliotecas universitarias recopilan, de manera rutinaria estadísticas sobre el uso de sus colecciones impresas y de la actividad in situ. Paralelamente y de manera sostenida, han ido incorporando recursos y servicios electrónicos, lo que ha motivado la elaboración de normas internacionales que definen indicadores que permiten medir su uso, no obstante contar con un software estándar es aún un asunto pendiente. Por otro lado, para medir la actividad de un sitio web existen varios programas gratuitos y de código abierto. Este trabajo tiene como objetivo determinar si los softwares de analítica web gratuitos para sitios web AWStats, Google Analytics y Piwik, pueden utilizarse para evaluar el uso de recursos y servicios electrónicos, conforme a los indicadores propuestos por las normas ANSI/NISO Z39.7-2013, ISO 2789:2003, ISO 20983:2003, BS ISO 11620:2008, EMIS, Counter e ICOLC. Para tales efectos, fueron utilizados para realizar el análisis de esta investigación sitio web y el catálogo en línea de la Biblioteca Florentino Ameghino, Biblioteca Central de la Facultad de Ciencias Naturales y Museo de la Universidad Nacional de la Plata, Argentina. Los resultados reflejan las características de los indicadores, el software y el caso de estudio. Estas características son abordadas en las conclusiones con el fin de darle contexto y perspectiva a la respuesta de la pregunta de si es viable medir el uso de recursos y servicios electrónicos de una biblioteca universitaria por medio de programas estadísticos para sitios web

Predictive Analytics and Decision Support for Heart Failure patients

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

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.

Un repositorio RDF para la Integración de Flujos de Datos de Analítica Web y Comercio Electrónico

La Analítica Web supone hoy en día una tarea ineludible para las empresas de comercio electrónico, ya que les permite analizar el comportamiento de sus clientes. El proyecto Europeo SME-Ecompass tiene como objetivo desarrollar herramientas avanzadas de analítica web accesibles para las PYMES. Con esta motivación, proponemos un servicio de integración de datos basado en ontologías para recopilar, integrar y almacenar información de traza web procedente de distintas fuentes.Estas se consolidan en un repositorio RDF diseñado para proporcionar semántica común a los datos de análisis y dar servicio homogéneo a algoritmos de Minería de Datos. El servicio propuesto se ha validado mediante traza digital real (Google Analitics y Piwik) de 15 tiendas virtuales de diferentes sectores y países europeos (UK, España, Grecia y Alemania) durante varios meses de actividad.