Analytics for customer engagement

In this article, we discuss the state of the art of models for customer engagement and the problems that are inherent to calibrating and implementing these models. The authors first provide an overview of the data available for customer analytics and discuss recent developments. Next, the authors discuss the models used for studying customer engagement, where they distinguish the following stages: customer acquisition, customer development, and customer retention. Finally, they discuss several organizational issues of analytics for customer engagement, which constitute barriers for introducing analytics for customer engagement.

Google - Security Testing Tool

Using Google as a security testing tool, basic and advanced search techniques using advanced google search operators. Examples of obtaining control over security cameras, VoIP systems, web servers and collecting valuable information as: Credit card details, cvv codes – only using Google.

Use of Google as a tool to aid diagnosis by optometry students

Background: Previous work has shown that medical problems can be diagnosed by practitioners using Google. The aim of this study was to determine whether optometry students would benefit from using Google when diagnosing eye diseases. Methods: Participants were given symptoms and signs and instructed to list three key words and use them to search Aston University e-Library and Google UK. Results: Aston University e-Library only search resulted in correct diagnosis in 16 of 60 simulated cases. Aston e-Library plus Google search resulted in correct diagnosis in 31 of 60 simulated cases. Conclusion: Google is a useful aid to help optometry students improve their success rate when diagnosing eye conditions.

Topographic visual analytics of multibeam dynamic SONAR data

This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.

Benchmarking of distributed computing engines spark and GraphLab for big data analytics

In this paper we evaluate and compare two representativeand popular distributed processing engines for large scalebig data analytics, Spark and graph based engine GraphLab. Wedesign a benchmark suite including representative algorithmsand datasets to compare the performances of the computingengines, from performance aspects of running time, memory andCPU usage, network and I/O overhead. The benchmark suite istested on both local computer cluster and virtual machines oncloud. By varying the number of computers and memory weexamine the scalability of the computing engines with increasingcomputing resources (such as CPU and memory). We also runcross-evaluation of generic and graph based analytic algorithmsover graph processing and generic platforms to identify thepotential performance degradation if only one processing engineis available. It is observed that both computing engines showgood scalability with increase of computing resources. WhileGraphLab largely outperforms Spark for graph algorithms, ithas close running time performance as Spark for non-graphalgorithms. Additionally the running time with Spark for graphalgorithms over cloud virtual machines is observed to increaseby almost 100% compared to over local computer clusters.

What Google Maps can do for Biomedical Data Dissemination: Examples and a Design Study

Background: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data. Results: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers. Conclusions: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations.

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Escrita colaborativa com google docs: flash fiction, noticing e aprendizagem de inglês como L2

Google Docs (GD) is an online word processor with which multiple authors can work on the same document, in a synchronous or asynchronous manner, which can help develop the ability of writing in English (WEISSHEIMER; SOARES, 2012). As they write collaboratively, learners find more opportunities to notice the gaps in their written production, since they are exposed to more input from the fellow co-authors (WEISSHEIMER; BERGSLEITHNER; LEANDRO, 2012) and prioritize the process of text (re)construction instead of the concern with the final product, i.e., the final version of the text (LEANDRO; WEISSHEIMER; COOPER, 2013). Moreover, when it comes to second language (L2) learning, producing language enables the consolidation of existing knowledge as well as the internalization of new knowledge (SWAIN, 1985; 1993). Taking this into consideration, this mixed-method (DÖRNYEI, 2007) quasi-experimental (NUNAN, 1999) study aims at investigating the impact of collaborative writing through GD on the development of the writing skill in English and on the noticing of syntactic structures (SCHMIDT, 1990). Thirtyfour university students of English integrated the cohort of the study: twenty-five were assigned to the experimental group and nine were assigned to the control group. All learners went through a pre-test and a post-test so that we could measure their noticing of syntactic structures. Learners in the experimental group were exposed to a blended learning experience, in which they took reading and writing classes at the university and collaboratively wrote three pieces of flash fiction (a complete story told in a hundred words), outside the classroom, online through GD, during eleven weeks. Learners in the control group took reading and writing classes at the university but did not practice collaborative writing. The first and last stories produced by the learners in the experimental group were analysed in terms of grammatical accuracy, operationalized as the number of grammar errors per hundred words (SOUSA, 2014), and lexical density, which refers to the relationship between the number of words produced with lexical properties and the number of words produced with grammatical properties (WEISSHEIMER, 2007; MEHNERT, 1998). Additionally, learners in the experimental group answered an online questionnaire on the blended learning experience they were exposed to. The quantitative results showed that the collaborative task led to the production of more lexically dense texts over the 11 weeks. The noticing and grammatical accuracy results were different from what we expected; however, they provide us with insights on measurement issues, in the case of noticing, and on the participants‟ positive attitude towards collaborative writing with flash fiction. The qualitative results also shed light on the usefulness of computer-mediated collaborative writing in L2 learning.

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.

Progettazione e Sviluppo di un Sistema di Proactive Alerting all'interno di una piattaforma di Big Data Analytics

Il lavoro presentato in questo elaborato tratterà lo sviluppo di un sistema di alerting che consenta di monitorare proattivamente una o più sorgenti dati aziendali, segnalando le eventuali condizioni di irregolarità rilevate; questo verrà incluso all'interno di sistemi già esistenti dedicati all'analisi dei dati e alla pianificazione, ovvero i cosiddetti Decision Support Systems. Un sistema di supporto alle decisioni è in grado di fornire chiare informazioni per tutta la gestione dell'impresa, misurandone le performance e fornendo proiezioni sugli andamenti futuri. Questi sistemi vengono catalogati all'interno del più ampio ambito della Business Intelligence, che sottintende l'insieme di metodologie in grado di trasformare i dati di business in informazioni utili al processo decisionale. L'intero lavoro di tesi è stato svolto durante un periodo di tirocinio svolto presso Iconsulting S.p.A., IT System Integrator bolognese specializzato principalmente nello sviluppo di progetti di Business Intelligence, Enterprise Data Warehouse e Corporate Performance Management. Il software che verrà illustrato in questo elaborato è stato realizzato per essere collocato all'interno di un contesto più ampio, per rispondere ai requisiti di un cliente multinazionale leader nel settore della telefonia mobile e fissa.

Gaming learning analytics for serious games

Los juegos serios (o videojuegos educativos), se consideran una herramienta importante para la educación en el futuro. Por ello, se está invirtiendo mucho esfuerzo en el análisis de su corrección e idoneidad para alcanzar los objetivos educativos pretendidos. El campo de análisis de aprendizaje con juegos pretende proporcionar herramientas que verifiquen estas características mejorando la calidad y efectividad de los juegos serios. Para ello, se necesitan normalmente tres etapas: 1), monitorizar los datos de la interacción del jugador con el juego; 2), analizar esos datos recolectados; y 3), visualizar los resultados. En este contexto, hay algunos asuntos importantes a considerar: nivel de conocimiento del juego, receptor de las visualizaciones finales o cantidad y complejidad de los datos. Estas ideas se ponen en práctica con dos ejemplos de juegos serios centrándonos en las dos últimas etapas del proceso. Se realizan varios análisis y visualizaciones con ellos considerando los diferentes aspectos antes mencionados. Entre las conclusiones que se pueden extraer, destaca que, a pesar de haber algunos aspectos aún por mejorar, el análisis de aprendizaje con juegos es una herramienta esencial para muchos usuarios con una amplia variedad de intereses en juego serios.

Using knowledge management to give context to analytics and big data and reduce strategic risk

At the moment, the phrases “big data” and “analytics” are often being used as if they were magic incantations that will solve all an organization’s problems at a stroke. The reality is that data on its own, even with the application of analytics, will not solve any problems. The resources that analytics and big data can consume represent a significant strategic risk if applied ineffectively. Any analysis of data needs to be guided, and to lead to action. So while analytics may lead to knowledge and intelligence (in the military sense of that term), it also needs the input of knowledge and intelligence (in the human sense of that term). And somebody then has to do something new or different as a result of the new insights, or it won’t have been done to any purpose. Using an analytics example concerning accounts payable in the public sector in Canada, this paper reviews thinking from the domains of analytics, risk management and knowledge management, to show some of the pitfalls, and to present a holistic picture of how knowledge management might help tackle the challenges of big data and analytics.

Cumulon: Simplified Matrix-Based Data Analytics in the Cloud

Cumulon is a system aimed at simplifying the development and deployment of statistical analysis of big data in public clouds. Cumulon allows users to program in their familiar language of matrices and linear algebra, without worrying about how to map data and computation to specific hardware and cloud software platforms. Given user-specified requirements in terms of time, monetary cost, and risk tolerance, Cumulon automatically makes intelligent decisions on implementation alternatives, execution parameters, as well as hardware provisioning and configuration settings -- such as what type of machines and how many of them to acquire. Cumulon also supports clouds with auction-based markets: it effectively utilizes computing resources whose availability varies according to market conditions, and suggests best bidding strategies for them. Cumulon explores two alternative approaches toward supporting such markets, with different trade-offs between system and optimization complexity. Experimental study is conducted to show the efficiency of Cumulon's execution engine, as well as the optimizer's effectiveness in finding the optimal plan in the vast plan space.

AGOCS – Accurate Google Cloud Simulator Framework

This paper presents the Accurate Google Cloud Simulator (AGOCS) – a novel high-fidelity Cloud workload simulator based on parsing real workload traces, which can be conveniently used on a desktop machine for day-to-day research. Our simulation is based on real-world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise and detailed parameters of the executed jobs, tasks and nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution and an easy-to-extend design concept. The paper presents the detailed structural framework for AGOCS and discusses our main design decisions, whilst also suggesting alternative and possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository.