Gamification Analytics: Support for Monitoring and Adapting Gamification Designs

Inspired by the engaging effects in video games, gamification aims at motivating people to show desired behaviors in a variety of contexts. During the last years, gamification influenced the design of many software applications in the consumer as well as enterprise domain. In some cases, even whole businesses, such as Foursquare, owe their success to well-designed gamification mechanisms in their product. Gamification also attracted the interest of academics from fields, such as human-computer interaction, marketing, psychology, and software engineering. Scientific contributions comprise psychological theories and models to better understand the mechanisms behind successful gamification, case studies that measure the psychological and behavioral outcomes of gamification, methodologies for gamification projects, and technical concepts for platforms that support implementing gamification in an efficient manner. Given a new project, gamification experts can leverage the existing body of knowledge to reuse previous, or derive new gamification ideas. However, there is no one size fits all approach for creating engaging gamification designs. Gamification success always depends on a wide variety of factors defined by the characteristics of the audience, the gamified application, and the chosen gamification design. In contrast to researchers, gamification experts in the industry rarely have the necessary skills and resources to assess the success of their gamification design systematically. Therefore, it is essential to provide them with suitable support mechanisms, which help to assess and improve gamification designs continuously. Providing suitable and efficient gamification analytics support is the ultimate goal of this thesis. This work presents a study with gamification experts that identifies relevant requirements in the context of gamification analytics. Given the identified requirements and earlier work in the analytics domain, this thesis then derives a set of gamification analytics-related activities and uses them to extend an existing process model for gamification projects. The resulting model can be used by experts to plan and execute their gamification projects with analytics in mind. Next, this work identifies existing tools and assesses them with regards to their applicability in gamification projects. The results can help experts to make objective technology decisions. However, they also show that most tools have significant gaps towards the identified user requirements. Consequently, a technical concept for a suitable realization of gamification analytics is derived. It describes a loosely coupled analytics service that helps gamification experts to seamlessly collect and analyze gamification-related data while minimizing dependencies to IT experts. The concept is evaluated successfully via the implementation of a prototype and application in two real-world gamification projects. The results show that the presented gamification analytics concept is technically feasible, applicable to actual projects, and also valuable for the systematic monitoring of gamification success

9Solutions product quality system

Abstract. Quality management (QM) is an important managerial tool in production and service environments. It covers the social and technical factors affecting quality of products and services within an organization. Global competition and increasing customer demands emphasize the importance of QM in different organizations. If applied correctly, QM can be a success factor for a company, by increasing customer satisfaction and profitability of the company. The thesis is a constructive research in nature and performed in a case company. The objective of the study is to examine the current state and the biggest challenges regarding QM in the case company and to suggest improvement proposals based on theory and empirical findings. The study addresses QM and its utilization in the case company in the form of a quality management system (QMS). The literature review familiarizes with the concept of quality the QM principles, and its involvement in company’s functions, such as product development (PD). The empirical part of the research examines the current state of QM at the case company with the use of theme interviews. Also, three benchmarking interviews contribute to empirical study, highlighting the best QM practices from technology companies of similar magnitude. The empirical part of the study demonstrates that in the case company quality is managed with several procedures, but systematic and documented system, as well as clear, strategy-based quality policies and objectives, are missing. The lack of systematic QM complicates detecting problems in PD and other organizational functions, leading to both direct and indirect quality costs. Thus, the existing literature’s perception of reactive QM applies to the case company for the most part. The study aims to solve QM related challenges in the company by utilizing the key points of existing literature and benchmarking observations. Existing literature emphasizes the concepts of quality planning and continuous improvement as the most important factors for an organization to move towards preventive QM, including planning for the quality management system. The QMSs of the benchmarking companies differ, but their unifying factors were observed to be process management, clear documentation of the system, clear objectives, and systematic QM in PD processes. Evaluating the theory and empirical findings demonstrates, that QM at the case company can also be developed with the implementation of a process-based QMS. The proposed improvement model covers those basic QM methods, that the case company should assimilate to develop a QMS. The development proposals include quality planning, measuring organizational performance and process management, which together create a body for the QMS. Also, recommendations for QMS documentation procedures and audits are presented. Together, the improvement proposals offer the case company a concrete model for initiating quality work and developing the quality of products and services.Tiivistelmä. Laadunhallinta on tärkeä johtamisen apuväline sekä tuotanto- että palveluympäristöissä. Se kattaa ne organisaation sosiaaliset ja tekniset tekijät, jotka vaikuttavat tuotteiden ja palveluiden laatuun. Nykyinen globaali kilpailu ja asiakkaiden kasvavat laatuvaatimukset korostavat laadunhallinnan tarvetta erilaisissa organisaatioissa. Oikein sovellettuna laadunhallinta voi olla menestystekijä yritykselle, parantaen asiakastyytyväisyyttä ja yrityksen kannattavuutta. Tämä diplomityö on luonteeltaan konstruktiivinen tutkimus, joka suoritettiin kohdeyrityksessä. Työn tavoitteena on selvittää kohdeyrityksen laadunhallinnan nykytila ja suurimmat haasteet, sekä esittää kehitysehdotuksia kirjallisuuden ja empiiristen havaintojen pohjalta. Tutkimus käsittelee laadunhallintaa ja sen hyödyntämistä kohdeyrityksessä laatujärjestelmän muodossa. Kirjallisuuskatsaus perehtyy laadun käsitteeseen, laadunhallinnan periaatteisiin sekä sen merkitykseen yrityksen funktioille, kuten tuotekehitykselle. Empiirinen osa tutkimuksesta tutkii laadunhallinnan nykytilaa kohdeyrityksessä teemahaastattelujen avulla. Myös kolme benchmarking-haastattelua ovat osana empiiristä tutkimusta, tuoden esiin parhaita laadunhallinnallisia käytäntöjä vastaavan kokoluokan teknologiayrityksistä. Tutkimuksen empiirinen osa osoittaa, että laatua hallitaan kohdeyrityksessä eri toimintamallien avulla, mutta järjestelmällinen ja dokumentoitu laatujärjestelmä sekä selkeät, yrityksen strategiaan perustuvat laatulinjaukset ja -tavoitteet puuttuvat. Systemaattisen laadunhallinnan puute vaikeuttaa ongelmien havaitsemista niin tuotekehityksessä kuin muissakin organisaation toiminnoissa, johtaen sekä suoriin että epäsuoriin laatukustannuksiin. Täten kirjallisuuden käsitys reaktiivisesta laadunhallinnasta pätee suurin osin myös kohdeyrityksessä. Tutkimus pyrkii ratkaisemaan laadunhallinnallisia haasteita yrityksessä hyödyntämällä olemassa olevan kirjallisuuden pääkohtia sekä havaintoja benchmarkingista. Olemassa oleva kirjallisuus korostaa laatusuunnittelun ja jatkuvan kehittymisen konsepteja tärkeimpinä tekijöinä organisaation kehittyessä ennakoivaan laadunhallintaan, sisältäen myös laatujärjestelmän suunnittelun. Benchmarking-yritysten käyttämät laatujärjestelmät poikkeavat toisistaan, mutta niiden yhdistävinä, laatua edistävinä tekijöinä havaittiin prosessijohtaminen, selkeä järjestelmädokumentaatio, selkeät tavoitteet sekä järjestelmällinen laadunhallinta tuotekehitysprosesseissa. Kirjallisuuden ja empiiristen havaintojen vertailu osoittaa, että myös kohdeyrityksen laadunhallintaa voidaan kehittää prosessipohjaisen laatujärjestelmän toteuttamisen avulla. Ehdotettu kehitysmalli kattaa ne perustavanlaatuiset laadunhallinnan menetelmät, jotka kohdeyrityksen tulee sisäistää laatujärjestelmän kehittämiseksi. Kehitysehdotukset sisältävät laatusuunnittelun, organisaation suorituskyvyn mittaamisen ja prosessijohtamisen, jotka yhdessä luovat rungon laatujärjestelmälle. Myös suositukset laatujärjestelmän dokumentaatiomenetelmistä ja auditoinnista on esitetty. Yhdessä kehitysehdotukset tarjoavat kohdeyritykselle konkreettisen mallin laatutyön aloittamiseksi, sekä tuotteiden ja palveluiden laadun kehittämiseksi

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Business Intelligence: Development of a performance monitoring dashboard in a pharmaceutical company

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsBusiness intelligence is a concept that has been around for over 150 years. Although, only for the past 10 years has it become an idea wildly known in the technology world. Now, it is a tool used to help organizations handle big amounts of data and transform it into real-time information and ultimately make better and more effective decisions. In this context, the report describes my internship experience working as a Business Intelligence consultant at SDG. Which is an established consulting firm with offices in almost every continent. SDG helps companies handle their most important challenges as well as discover new opportunities with the use of advanced analytics and data-driven business models. The report specifies one of the projects that was accomplished during my time at SDG, with the role of developer. The goal of this project was to develop a dashboard using Qlik that would help two pharmaceutical companies measure the performance of a new drug. To do so the necessary data sources were provided from both sides of the company, and using an ETL approach, the data was integrated and ready to be used in the project. The companies provided an initial mock-up of the expected visualizations, and with some changes considering the initial discoveries, the data model was created. Following, the KPI’s were defined and implemented in the dashboard’s visualizations. The project was deemed successful, and it fulfilled the clients’ expectations

Visual Data Mining : Real Applications and New Approaches

En los últimos años, la visualización de datos se ha convertido en un área muy activa y vital de la investigación. Es una manera eficaz de analizar grandes cantidades de datos para identificar correlaciones, tendencias, valores extremos, patrones, entre otra mucha información. Los datos sin procesar a menudo carecen de sentido, pero representar dichos datos visualmente ofrece al público un contexto importante para entender la información contenida en ellos. Debido a la importancia de esta área de investigación, y a su novedad, esta tesis se centra en esta temática y pretende descubrir nuevos hallazgos, extraer conclusiones y legar contribuciones relevantes a la comunidad científica en dicho campo. Para alcanzar dicho propósito, este trabajo trata de abordar dos objetivos principales. El primer objetivo de la presente tesis es tratar de desarrollar nuevos métodos de visualización para interpretar los resultados de varios algoritmos de minería de datos. Por ejemplo, el análisis de clusters o técnicas de agrupamiento es un gran desafío en la visualización de datos; por esta razón, ambos van a menudo de la mano. Sin embargo, hay una falta de técnicas de visualización asociadas al clustering y clustering jerárquico que proporcionen información sobre los valores de los atributos de los centroides y de las relaciones entre ellos. Por lo tanto, esta tesis investiga nuevas aproximaciones que hagan posible incluir esta información visualmente, además de encontrar nuevos métodos para visualizar los resultados de varios algoritmos de minería de datos, aparte de los anteriormente mencionados, con el fin de ayudar a simplificar su interpretación y para obtener una mejor comprensión. Otro de los objetivos de esta tesis se centra en abordar diferentes problemas reales de diversa índole, algunos de ellos enmarcados en proyectos de investigación financiados. La solución de estos problemas se aborda a través de la visualización de datos y minería de datos visual con el fin de obtener una perspectiva sobre el problema, lo que hace posible la extracción de conocimiento, el descubrimiento de información oculta y encontrar patrones y relaciones entre los datos. En particular, la presente tesis se centra en el uso de los conocidos Self-Organizing Maps (SOMs) para resolver problemas reales en diversos campos de investigación, proporcionando soluciones a problemas complejos que de otra manera habría sido muy difícil de resolver.Data visualization has in recent years become a very active and vital area of research. It is an effective way to analyze large amounts of data to identify correlations, trends, outliers, patterns, among many other information. Raw data are often meaningless, but representing visually such data offers audiences important context for understanding the information contained in them. Due to the importance of this area of research, and its novelty, this thesis aims to discover new findings, draw conclusions and bequeath significant contributions to the scientific community in this field. To achieve this purpose, this work attempts to address two main objectives. The first objective of this thesis is to try to develop new visualization methods for interpreting the results of several data mining algorithms. For example, cluster analysis is a big challenge in data visualization; for this reason, they both often go hand in hand. Nonetheless, there is a lack of visualization techniques associated with clustering and hierarchical clustering that provide information about the values of the centroids’ attributes and the relationships among them. Thus, this thesis researches new approaches that make possible to include this information visually, as well as to find new methods for visualizing the results of several data mining algorithms, apart from those above mentioned, in order to help simplify their interpretation and to obtain a better understanding. Another objective of the present thesis is focused on addressing different real problems of diverse nature, some of them framed in funded research projects. The solution of these problems are approached through data visualization and visual data mining in order to gain insight about the problem making possible the knowledge extraction, the discovery of hidden information, and finding patterns and relationships in data. Particularly, the present thesis focuses on the use of the well-known Self- Organizing Maps (SOMs) to solve real problems in several different fields of research, providing solutions to complex problems that would otherwise have been very difficult to solve

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available

STATISTICAL METHODS AND TOOLS FOR FOOTBALL ANALYTICS

Gli strumenti di digitalizzazione e di machine learning hanno avuto una crescita esponenziale nel corso degli ultimi anni e tutto ciò ha riguardato di riflesso i più svariati settori della nostra vita: in particolar modo, questa tesi ha l'obiettivo di focalizzarsi sulla sport analytics, in particolare sul calcio, lo sport più praticato al mondo. A causa della crescente necessità dei club professionistici, gli strumenti analitici nel calcio stanno diventando uno snodo cruciale per aiutare gli staff tecnici, le aree scouting e i management nell'ottimizzare e nel prendere decisioni; per questa ragione, in questa tesi sono state sviluppate diverse applicazioni statistiche, una per ogni capitolo, ognuna corrispondente ad un articolo scientifico pubblicato o in revisione da parte di una rivista scientifica. Nell'introduzione della tesi sono elencate le principali attività svolte durante il periodo di dottorato, seguite dal primo capitolo dedicato alla revisione della letteratura, effettuato in forma analitica grazie ad un originale analisi bibliometrica sugli ultimi 10 anni di produzione scientifica. Il secondo capitolo è dedicato ad un approfondimento metodologico sul Partial Least Squares Structural Equation Modeling (PLS-SEM), metodologia statistica utilizzata per la creazione di indicatori compositi volti ad analizzare la performance dei giocatori, tramite l'utilizzo di dati forniti dagli esperti di Electronic Arts (EA) e disponibili sulla piattaforma di data science Kaggle; nella seconda parte del capitolo è presente l'applicazione sviluppata, in particolare un modello gerarchico del terzo ordine utilizzando i Key Performance Indices di sofifa per calcolare un indicatore composito differenziato per ogni ruolo. Nel terzo capitolo il modello sviluppato nel capitolo precedente è stato rifinito e validato per ogni ruolo, applicando una Confirmatory Tetrad Analysis (CTA) e una Confirmatory Composite Analysis (CCA), utilizzando i dati relativi ai più recenti campionati (stagione 2021/2022); i risultati ottenuti sottolineano come le diverse aree e sottoaree di performance hanno diversi pesi e valori a seconda del ruolo del giocatore. Infine, con lo scopo di valutare la validità predittiva del modello, il nuovo indicatore composito (PI) overall è stato confrontato con un benchmark (EA overall) e con delle variabili proxy come il valore di mercato e l'ingaggio dei giocatori, ottenendo dei risultati interessanti e significativi. A questo punto, nell'ultimo capitolo gli indicatori compositi sviluppati in precedenza sono stati introdotti come regressori nel modello di expected goal (xG), con lo scopo di migliorarne l'accuratezza predittiva. Il modello xG è infatti uno dei modelli emergenti nel mondo della football analytics e ha lo scopo di prevedere i goal e misurarne la qualità. Per fare questo è stato applicato un modello logistico classico ed un modello logistico aggiustato su diversi scenari per campioni bilanciati. Nella fattispece, alcuni indicatori compositi e altri nuovi regressori (variabili di tracking) sono risultati significativi per il modello di classificazione, contribuendo a migliorare l'accuratezza nella predizione dei goal, confrontandolo con un benchmark.Machine learning and digitization tools are exponentially increasing in these last years and their applications are reflected in different areas of our life: in particular, this thesis aims to focus on football (i.e. soccer for Americans), the most practised sport in the world. Due to needing of professional teams, analytics tools in football are becoming a crucial point, in order to help technical staff, scouting and clubs management in policy evaluation and to optimize strategic decisions; for this reason, different statistical applications have been developed, one for each chapter, corresponding to published or submitted scientific articles. In the first part are presented the main activities I attended during my PhD, then the first chapter is dedicated to literature review, by an original bibliometric analysis relying football analytics development in the decade 2010-2020. The following chapter is designated for in-depth the Partial Least Squares Structural Equation Modeling (PLS-SEM) framework, in order to study and create some original composite indicators for players performance using data provided by Electronic Arts (EA) experts and available on the Kaggle data science platform; in particular, a Third-Order PLS-PM approach was adopted on the sofifa Key Performance Indices, in order to compute a composite indicator differentiated by role. In the next chapter the PLS-SEM model has been refined and validated, applying both Confirmatory Tetrad Analysis (CTA) and Confirmatory Composite Analysis (CCA), using EA \emph{sofifa} data relying the most recent football season (2021/2022); the final results underline how some sub-areas of performance have different significance weights depending on the player's role; as concurrent and predictive analysis, the new Player Indicator (PI) overall was compared with a benchmark (the EA overall) and with some performance quality proxies, such as players' market value and wage, showing interesting and consistent relations. At this point, these original composite indicators have been introduced as regressors in the last chapter for improving in terms of prediction performance the expected goal (xG) model; it is one emerging tool in the field of football analytics, that aims to predict goal and measure the quality of each shot, by applying a supervised machine learning approach (logit model) on different scenarios for sample balanced techniques. In particular, some performance composite indicators obtained by the PLS-SEM and some original tracking variables are significant for the classification model, contributing to increase the goal prediction probability, compared with a benchmark