Lessons learned from challenging data science case studies

In this chapter, we revisit the conclusions and lessons learned of the chapters presented in Part II of this book and analyze them systematically. The goal of the chapter is threefold: firstly, it serves as a directory to the individual chapters, allowing readers to identify which chapters to focus on when they are interested either in a certain stage of the knowledge discovery process or in a certain data science method or application area. Secondly, the chapter serves as a digested, systematic summary of data science lessons that are relevant for data science practitioners. And lastly, we reflect on the perceptions of a broader public towards the methods and tools that we covered in this book and dare to give an outlook towards the future developments that will be influenced by them

A survey of comics research in computer science

Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, human-computer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks

New Frontiers of Quantified Self: Finding New Ways for Engaging Users in Collecting and Using Personal Data

In spite of the fast growth in the market of devices and applications that allow people to collect personal information, Quantified Self (QS) tools still present a variety of issues when they are used in everyday lives of common people. In this workshop we aim at exploring new ways for designing QS systems, by gathering different researchers in a unique place for imagining how the tracking, management, interpretation and visualization of personal data could be addressed in the future

User behaviour analysis and personalized TV content recommendation

Nowadays, there are many channels and television (TV) programs available, and when the viewer is confronted with this amount of information has difficulty in deciding which wants to see. However, there are moments of the day that viewers see always the same channels or programs, that is, viewers have TV content consumption habits. The aim of this paper was to develop a recommendation system that to be able to recommend TV content considering the viewer profile, time and weekday. For the development of this paper, were used Design Science Research (DSR) and Cross Industry Standard Process for Data Mining (CRISP-DM) methodologies. For the development of the recommendation model, two approaches were considered: a deterministic approach and a Machine Learning (ML) approach. In the ML approach, K-means algorithm was used to be possible to combine STBs with similar profiles. In the deterministic approach the behaviors of the viewers are adjusted to a profile that will allow you to identify the content you prefer. Here, recommendation system analyses viewer preferences by hour and weekday, allowing customization of the system, considering your historic, recommending what he wants to see at certain time and weekday. ML approach was not used due to amount of data extracted and computational resources available. However, through deterministic methods it was possible to develop a TV content recommendation model considering the viewer profile, the weekday and the hour. Thus, with the results it was possible to understand which viewer profiles where the ML can be used.COMPETE: POCI-01-0145-FEDER-007043 and FCT (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013 and was developed in partnership with AlticeLab

Design Spaces in Visual Analytics Based on Goals: Analytical Behaviour, Exploratory Investigation, Information Design & Perceptual Tasks

This paper considers a number of perspectives on design spaces in visual analytics and proposes a new set of four design spaces, based on user goals. Three of the user goals are derived from the literature and are categorised under the terms exploratory investigation, perceptual tasks, and information design. The fourth goal is categorised as analytical behaviour; a recently defined term referring to the study of decision-making facilitated by visual analytics. This paper contributes to the literature on decision-making in visual analytics with a survey of real-world applications within the analytical behaviour design space and by providing a new perspective on design spaces. Central to our analysis is the introduction of decision concepts and theories from economics into a visual analytics context. Given the recent interest in decision-making we wanted to understand the emerging topic of analytical behaviour as a design space and found it necessary to look at more than just decision-making to make a valuable contribution. The result is an initial framework suitable for use in the analysis or design of analytical behaviour applications