COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets

Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets.

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three gaps stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind ( http://www.dataonthemind.org ), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement-not supplant-traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets

Capturing high-level requirements of information dashboards' components through meta-modeling

[EN]Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays.These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes.However, the design and development processes are complex, because several perspectives and components can be involved.Tailoringcapabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structuresand relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles.One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities.To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided

Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization

[EN]Information dashboards are useful tools for exploiting datasets and support decision-making processes. However, these tools are not trivial to design and build. Information dashboards not only involve a set of visualizations and handlers to manage the presented data, but also a set of users that will potentially benefit from the knowledge generated by interacting with the data. It is important to know and understand the requirements of the final users of a dashboard because they will influence the design processes. But several user profiles can be involved, making these processes even more complicated. This paper identifies and discusses why it is essential to include the final users when modeling a dashboard. Through meta-modeling, different characteristics of potential users are structured, thus obtaining a meta-model that dissects not only technical and functional features of a dashboard (from an abstract point of view) but also the different aspects of the final users that will make use of it. By identifying these user characteristics and by arranging them into a meta-model, software engineering paradigms such as model-driven development or software product lines can employ it as an input for generating concrete dashboard products. This approach could be useful for generating Learning Analytics dashboards that take into account the users' motivations, beliefs, and knowledge

Management Misinformation Systems: A Time to Revisit?

In this essay, we revisit Ackoff’s (1967) classic “Management Misinformation Systems” and its five myths. The paper appeared at the dawn of the information systems (IS) field and shattered popular assumptions about designing and using IS. The paper shaped the direction and scope of scholarly discourse around information systems; in contrast to dominant claims at that time, he argued that managers swam in the abundance of irrelevant information, were victims of poor modeling and, consequently, poor understanding of their own decisions, participated in destructive communication due to conflicting goals, and had a poor understanding of how systems worked. Despite the passage of 50 years (and many revolutions in information technology), researchers in the IS field still regard Ackoff’s arguments as valid and rarely debate them. Yet, given the new information-rich environments and our nearly limitless capability to collect and analyze data, we may need to reexamine these arguments to correctly frame information systems’ contemporary effects on managerial decision making. We scrutinize Ackoff’s five assumptions in light of today’s IT and data-rich environments and identify key tenets that will reframe the disciplinary discourse concerning the effects of information systems. We identify significant shifts in research on decision making including the role of abduction, data layering and options, and intelligence augmentation. We honor the extraordinary legacy of Ackoff’s remarkable paper as an IS scholar by shaping the field’s future inquiries in the spirit of the original paper

Discovering the Relationship Between Big Data, Big Data Analytics, and Decision Making: A Structured Literature Review

This paper focuses on providing a structured literature review on the role of Big Data (BD) and Big Data Analytics (BDA) in supporting decision making. The study aims to systematize the knowledge, the primary results, and research gaps related to BD and BDA in strategic management and in decision making by providing a future research agenda. Adopting the methodology of Massaro et al. (2015), the structured literature review investigates this phenomenon analyzing a sample of 97 articles published in high-level scientific journals ranked in ABS list in the Marketing, Strategic Management, Ethics, Gender, and Social Responsibility area. Bibliometric analysis, content analysis, and the PRISMA protocol have been used for the review. The study unveils the subject of decisions, factors influencing good decisions, and the main effects of using BD and BDA in decision making. New organizational factors, data chain dynamics, and inhibitors should be explored to remove the obstacles in decision making. The relationship between BD/BDA and decision making remains underexplored in public organizations, non-profit organizations, and small and medium-sized firms

THE ROLE OF EMOTION IN VISUALIZATION

The popular notion that emotion and reason are incompatible is no longer defensi- ble. Recent research in psychology and cognitive science has established emotion as a key element in numerous aspects of perception and cognition, including attention, memory, decision-making, risk perception, and creativity. This dissertation centers around the observation that emotion influences many aspects of perception and cog- nition that are crucial for effective visualization. First, I demonstrate that emotion influences accuracy in fundamental visualiza- tion tasks by combining a classic graphical perception experiment (from Cleveland and McGill) with emotion induction procedures from psychology (chapter 3). Next, I expand on the experiments in the first chapter to explore additional techniques for studying emotion and visualization, resulting in an experiment that shows that performance differences between primed individuals persist even as task difficulty in- creases (chapter 4). In a separate experiment, I show how certain emotional states (i.e. frustration and engagement) can be inferred from visualization interaction logs using machine learning (chapter 5). I then discuss a model for individual cognitive dif- ferences in visualization, which situates emotion into existing individual differences research in visualization (chapter 6). Finally, I propose an preliminary model for emotion in visualization (chapter 7)