Towards a Taxonomy of the Model-Ladenness of Data

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data

A General Forecast-error Taxonomy

The paper considers the sources of forecast errors and their consequences in an evolving economy subject to structural breaks,forecasting from mis-specified, data-based models. A model-free taxonomy of forecast errors highlights that deterministic shifts are a major cause of systematic forecast failure. Other sources seem to pose fewer problems. The taxonomy embeds several previous model-based taxonomies for VARs, VECMs, and multi-step estimators, and reveals the stringent requirements that rationality assumptions impose on economic agents.

Understanding Car Data Monetization: A Taxonomy of Data-Driven Business Models in the Connected Car Domain

Data monetization has proven to be one of the most viable profit pools across industries. As vehicles become increasingly connected, leveraging their collected data through novel business models is the most promising value driver for automotive enterprises. Despite the increasing practical relevance, theoretical and conceptual insights on connected cars and their associated business models are still scarce. Thus, we develop a taxonomy of data-driven business models in theconnected car domain according to four perspectives—value proposition, value architecture, value network, and value finance. Further, we apply the taxonomy to analyze the business model of 70 companies acting under the realm of connected cars. A subsequent evaluation indicates both the robustness and general feasibility of our taxonomy. Our taxonomy contributes to descriptive knowledge in this emerging field and enables researchers and practitioners to analyze, design, andconfigure data-driven business models for connected cars

UNDERSTANDING THE ANATOMY OF DATA-DRIVEN BUSINESS MODELS – TOWARDS AN EMPIRICAL TAXONOMY

As a consequence of the increasing digitization, massive amounts of data are created every day. While scholars and practitioners suggest that organizations can use this data to develop new data-driven business models, many organizations struggle to systematically develop such models. A fundamental challenge in this regard is presented by the limited research on data-driven business models. Accordingly, the goal of this research is to better understand data-driven business models by identifying key dimensions that can be used to distinguish them and to develop a taxonomy. As our taxonomy aims to guide future studies in a way that ultimately serves organizations, it is based on dimensions regarded to be most relevant from the practitioners’ perspective. To develop this taxonomy, we utilize an established empirical approach based on a combination of multidimensional scaling (MDS), property fitting (ProFit), and qualitative data. Our results reveal that the most important dimensions distinguish data-driven business models based on the data source utilized, the target audience, and the technological effort required. Based on these dimensions, our taxonomy distinguishes eight ideal-typical categories of data-driven business models. By providing an increased understanding regarding the topic, our results form the foundation for subsequent investigations in this new field of research

How to Share Data Online (fast) – A Taxonomy of Data Sharing Business Models

Data is an integral part of almost every business. Sharing data enables new opportunities to generate value or enrich the existing data repository, opening up new potentials for optimization and business models. However, these opportunities are still untapped, as sharing data comes with many challenges. First and foremost, aspects such as trust in partners, transparency, and the desire for security are issues that need to be addressed. Only then can data sharing be used efficiently in business models. The paper addresses this issue and generates guidance for the data-sharing business model (DSBM) design in the form of a taxonomy. The taxonomy is built on the empirical analysis of 80 DSBMs. With this, the primary contributions are structuring the field of an emerging phenomenon and outlining design options for these types of business models

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure