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The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study.

To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.

The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.

A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.

This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.

Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.

DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.

The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.

With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.

The purpose of this paper is to analyze the role of industrial self-supply in the transition process from centralized energy generation based on fossil fuels and nuclear power to decentralized supply based on renewable energies in the Bavarian electricity system.

To quantify effects on system and price stability, a model of the Bavarian electricity grid is created and used to simulate electricity system behavior during a 1-year period for scenarios that are characterized by parameter variations in industrial self-supply, nuclear power capacity, renewable power generation and the capacity of electricity imports.

The simulations show that industrial self-supply can reduce instances of maximum grid utilization by 23 per cent and, based on the merit-order effect, decrease electricity market prices by 1.90 and 5.03 €/MWh in the scenarios with and without nuclear power, respectively; these values represent 5.7 and 15.0 per cent of average market prices from 2014.

The analysis shows that industrial self-supply can contribute to transforming the electricity system in a secure, sustainable and affordable manner. However, merit-order-based price effects have a limitation concerning the future applicability of results as quantified effects may not be permanent when the electricity system adapts.

This paper connects industrial self-supply and the merit-order effect within a nodal energy model. It provides insights into the relevant interdependencies and reciprocal effects by means of a simulation.