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The application of business process methods in the domain of disaster response management (DRM) is seen as promising approach due to the similarity of business processes and disaster response processes at the general structure and goals. But up to now only a few approaches were able to handle the special characteristics of the DRM domain. Thus, the purpose of this paper is to identify the existing approaches and analyze them for the discussion of general requirements for applying methods and tools from business process management to DRM.

A structured literature review covering a wide field of information system-related publications (conferences and journals) is used to identify and classify general requirements discussed as the state of the art.

The work in this paper resulted in a suitable classification of requirements for the development of process-oriented DRM approaches deduced from the existing work. This was used to outline and analyze the current research landscape of this topic and identify research gaps as well as existing limitations.

Although the review of the state of the art is based on a wide set of publication databases, there may exist relevant research papers which have not been taken into consideration.

The elaborated requirements provide value for both the research community and practitioners. They can be considered to develop new or improve existing DRM systems and, thus, to exploit the potentials of process-oriented IT in supporting DRM in the case of disaster.

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.