Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data

RegTech Opportunities in the Platform-Based Business Sector

The notion of RegTech has emerged in recent years, but its application appears to have been mostly limited to the use of technology to assist with organisations\u27 compliance with regulatory requirements. A model is presented that encompasses RegTech\u27s full scope, embracing its capacity to address the needs not only of regulatees, but also of regulators and the intended beneficiaries of regulatory regimes. The model is then applied to the recently-popularised platform-based business model, whose mature form is evidenced by Uber and Airbnb. A range of opportunities is identified for practitioners and researchers to contribute to the application of information technologies in the regulatory space

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Knowledge modelling of emerging technologies for sustainable building development

In the quest for improved performance of buildings and mitigation of climate change, governments are encouraging the use of innovative sustainable building technologies. Consequently, there is now a large amount of information and knowledge on sustainable building technologies over the web. However, internet searches often overwhelm practitioners with millions of pages that they browse to identify suitable innovations to use on their projects. It has been widely acknowledged that the solution to this problem is the use of a machine-understandable language with rich semantics - the semantic web technology. This research investigates the extent to which semantic web technologies can be exploited to represent knowledge about sustainable building technologies, and to facilitate system decision-making in recommending appropriate choices for use in different situations. To achieve this aim, an exploratory study on sustainable building and semantic web technologies was conducted. This led to the use of two most popular knowledge engineering methodologies - the CommonKADS and "Ontology Development 101" in modelling knowledge about sustainable building technology and PV -system domains. A prototype system - Photo Voltaic Technology ONtology System (PV -TONS) - that employed sustainable building technology and PV -system domain knowledge models was developed and validated with a case study. While the sustainable building technology ontology and PV -TONS can both be used as generic knowledge models, PV -TONS is extended to include applications for the design and selection of PV -systems and components. Although its focus was on PV -systems, the application of semantic web technologies can be extended to cover other areas of sustainable building technologies. The major challenges encountered in this study are two-fold. First, many semantic web technologies are still under development and very unstable, thus hindering their full exploitation. Second, the lack of learning resources in this field steepen the learning curve and is a potential set-back in using semantic web technologies

Contributions to the selection and implementation of standard software for CRM and electronic invoicing

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IS standards in designing business-to-government collaborations.

IS STANDARDS IN DESIGNING BUSINESS-TO-GOVERNMENT COLLABORATIONS. Elaborating the impact of standards on inter-organizational collaborations, inter-organizational studies demonstrated a standard’s positive impact on the collaboration between governmental and business partners. How and under which conditions information systems (IS) standards contribute to the effectiveness of business-to-government (B2G) collaborations in customs management is the topic of this thesis. Chapter 2 provides the theoretical and methodological background of the thesis. It illustrates how standards research emerged under institutional conditions such as actor types, linkages and social structures. With the case study in Chapter 3, the thesis introduces a reference framework that gathers different aspects in three pre-selected international business-to-government collaborations. Describing the cases that are subject to the export from EU to non-EU countries a diagnosis of B2G collaborations and relevant elements for the design of the artifact is conducted. A diagnosis of related work in the field of B2G collaborations is provided in Chapter 4. The assessment of collaboration forms revealed necessary constructs of a procedure model and institutional steps necessary to form B2G collaboration as such. Chapter 5 distils related work of IS standards research. In Chapters 6 and 7 considerations from the previous chapters lead to the core part of the thesis, the design and build of a procedure model to institutionalize B2G collaborations, the B2G Procedure Model (B2GPM). The results from the first round of design, the building blocks for B2G collaborations, are subject to Chapter 6. They conclude in a set of design principles of the B2GPM that are being introduced in the chapter. Chapter 7 covers the second round of design by refining the elements of B2G collaboration and the design principles. It continues with the design of the B2GPM. The composition, description, and documentation of the procedure model are the core part of this chapter. Chapter 8 is dedicated to the question of required organizational adoption to deploy the B2GPM. The model is seen as a procedural innovation by which B2G collaboration in customs management can be further improved. The applicability of the B2GPM is based on a series of evaluation cycles and results in the provision of influencing factors of organizational adoption.