A Knowledge Perspective on Big Data by Joining Enterprise Modeling and Data Analyses

Fill, Hans-Georg; Johannsen, Florian

doi:10.1109/hicss.2016.503

Cited by 14 publications

(7 citation statements)

References 43 publications

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“…Research is required on lightweight EM practices that do not require extensive familiarity with underlying formalisms. A recent example can be found in quality management where the analysis of local practices led to an EM method for business process improvement (Fill and Johannsen 2016). Future EM tools should rely on interaction and interface paradigms that represent the standard in office environments, e.g., browser-based applications today and in the future deviceless interaction.…”

Section: Specific But Integrative Local Practicesmentioning

confidence: 99%

“…Through local practices, the scope of enterprise models has to be widened to act as boundary objects between domain experts and machine-processing mechanisms. Besides the establishment of interfaces to complimentary disciplines such as big data analysis (Fill and Johannsen 2016), future research should also open up new domains for EM -e.g., for conceptualizing modeling methods for the legal/compliance domain or for cyber-physical systems. The processing of model information needs to be accomplished via new approaches for visualizing model contents (Iyer and Basole 2016) and the semantic integration of models.…”

Section: Specific But Integrative Local Practicesmentioning

confidence: 99%

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From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

et al. 2018

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The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this ''grass roots modeling'' could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research agenda.

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Section: Specific But Integrative Local Practicesmentioning

confidence: 99%

Section: Specific But Integrative Local Practicesmentioning

confidence: 99%

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

et al. 2018

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“…Enterprise modeling can support this process through the abstraction from complex technologies and by presenting them in a way that facilitates their application by domain experts. An example for such an abstraction that is of high relevance for digital businesses are modeling approaches for data analytics [28], [29], [30], [31]. These permit even users with little technical knowledge to use these technologies for their tasks and thus quickly leverage their potential.…”

Section: Enterprise Modeling For Digital Ubiquitymentioning

confidence: 99%

Enterprise Modeling: From Digital Transformation to Digital Ubiquity

Fill¹

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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While digital transformation is still a challenge for many companies when introducting digital technologies in existing processes and business models, digital ubiquity stands for the next step in digitalization. It characterizes the omnipresence of a large range of digital technologies, connectivity, and data as well as entirely digital organizations. This includes for example upcoming technologies such as distributed ledgers, artificial intelligence or augmented reality and according interfaces and data sources as well as decentralized apps and autonomous organizations. The challenge thus becomes to optimally deal with these opportunities and deploy them efficiently in business scenarios. In this paper we will investigate the role of enterprise modeling under this paradigm and how it can contribute to a well-structured, systematic understanding of complex digital phenomena for supporting business and technological decisions.

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“…Volume is about the amount of data "available" to an organization, not just what it owns . On the technical side, the challenges lie in the features making big data difficult to handle and to extract from different systems and platforms [10]. Many authors concurred that big data grows at exceptional rates.…”

Section: Growth Rate (Gr 23)mentioning

confidence: 99%

Big Data Redux: New Issues and Challenges Moving Forward

Espinosa

Kaisler

Armour

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics.

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A Knowledge Perspective on Big Data by Joining Enterprise Modeling and Data Analyses

Cited by 14 publications

References 43 publications

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

Enterprise Modeling: From Digital Transformation to Digital Ubiquity

Big Data Redux: New Issues and Challenges Moving Forward

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