DCEIS 2020 Abstracts

Full Papers
Full Papers
Paper Nr: 1
Title:

Automatic Knowledge Extraction and Identification Across Multiple Phases to Enhance Research Data Management
Authors:

Benedikt Heinrichs
Abstract: Institutional research data management (RDM) is currently an important topic for universities worldwide and therefore also at the RWTH Aachen University. This projected work aims at tackling some questions to further improve the state of RDM by finding a solution for tracking research data over its whole life-cycle and helping it transition through multiple phases. The research focuses on a three-step approach: information extraction as metadata, similarity metrics and finally tracking in decentralized systems. Finding a general way to describe research data automatically with so-called metadata is the first step for tackling this research. Using this metadata, a method will be developed which helps to find similar so-called research data objects, which describe a singular entity in a set of research data, and by that similar research. This will in return help to identify moved research data since it would be able to relatively accurate describe if two research data objects are similar or not. The results of the research will over time be integrated in a central system which supports researchers with data management services.
Paper Nr: 2
Title:

Towards NLP-supported Semantic Data Management
Authors:

Andreas Burgdorf, André Pomp and Tobias Meisen
Abstract: The heterogeneity of data poses a great challenge when data from different sources is to be merged for one application. Solutions for this are offered, for example, by ontology-based data management (OBDM). A challenge of OBDM is the automatic creation of semantic models from datasets. This process is typically performed either data- or label-driven and always involves manual human intervention. We identified textual descriptions of data – a form of metadata, quickly to be produced and consumed by humans - as third possible basis for automatic semantic modelling. In this paper, we present, how we plan to use textual descriptions to enhance semantic data management. We will use state of the art NLP technologies to identify concepts within textual descriptions and build semantic models from this in combination with an evolving ontology. We will use automatically identified models in combination with the human data provider to automatically extend the ontology so that it learns new verified concepts over time. Finally, we will use the created ontology and automatically identified semantic models to either rate descriptions for new data sources or even to automatically generate descriptive texts that are easier to understand by the human user than formal models. We present the procedure which we plan for the ongoing research, as well as expected outcomes.
Paper Nr: 3
Title:

The Proposal of an Acceleration Model to Support the Combined Use of Agile with User-Centered Design and Lean Startup
Authors:

Cassiano Moralles and Sabrina Marczak
Abstract: Despite the benefits of Agile Software Development, organizations still do not have a clear understanding of the problem to be solved by the software product-to-be, a problem that can solve by having a closer engagement with users. The combined use of Agile, User-Centered Design and Lean Startup has been pointed out as a strategy to fill in this gap. We propose literature and practice-informed model to aid in the adaptation to this combined approach. The model is composed of a set of principles, activities, and techniques derived from Agile, User-Centered Design, and Lean Startup pillars. These serve as guidance for software teams to determine the competences and skills they need, given the context they are inside. The model also accounts for forces that might influence the work processes. We discuss how the model could help teams adapt to the transformation to the combined approach and point out our future research plans to define an acceleration model.
Paper Nr: 4
Title:

Grounded Theory of the Evolutionary Behavior of Social Machines
Authors:

Brunno D. Souza
Abstract: The insertion of the combination of computational and human elements in society has led to the emerging area of Social Machines. The research challenges related to Social Machines are from understanding the theme, its functionalities, behavior, and evolution. Thus, the objective of this research proposal is to develop a grounded theory that portrays the evolutionary behavior of social machines and their perspectives, also based on the formation of taxonomy and ontology. The research is purely qualitative and uses the DSR (Design Science Research) methodology. In this research, too, a systematic mapping was made based on criteria of a systematic review, and it is expected that the results bring, in a way, a new grounded theory through a set of perspectives of the functioning of Social machines.