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The Internet of Things (IoT) is a network of addressable, physical objects that contain embedded sensing, communication and actuating technologies to sense and interact with their environment (Geschickter 2015). Like every novel paradigm, the IoT sparks interest throughout all domains both in theory and practice, resulting in the development of systems pushing technology to its limits. These limits become apparent when having to manage an increasing number of Things across various contexts. A plethora of IoT architecture proposals have been developed and prototype products, such as IoT platforms, been introduced. However, each of these architectures and products apply their very own interpretations of an IoT architecture and its individual components so that IoT is currently more an Intranet of Things than an Internet of Things (Zorzi et al. 2010). Thus, this thesis aims to develop a common understanding of the elements forming an IoT architecture and provide high-level specifications in the form of a Holistic IoT Architecture Framework.
Design Science Research (DSR) is used in this thesis to develop the architecture framework based on the pertinent literature. The development of the Holistic IoT Architecture Framework includes the identification of two new IoT Architecture Perspectives that became apparent during the analysis of the IoT architecture proposals identified in the extant literature. While applying these novel perspectives, the need for a new component for the architecture framework, which was merely implicitly mentioned in the literature, became obvious as well. The components of various IoT architecture proposals as well as the novel component, the Thing Management System, were combined, consolidated and related to each other to develop the Holistic IoT Architecture Framework. Subsequently, it was shown that the specifications of the architecture framework are suitable to guide the implementation of a prototype.
This contribution provides a common understanding of the basic building blocks, actors and relations of an IoT architecture.
Mapping ORM to TGraph
(2017)
Object Role Modeling (ORM) is a semantic modeling language used to describe objects and their relations amongst each other. Both objects and relations may be subject to rules or ORM constraints.
TGraphs are ordered, attributed, typed and directed graphs. The type of a TGraph and its components, the edges and vertices, is defined using the schema language graph UML (grUML), a profiled version of UML class diagrams. The goal of this thesis is to map ORM schemas to grUML schemas in order to be able to represent ORM schema instances as TGraphs.
Up to this point, the preferred representation for ORM schema instances is in form of relational tables. Though mappings from ORM schemas to relational schemas exist, those publicly available do not support most of the constraints ORM has to offer.
Constraints can be added to grUML schemas using the TGraph query language GReQL, which can efficiently check whether a TGraph validates the constraint or not. The graph library JGraLab provides efficient implementations of TGraphs and their query language GReQL and supports the generation of grUML schemas.
The first goal of this work is to perform a complete mapping from ORM schemas to grUML schemas, using GReQL to sepcify constraints. The second goal is to represent ORM instances in form of TGraphs.
This work gives an overview of ORM, TGraphs, grUML and GReQL and the theoretical mapping from ORM schemas to grUML schemas. It also describes the implementation of this mapping, deals with the representation of ORM schema instances as TGraphs and the question how grUML constraints can be validated.
The extensive literature in the data visualization field indicates that the process of creating efficient data visualizations requires the data designer to have a large set of skills from different fields (such as computer science, user experience, and business expertise). However, there is a lack of guidance about the visualization process itself. This thesis aims to investigate the different processes for creating data visualizations and develop an integrated framework to guide the process of creating data visualizations that enable the user to create more useful and usable data visualizations. Firstly, existing frameworks in the literature will be identified, analyzed and compared. During this analysis, eight views of the visualization process are developed. These views represent the set of activities which should be done in the visualization process. Then, a preliminary integrated framework is developed based on an analysis of these findings. This new integrated framework is tested in the field of Social Collaboration Analytics on an example from the UniConnect platform. Lastly, the integrated framework is refined and improved based on the results of testing with the help of diagrams, visualizations and textual description. The results show that the visualization process is not a waterfall type. It is the iterative methodology with the certain phases of work, demonstrating how to address the eight views with different levels of stakeholder involvement. The findings are the basis for a visualization process which can be used in future work to develop the fully functional methodology.
The Internet of Things (IoT) recently developed from the far-away vision of ubiquitous computing into very tangible endeavors in politics and economy, implemented in expensive preparedness programs. Experts predict considerable changes in business models that need to be addressed by organizations in order to respond to competition. Although there is a need to develop strategies for upcoming transformations, organizational change literature did not turn to the specific change related to the new technology yet. This work aims at investigating IoT-related organizational change by identifying and classifying different change types. It therefore combines the methodological approach of grounded theory with a discussion and classification of identified change informed by a structured literature review of organizational change literature. This includes a meta-analysis of case studies using a qualitative, exploratory coding approach to identify categories of organizational change related to the introduction of IoT. Furthermore a comparison of the identified categories to former technology-related change is provided using the example of Electronic Business (e-business), Enterprise Resource Planning (ERP) systems, and Customer Relationship Management (CRM) systems. As a main result, this work develops a comprehensive model of IoT-related business change. The model presents two main themes of change indicating that personal smart things will transform businesses by means of using more personal devices, suggesting and scheduling actions of their users, and trying to avoid hazards. At the same time, the availability of information in organizations will further increase to a state where information is available ubiquitously. This will ultimately enable accessing real time information about objects and persons anytime and from any place. As a secondary result, this work gives an overview on concepts of technology-related organizational change in academic literature.
The output of eye tracking Web usability studies can be visualized to the analysts as screenshots of the Web pages with their gaze data. However, the screenshot visualizations are found to be corrupted whenever there are recorded fixations on fixed Web page elements on different scroll positions. The gaze data are not gathered on their fixated fixed elements; rather they are scattered on their recorded scroll positions. This problem has raised our attention to find an approach to link gaze data to their intended fixed elements and gather them in one position on the screenshot. The approach builds upon the concept of creating the screenshot during the recording session, where images of the viewport are captured on visited scroll positions and lastly stitched into one Web page screenshot. Additionally, the fixed elements in the Web page are identified and linked to their fixations. For the evaluation, we compared the interpretation of our enhanced screenshot against the video visualization, which overcomes the problem. The results revealed that both visualizations equally deliver accurate interpretations. However, interpreting the visualizations of eye tracking Web usability studies using the enhanced screenshots outperforms the video visualizations in terms of speed and it requires less temporal demands from the interpreters.