Abstraction, Visualization, and Evolution of Process Models

The increasing adoption of process orientation in companies and organizations has resulted in large process model collections. Each process model of such a collection may comprise dozens or hundreds of elements and captures various perspectives of a business process, i.e., organizational, functional, control, resource, or data perspective. Domain experts having only limited process modeling knowledge, however, hardly comprehend such large and complex process models. Therefore, they demand for a customized (i.e., personalized) view on business processes enabling them to optimize and evolve process models effectively. This thesis contributes the proView framework to systematically create and update process views (i.e., abstractions) on process models and business processes respectively. More precisely, process views abstract large process models by hiding or combining process information. As a result, they provide an abstracted, but personalized representation of process information to domain experts. In particular, updates of a process view are supported, which are then propagated to the related process model as well as associated process views. Thereby, up-to-dateness and consistency of all process views defined on any process model can be always ensured. Finally, proView preserves the behaviour and correctness of a process model. Process abstractions realized by views are still not sufficient to assist domain experts in comprehending and evolving process models. Thus, additional process visualizations are introduced that provide text-based, form-based, and hierarchical representations of process models. Particularly, these process visualizations allow for view-based process abstractions and updates as well. Finally, process interaction concepts are introduced enabling domain experts to create and evolve process models on touch-enabled devices. This facilitates the documentation of process models in workshops or while interviewing process participants at their workplace. Altogether, proView enables domain experts to interact with large and complex process models as well as to evolve them over time, based on process model abstractions, additional process visualizations, and process interaction concepts. The framework is implemented in a proof-ofconcept prototype and validated through experiments and case studies

Collaborative Business Process Management - A Literature-based Analysis of Methods for Supporting Model Understandability

Due to the growing amount of cooperative business scenarios, collaborative Business Process Management (cBPM) has emerged. The increased number of stakeholders with minor expertise in process modeling leads to a high relevance of model understandability in cBPM contexts. Despite extensive works in the research fields of cBPM and model understandability in BPM, there is no analysis and comprehensive overview of methods supporting process model understandability in cBPM scenarios. To address this research gap, this paper presents the results of a literature review. The paper identifies concepts for supporting model understandability in BPM, provides an overview of methods implementing these concepts, and discusses the methods’ applicability in cBPM. The four concepts process model transformation, process model visualization, process model description, and modeling support are introduced. Subsequently, 69 methods are classified and discussed in the context of cBPM. Results contribute to revealing existing academic voids and can guide practitioners in cBPM scenarios

Enabling Personalized Business Process Modeling: The Clavii BPM Platform

Increasing adoption of business process management systems has resulted in large business process models comprising hundreds of activities. Particularly, such process models are hard to understand and maintain. This issue requires innovative approaches to simplify and personalize process models. Therefore, this thesis introduces fundamentals for process views offering personalized perspectives for process participants by abstracting not necessary information. Furthermore, an approach for a domain-specific process modeling language, so-called Process Query Language, is presented. The latter offers process modeling notation independent abilities to define, search, and modify process models as well as process views. The proof-of-concept implementation, so-called Clavii BPM platform, shows up as integrated solution for simple, web-based business process modeling and execution. Thus, it implements basic concepts for process views and the PQL language

Language Dynamism: A Cross-Cultural Analysis of Political Discourse

Politicians constantly strive to manipulate language in a way that communicates their intentions without upsetting their audience. The present study is a political discourse analysis of the inaugural speeches of political leaders- Presidents and Prime Ministers of four countries selected from three continents across the world. The selected countries are Nigeria, Liberia, United States of America, and United Kingdom, and the selected speeches are that of Presidents Olusegun Obasanjo and Muhammadu Buhari of Nigeria, Presidents Ellen Johnson Sirleaf and George Weah of Liberia, Presidents Barack Obama and Donald Trump of the United States of America, and Prime Ministers David Cameron and Theresa May of the United Kingdom. The study is a qualitative and quantitative survey text analytical research. It utilizes inaugural speeches as primary data and literature in the field of political discourse as secondary data. Meaning was analyzed using Fairclough’s (2010) CDA approach as well as Halliday’s Systemic Functional Grammar. Furthermore, analysis was done in the three dimensions of Description (text analysis), Interpretation (processing analysis), and Explanation (social analysis). Research findings showed that the speeches communicated the messages of the leaders based on their sociocultural and sociopolitical reality. It however also reveals some general features of political discourse which cut across cultures, countries and continents. Although there were trends that were peculiar to each country, there were more features such as, context, personality, gender, state of the nation, etc. that served to individually distinguish speakers. In conclusion, the research submits that the combination of different approaches to language analysis facilitated a wholesome interpretation of the considered speeches, including the discourse and sociocultural practices. In addition, context is of immense importance when analyzing content

A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Prompt engineering is a technique that involves augmenting a large pre-trained model with task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be created manually as natural language instructions or generated automatically as either natural language instructions or vector representations. Prompt engineering enables the ability to perform predictions based solely on prompts without updating model parameters, and the easier application of large pre-trained models in real-world tasks. In past years, Prompt engineering has been well-studied in natural language processing. Recently, it has also been intensively studied in vision-language modeling. However, there is currently a lack of a systematic overview of prompt engineering on pre-trained vision-language models. This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e.g. Flamingo), image-text matching models (e.g. CLIP), and text-to-image generation models (e.g. Stable Diffusion). For each type of model, a brief model summary, prompting methods, prompting-based applications, and the corresponding responsibility and integrity issues are summarized and discussed. Furthermore, the commonalities and differences between prompting on vision-language models, language models, and vision models are also discussed. The challenges, future directions, and research opportunities are summarized to foster future research on this topic

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

How data governance frameworks can leverage data-driven decision making: a sustainable approach for data governance in organizations

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceWith the technological advances, organizations have experienced an increasing volume and variety of data, as well as the need to explore it to stay competitive. Data governance importance emerges to support the data flow, to record and manage knowledge derived from data, as well as establishing roles, accountabilities, and strategies, which further results in better decision-making. Through the definition of strategies to manage data in a consistent manner, data governance establishes the path to an enterprise-wide standardization, providing unchallenging access, management, and analysis of data to derive useful insights. Research on data governance frameworks is limited and lacks a key perspective: how can firms ensure sustainability on their programs. Data governance programs can only be continuously valuable if supported by a holistic framework focused on sustainability. To understand this gap, five frameworks are presented, analyzed and evaluated according to an assessment matrix based on eleven critical success factors for data governance. As a result of this study, where we offer a more comprehensive assessment tool, both researchers and practitioners can understand the maturity level of each critical success factor in the reviewed frameworks and identify which areas need further exploration and how to accomplish higher data governance maturity levels

Development of a Cloud Platform for Business Process Administration, Modeling, and Execution

Current business process management systems (BPMS) are laid out for large enterprises with business process management (BPM) expertise. Hence, there is a lack of tailored BPMS for small and medium-sized enterprises (SMEs) targeting at users with hardly BPM expertise. Clavii BPM cloud is a compact solution for web-based business process administration, modeling, and execution. Therefore, Clavii BPM cloud offers features to easily manage and share process models, as well as features for collaborative process modeling and execution. Moreover, Clavii BPM cloud has a unique feature set, which includes process views to reduce process model complexity and an easily extendable object-oriented data model. It also provides unique capabilities for process visualization with different notations like business process model and notation (BPMN) and a newly developed Transit Map. Created process models can be executed directly in the cloud as part of the seamlessly integrated modeling and execution environment