Project management incorporates a wide range of different tools for the initiation and processing of different projects. A suitable project management approach is essential for a sustainable and innovative management and is therefore often tailored to the requirements of the individual project and its environment. In research and practice, multiple approaches for tailoring exist. This paper presents an in-depth survey and its results concerning the tailoring of project management methodologies on the basis of data.

Project management tailoring is fundamental to the success of development projects, but requires a deep understanding of the complex factors that influence a project and its management. This paper presents INFACTS, a comprehensive structuring model that contains project-, organization-, product- and person-oriented influencing factors that have an impact on the selection and tailoring of project management. INFACTS integrates a theoretical knowledge base with a practical decision-making aid for project management tailoring. By combining literature analysis, terminology work, and qualitative content analysis, it translates complex project situations into a structured visual framework. Each influencing factor was assigned five qualitative or quantitative characteristics, enabling a clear positioning between the spectrum of plan-based and agile project management. A real-world R&D project case study demonstrated the applicability and value of INFACTS. It showed that early context analysis increases transparency and can lead to more informed decisions during tailoring, thus proactively preventing misalignments in the project management approach. INFACTS can be used independently of a specific project management method, closes a key research gap and lays the foundation for further research in the field of modern project management.

Very few available individual bandwidth reservation schemes provide efficient and cost-effective bandwidth reservation that is required for safety-critical and time-sensitive vehicular networked applications. These schemes allow vehicles to make reservation requests for the required resources. Accordingly, a Mobile Network Operator (MNO) can allocate and guarantee bandwidth resources based on these requests. However, due to uncertainty in future reservation time and bandwidth costs, the design of an optimized reservation strategy is challenging. In this article, we propose a novel multi-objective bandwidth reservation update approach with an optimal strategy based on Double Deep Q-Network (DDQN). The key design objectives are to minimize the reservation cost with multiple MNOs and to ensure reliable resource provisioning in uncertain situations by solving scenarios such as underbooked and overbooked reservations along the driving path. The enhancements and advantages of our proposed strategy have been demonstrated through extensive experimental results when compared to other methods like greedy update or other deep reinforcement learning approaches. Our strategy demonstrates a 40

Choosing the right project management method is essential for the successful implementation of a project. At the same time, it is a difficult decision, as a large number of standards, frameworks, and process models have been developed and established over the years, ranging from plan-based to agile versions. That makes it all the more important to know the characteristics of those models in detail. This paper fundamentally analyzes existing project management models with regard to their essential elements. Elements are the building blocks that make up each individual model. Each element has its own mission with regard to structure-, process-, and function-oriented tasks in a model, and therefore also in the project. Through the qualitative content analysis of the documentation and guidelines for the models, strengths and weaknesses within a wide range of 23 different project management models could be uncovered. Furthermore, a helpful comparison could be made between the models on aspects such as temporal structure, roles, quality assurance, communication, and collaboration. This analysis and the knowledge of the individual advantages and disadvantages of the models form the basis for making targeted adjustments to project management models in future.

This paper presents the evaluation of a cross-university certification system for students in project management. For this purpose, the certification system is briefly described and the evaluation by means of an online survey is explained. The results show that most of the participants of the certification were satisfied with the organization and the structure, the level of difficulty and the contents of the certification. The survey also showed that companies value the certificate when certificate holders apply for jobs. From the results, opportunities for improvement can be derived, such as increasing awareness and providing more information upfront, which can be implemented in the future.

Onsite bandwidth reservation requests often face challenges such as price fluctuations and fairness issues due to unpredictable bandwidth availability and stringent latency requirements. Requesting bandwidth in advance can mitigate the impact of these fluctuations and ensure timely access to critical resources. In a multi-Mobile Network Operator (MNO) environment, vehicles need to select cost-effective and reliable resources for their safety-critical applications. This research aims to minimize resource costs by finding the best price among multiple MNOs. It formulates multi-operator scenarios as a Markov Decision Process (MDP), utilizing a Deep Reinforcement Learning (DRL) algorithm, specifically Dueling Deep Q-Learning. For efficient and stable learning, we propose a novel area-wise approach and an adaptive MDP synthetic close to the real environment. The Temporal Fusion Transformer (TFT) is used to handle time-dependent data and model training. Furthermore, the research leverages Amazon spot price data and adopts a multi-phase training approach, involving initial training on synthetic data, followed by real-world data. These phases enable the DRL agent to make informed decisions using insights from historical data and real-time observations. The results show that our model leads to significant cost reductions, up to 40%, compared to scenarios without a policy model in such a complex environment.

Resource trading between vehicles, which involves the buying and selling of computing and bandwidth resources, is a promising approach for cost-effectively provisioning services in safety-critical applications such as autonomous driving. These applications require a guarantee and the timely receipt of resources through efficient advance reservations. However, due to uncertainties in future reservation duration and resource costs, vehicles exhibit two distinct patterns: some may have reserved insufficient resources and need to purchase more (acting as vehicle requesters), while others may have overbooked resources and need to sell (acting as vehicle providers). In this paper, we formulate the resource trading problem from both the requester and provider perspectives and propose a resource trading architecture to optimize bandwidth reservation. It utilizes blockchain smart contracts for secure and efficient resource exchange within a mobile network operator (MNO) environment. Two algorithms are introduced: a provider selection algorithm to enhance system efficiency by selecting cost-effective providers, and a decision-making algorithm to assist providers in choosing between selling overbooked bandwidth or canceling it. Through simulations, the results show that these algorithms lead to significant cost reductions for requesters and profit gains for providers, up to 59% and 19%, respectively, compared to reservation schemes without resource trading in such a dynamic environment.

Edge computing resources are crucial for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require guaranteed resource availability for real-time communication and computation to ensure safety and efficiency. With the emergence of integrated sensing and communication (ISAC) in 6G communication systems, extensive sensing data will be added to the communication data. To meet these challenges, resource reservation becomes a necessity. Resource reservation approaches can be either network-side or vehicle-side. Most existing approaches are network-side. However, these approaches are insufficient for these applications because they only guarantee resources to individual vehicles with a certain probability. Vehicle-side reservation is challenging because vehicles usually do not have sufficient information to reason about future available resources and their costs. This comprehensive survey aims to define the characteristics of TSSCV applications and to analyze existing reservation approaches and cost-effective schemes, considering different reservation scenarios in single and multiple mobile network operator (MNO) environments. In addition, it explores strategies to revise or update reservations when faced with uncertain reservation times and costs. To this end, we propose to enable resource trading and reservation exchange facilitated by blockchain smart contracts. This approach promises a secure and efficient method for resource management in future smart vehicles.

With the advancement of big data, the scope and potential of Artificial Intelligence (AI) have acquired major momentum. Data-Centric Artificial Intelligence (DCAI) is one of the most emergent fields of study in the current era of digitalization. Many examples have proven the effectiveness of Machine- and Deep Learning methods. In industrial production, however, limitations are still present that hinder application online and in a series that goes beyond isolated use cases. One crucial issue is data precondition, i.e., data quality, consistency, and labeling. As DCAI addresses these issues, developments in this field have caught the attention of various experts. In summary, DCAI continues to be an exciting and promising field of study that enhances AI applicability. Several research works have been conducted in DCAI, but unfortunately, no comprehensive reviews have been conducted to summarize and highlight the results. This gap in knowledge inspired our work, that aims to answer well-structured research questions. The focus of this paper is to clarify the terminology used in DCAI while also distinguishing it from other AI-related problems. This helps to analyze the current standards and problems associated with DCAI. This paper summarizes current use cases of DCAI and their impact on industries. Through this detailed description, readers can understand the potential and benefits of using DCAI in different business sectors. The analysis of the latest methods employed by DCAI to achieve enhanced AI performance and outcomes provides valuable insights for professionals and organizations that strive to incorporate AI into their business.

A well-balanced project design using appropriate process models is an important success factor for the management of engineering development projects. There exists a wide range of different process models, including traditional waterfall-based models as well as agile development models like Scrum. Such standardized process models offer a good starting point for the management of engineering development projects; however, they have to be tailored in order to meet the needs of a specific project. Additionally, in complex environments, it might be necessary to construct dedicated process models, like hybrid combinations of several models. In this article, the groundwork for a systematic approach to the construction and tailoring of process models for engineering development projects is presented. It considers relevant international standards as well as traditional and agile process models and can serve as a basis for the subsequent tailoring processes. The developed framework offers the possibility to add distinctive characteristics to a process model, like means which facilitate quality, communication, or knowledge transfer. The evaluation of the framework was done by means of a case study, a survey, and expert interviews.

Time-sensitive and safety-critical networked vehicular applications, such as autonomous driving, require deterministic guaranteed resources. This is achieved through advanced individual bandwidth reservations. The efficient timing of a vehicle decision to place a cost-efficient reservation request is crucial, as vehicles typically lack sufficient information about future bandwidth resource availability and costs. Predicting bandwidth costs often using time-series machine learning models like Long Short-Term Memory (LSTM). However, standard LSTM models typically require longer durations of multiple input data sets to achieve high accuracy. In certain scenarios, quick decisions must be made, even if the vehicle means sacrificing some accuracy. We propose a batched LSTM model to assist vehicles in placing bandwidth reservation requests within a limited data for an upcoming driving path. The model divides data during training to enhance computational efficiency and model performance. We validated our model using historical Amazon price data, providing a real-world scenario for experiment. The results demonstrate that the batched LSTM model not only achieves higher accuracy within a short input data duration but also significantly reduces bandwidth costs by up to 27% compared to traditional time-series machine learning models.

Protecting children from traffic hazards near schools is of essential importance. Emergent technologies present new possibilities for monitoring traffic more efficiently and a dequately warning road users. This paper presents a new approach, that integrates well-selected technologies to increase road safety for schoolchildren on their way to school. It delves into how utilizing multimodal sensing, 5G mobile communications infrastructure, Artificial Intelligence and Edge Computing can achieve timely situation awareness to prevent traffic h azards near schools. Five use cases are outlined to enhance school route safety, derive critical requirements for their solution, and reveal potential challenges.

This paper deals with master data from the project management environment. A general approach for predicting project outcome including goal achievement is developed. A first implementation in RapidMiner Studio V9.10 based on a decision tree algorithm is derived and discussed. The purpose of this paper is to provide a basic understanding of the topic and to adapt the theory to existing enterprise data. The potential of predictive analytics for project management is illustrated and a promising practical implementation with existing software is derived and demonstrated.

The use of blockchain technology in project management has not yet been widely explored. Nevertheless, a systematic literature search was conducted. This has shown that there are already initial use cases in which the advantages of this technology can be exploited. In the research results to date, blockchain has been used predominantly in the area of information and document management. However, individual publications have also described its use in task and contract management. This paper recommends to combine these three use cases to a blockchain-based task planning and management system for project management. Therefore a concept for implementing this approach is modeled. For this purpose, various decision criteria were used to investigate on which blockchain platforms these use cases can be applied. Based on the literature research, the selection could already be narrowed down to four platforms. In the end, the EOSIO platform was the best fit for this use case.

There is a strong desire from governments worldwide to digitalize their processes and to provide them for their citizens in an online format. Especially in the last view years, the SARS-COV-2 pandemic has amplified this desire, as during lock downs people were unable to visit their local government building. While there is a strong desire, some countries have struggled to provide online services to their citizens. Especially on the regional and local community level, the German government structures are just starting out to implement these processes. As these processes tend to become quite complex, this paper proposes to use adaptive process models in order make the process more clear for the ordinary citizen. To this case a short prototype on how an adaptive process model can be realized for applying for a building permit. This prototype uses a set of parameters to modify BPMN models in order to extract variants. The prototype is realized with the ADAMO Modeling tool.

Teaching and research have been the core activities of universities since ages. Nowadays, the topic of transfer is increasingly coming into focus, as universities are no longer seen as “ivory towers” in which research is cut off from the rest of the society, but rather as establishments with a profound knowledge and technology transfer. However, transfer often still occurs in an uncoordinated manner without distinct processes or coordination. A digital transformation of transfer activities could support and make universities fit for the future of transfer. Therefore, this paper does not only propose a framework for digital transformation of transfer, but also points out the importance of platforms and collaboration of universities.

Project management offers a broad set of tools for the definition, planning, execution and completion of very different projects. There are various more or less generic standards for this toolkit of project management-related roles, processes and methods. These need to be tailored to the specific needs of projects and their respective contexts. This paper provides a broad overview of tailoring approaches in relevant standards and other literature. The systematic literature search revealed very different concepts such as process-oriented or structure-oriented approaches. To provide orientation, the different approaches are categorized and described by way of examples. The results have the potential to help practitioners select an appropriate tailoring approach and to further improve the overall tailoring process in more comprehensive tailoring systems.