Institute for Enterprise Systems

Investigating the cost-effectiveness of future mobile stroke unit (MSU) services with respect to local idiosyncrasies is essential for enabling large-scale implementation of MSU services. The aim of this study was to assess the cost-effectiveness for varying urban German settings and modes of operation. Costs of different operating times together with different personnel configurations were simulated. Different possible catchment zones, ischemic stroke incidence, circadian distribution, rates of alternative diagnoses, as well as missed cases were incorporated to model case coverage and patient numbers. Based on internationally reported clinical outcomes of MSUs, a 5-year Markov model was applied to analyze the cost-effectiveness for the different program setups. Compared with standard stroke care, MSUs achieved an additional 0.06 quality-adjusted life years (QALYs) over a 5-year time horizon. Assuming a catchment zone of 750,000 inhabitants and 8 h/7 day operation resulted in an incremental cost-effectiveness ratio (ICER) of €37,182 per QALY from a societal perspective and €45,104 per QALY from a healthcare perspective. Lower ICERs were possible when coverage was expanded to 16 h service on 7 days per week and larger populations. Sensitivity analyses revealed that missing ischemic strokes significantly deteriorated economic performance of MSU. Major determinants of cost-effectiveness should be addressed when setting up novel MSU programs. Catchment zones of more than 500,000-700,000 inhabitants and operating times of at least 12-16 h per day, 7 days per week could enable the most cost-effective MSU services in the German healthcare system.

Requirements Elicitation (RE) is a crucial activity especially in the early stages of software development. GUI prototyping has widely been adopted as one of the most effective RE techniques for user-facing software systems. However, GUI prototyping requires (i) the availability of experienced requirements analysts, (ii) typically necessitates conducting multiple joint sessions with customers and (iii) creates considerable manual effort. In this work, we propose SERGUI, a novel approach enabling the Self-Elicitation of Requirements (SER) based on an automated GUI prototyping assistant. SERGUI exploits the vast prototyping knowledge embodied in a large-scale GUI repository through Natural Language Requirements (NLR) based GUI retrieval and facilitates fast feedback through GUI prototypes. The GUI retrieval approach is closely integrated with a Large Language Model (LLM) driving the prompting-based recommendation of GUI features for the current GUI prototyping context and thus stimulating the elicitation of additional requirements. We envision SERGUI to be employed in the initial RE phase, creating an initial GUI prototype specification to be used by the analyst as a means for communicating the requirements. To measure the effectiveness of our approach, we conducted a preliminary evaluation. Video presentation of SERGUI at: https://youtu.be/pzAAB9Uht80

The energy system in Germany consists of a large number of distributed facilities, including millions of PV plants, wind turbines, and biomass plants. To understand and manage this system efficiently, accurate and reliable information about all facilities is essential. In Germany, the Marktstammdatenregister (MaStR) serves as a central registry for units of the energy system. The reliability of this data is critical for the registry's usefulness, but few validation studies have been published. In this work we provide a review of existing literature that relies on data from the MaStR and thereby show the registry's importance. We then build a data and testing pipeline for relevant data of the registry, with a focus on the two aspects of facility's location and size. All test results are published online in a reproducible workflow. Hence, this work contributes to a reliable data foundation for the German energy system and starts an open validation process of the Marktstammdatenregister from an academic perspective.

We address the following mechanism design problem: Given a multi-player Normal-Form Game (NFG) with a continuous action space, find a non-discriminatory (i.e., identical for all players) restriction of the action space which maximizes the resulting Nash Equilibrium with respect to a fixed social utility function. First, we propose a formal model of a Restricted Game and the corresponding restriction optimization problem. We then present an algorithm to find optimal non-discriminatory restrictions under some assumptions. Our experimental results with Braess' Paradox and the Cournot Game show that this method leads to an optimized social utility of the Nash Equilibria, even when the assumptions are not guaranteed to hold. Finally, we outline a generalization of our approach to the much wider scope of Stochastic Games.

Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence. On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable. Exchangeability, which arises naturally in relational domains, has not been considered for efficient representation and inference in SPNs yet. The contribution of this paper is a novel probabilistic model which we call Exchangeability-Aware Sum-Product Networks (XSPNs). It contains both SPNs and MEVMs as special cases, and combines the ability of SPNs to efficiently learn deep probabilistic models with the ability of MEVMs to efficiently handle exchangeable random variables. We introduce a structure learning algorithm for XSPNs and empirically show that they can be more accurate than conventional SPNs when the data contains repeated, interchangeable parts.

Abstract Competitive Multi-Agent Systems (MAS) are inherently hard to control due to agent autonomy and strategic behavior, which is particularly problematic when there are system-level objectives to be achieved or specific environmental states to be avoided.Existing solutions for this task mostly assume specific knowledge about agent preferences, utilities and strategies, neglecting the fact that actions are not always directly linked to genuine agent preferences, but can also reflect anticipated competitor behavior, be a concession to a superior adversary or simply be intended to mislead other agents. This assumption both reduces applicability to real-world systems and opens room for manipulation.We therefore propose a new governance approach for competitive MAS which relies exclusively on publicly observable actions and transitions, and uses the acquired knowledge to purposefully restrict action spaces, thereby achieving the system’s objectives while preserving a high level of autonomy for the agents.KeywordsMulti-agent systemCompetitionStochastic gameGovernanceRestriction

Abstract We study the use of financial contracts as bid‐coordinating device in multi‐unit uniform price auctions. Coordination is required whenever firms face a volunteer's dilemma in pricing strategies: one firm (the “volunteer") is needed to increase the market clearing price. Volunteering, however, is costly, as inframarginal suppliers sell their entire capacity whereas the volunteer only sells residual demand. We identify conditions under which signing financial contracts solves this dilemma. We test our framework exploiting data on contract positions by large producers in the New York power market. Using a Monte Carlo simulation, we show that the contracting strategy is payoff dominant and provide estimates of the benefits of such strategy.

In this paper, a new application domain for mobility predictions is presented. Based on the application domain new challenges arise in terms of when and how the mobility prediction has to be done. This results in three cases for mobility predictions, i.e. predicting some time beforehand, predicting before departure, and predicting when departed. Several approaches are introduced to tackle the three cases, which are evaluated empirically on the GeoLife dataset.

Vertical database partitioning and the distribution of the partitions to different clouds improves the level of security and privacy, as the respective partitions from one cloud are worthless without the others. SeDiCo (a framework for a secure and distributed cloud data store) is an implementation of such an approach. However, the data partitioning and distribution approach demands a powerful client with sufficient hardware capabilities to efficiently join the partitioned data. Mobile devices have limited hardware resources in terms of memory, processing power, and storage capacity. The aim of this paper is to adopt the SeDiCo approach to mobile devices and to prove its feasibility. For this purpose, an Android application that implements the vertical partitioning approach is conceptualized and developed. Furthermore, the implementation is evaluated with the TPC-W benchmark. As a result, mobile devices should be enabled to use a secure and distributed cloud storage to foster a broader dissemination of Cloud Computing for mobile devices. Furthermore, this approach fosters bring-your-own-device (BYOD) strategies for enterprises, as the level of data security and privacy is improved.