Digital Library of Gesellschaft für Informatik e.V.
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Interact360: A new Paradigm for Interacting with a Future Smart City
No full textAs more and more cities worldwide move towards a smart city, the information available to residents grows. Consequently, novel approaches are required to interact with a smart city. In this work, we demonstrate the Interact360 framework, which is the foundation for mobile platforms to interact with a smart city through gestures and Augmented Reality (AR). In our vision, the user of such Apps can easily discover artifacts around the current location, based on context-aware filtering. Artifacts are located around the user, and thus a circular discover-view is proposed to visualize such artifacts. The user can then turn his device in the direction of different artifacts. Once the user turns his device in the direction of an artifact, the user receives haptic and acoustic feedback, using the speaker and the vibration motors of the Smartphone. After selecting an artifact, the user can raise the smartphone as though they are about to capture an image with the camera. Based on the artifact, different information is visualized in Augmented Reality with spatial relation to the artefacts. For example, in the case of a bus stop, the timetable is presented to the user. In addition, the user can perform context-based actions, such as purchasing a ticket. To accomplish this, the user can either swipe and tap on the display or employ basic gestures, such as gently rotating the device. These gestures are detected using the Inertial Measurement Unit (IMU) sensor of the smartphone in combination with on-device machine learning. To demonstrate feasibility, we used the Interact360 framework to create two applications, specifically targeting Android and iOS. To summarize, the Interact360 framework facilitates the development of innovative human interaction interfaces designed for context-aware and user-friendly applications in Smart city environments
Streaming Process Mining over Realistic Event Streams
No full textStreaming process mining analyzes infinite event streams for real-time business process insights. Current streaming approaches for process mining adapt classical techniques and in this way neglect stream characteristics. This paper discusses how to shift the foundation for streaming approaches for process mining appropriately. In particular, we categorize related approaches, identify research gaps, and introduce the streaming process mining challenge to advance algorithm evaluation under realistic conditions
WiMoVE: An Architecture for Large Wi-Fi Systems
No full textIn large Wi-Fi systems, broadcast traffic can become a problem, taking up airtime without providing benefits for receiving stations. Countermeasures like traffic filtering achieve acceptable performance, but commonly break end-user functionality. To improve on this, we identify design trade-offs and propose a new architecture called WiMoVE. By using many small Layer 2 (L2) overlay networks, WiMoVE reduces broadcast transmissions while providing end users with unfiltered L2 domains. With a prototype implementation, we show that our approach is technologically feasible
Processes, Methods, and Tools in Model-based Engineering -- A Qualitative Multiple-Case Study
No full textThis extended abstract summarizes our paper with the same title published in the Journal of Systems and Software (JSS
Practical Problems in Customer Data — A Use-Case-Driven Classification
No full textThis article presents a comprehensive analysis of data quality issues encountered in customer data at large enterprises. This analysis is based on data collected at a large medical technology manufacturer, and the problems observed there are clustered into distinct classes. Through this classification, nine key prevention requirements can be identified which are essential for improving data fitness. These include changes to data governance and to data architecture, among others. An evaluation of existing tools against these requirements furthermore highlights notable solutions. Despite the availability of numerous tools, gaps remain, especially regarding integration of all functionalities. Our findings suggest that while industry-standard solutions are accessible, integrating them into a cohesive framework posed significant challenges in our use case, necessitating continual adjustments to data architecture and processes to enable and maintain high quality of data
Scalable Data Management using GPUs with Fast Interconnects
No full textModern database management systems (DBMSs) are tasked with analyzing terabytes of data, employing a rich set of relational and machine learning operators. To process data at large scales, research efforts have strived to leverage the high computational throughput and memory bandwidth of specialized co-processors such as graphics processing units (GPUs). However, scaling data management on GPUs is challenging because (1) the on-board memory of GPUs has too little capacity for storing large data volumes, while (2) the interconnect bandwidth is not sufficient for ad hoc transfers from main memory. Thus, data management on GPUs is limited by a data transfer bottleneck. In practice, CPUs process large-scale data faster than GPUs, reducing the utility of GPUs for DBMSs. In this thesis, we investigate how a new class of fast interconnects can address the data transfer bottleneck and scale GPU-enabled data management. Fast interconnects link GPU co-processors to a CPU with high bandwidth and cache-coherence. We apply our insights to process stateful and iterative algorithms out-of-core by the examples of a hash join and k-means clustering. We first analyze the hardware properties. Our experiments show that the high interconnect bandwidth enables the GPU to efficiently process large data sets stored in main memory. Furthermore, cache-coherence facilitates new DBMS designs that tightly integrate CPU and GPU via shared data structures and pageable memory allocations. However, irregular accesses from the GPU to main memory are not efficient. Thus, the operator state of, e.g., joins does not scale beyond the GPU memory capacity. We scale joins to a large state by contributing our new Triton join algorithm. Our main insight is that fast interconnects enable GPUs to efficiently spill the join state by partitioning data out-of-core. Thus, our Triton join breaks through the GPU memory capacity limit and increases throughput by up to 2.5× compared to a radix-partitioned join on the CPU. We scale k-means to large data sets by eliminating two key sources of overhead. In existing strategies, execution crosses from the GPU to the CPU on each iteration, which results in the cross-processing and multi-pass problems. In contrast, our solution requires only a single data pass per iteration and speeds-up throughput by up to 20×. Overall, GPU-enabled DBMSs are able to overcome the data transfer bottleneck by employing new out-of-core algorithms that take advantage of fast interconnects
Interview with Tobias Lindner on the Relationship Between Academia and Politics: Digital Transformation, Participation, and Mitigating Disinformation
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Message from the SE'25 Workshop Chairs
No full textThis volume comprises the proceedings of the workshops held at the 2025 Software Engineering conference (SE’25), the premier conference for software engineering in German-speaking countries. Organized annually by the Gesellschaft für Informatik (GI), the SE serves as a platform for the exchange of experiences, ideas, and insights between practitioners and academics in the field of software engineering. The workshops, conducted on 24th and 25th of February 2025, provided participants with the opportunity to engage deeply with specialized topics, foster collaboration, and explore emerging trends and challenges in the discipline. The SE’25 was hosted by the Karlsruhe Institute of Technology (KIT), Germany