Digital Library of Gesellschaft für Informatik e.V.
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Der Lehrstuhl Data Engineering an der Universität Regensburg
No full textDie Data Engineering Gruppe an der Universität Regensburg wird seit April 2022 von Prof. Meike Klettke geleitet. Sie und ihr Team bestehend aus Dr. Tanja Auge (PostDoc), Dominique Hausler (wiss. Mitarbeiterin), Dominik Metz (Systemadministrator), Marion Königseder (Sekretariat), der externen Habilitandin Dr. Sylvia Melzer und den externen Promovend:innen Jennifer Landes, Maximilian Plazotta und Mark Lukas Möller arbeiten an Forschungsthemen zu Datenbank-Evolution für verschiedene Datenmodelle, der Ableitung von Informationen aus Daten sowie Verfahren für Data-Engineering-Pipelines, wirken beim Aufbau der Studiengänge mit und führen die Lehre für verschiedene Datenbank- und Programmierfächer durch
Towards future of work in immersive environments and its impact on the Quality of Working Life: a scoping review
No full textIn recent years, immersive environments and the technologies employed within them, such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), have become increasingly significant, particularly in the domains of education, work, and entertainment. Moreover, the concept of persistent, immersive virtual worlds – commonly referred to as Metaverses – has gained attention due to advancements and opportunities in VR and AR. These immersive environments are transforming how we work, especially in communication, coordination, and collaboration. Hence, an important question that arises is how these environments influence Quality of Working Life (QWL). This study provides an overview of the effects of immersive environments on QWL. We conducted a scoping review following the framework by Arksey and O’Malley in accordance with the PRISMA guidelines. The findings identify three major QWL topics influenced by immersive environments: (i) Mental Health, highlighting stress reduction and well-being enhancement; (ii) Safety & Prevention, emphasizing accident prevention and risk mitigation; and (iii) Workplace Design, focusing on improved ergonomics. We derive practical implications for QWL and provide theoretical implications to scoping reviews. While our study considered the short-term effects of such technologies as limitations, future studies should address the long-term effects of immersive environments on QWL
Rezension zu „Think Faster, Talk Smarter: How to Speak Successfully When You’re Put on the Spot“ von Matt Abrahams
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Building a Data Management System for the Cloud: Lessons Learned and Future Directions
No full textThe paper discusses the lessons learned from building Snowflake, a data management system for the cloud. Given the need for systems that can scale to handle large data volumes, provide expressive programming interfaces, and leverage the benefits of cloud computing, it describes the architecture of a cloud-based data management system and optimization techniques specific to the cloud. Key techniques include pruning large file sets at both compile time and query runtime, optimizing data layouts in the background, and, more generally, the importance of performing maintenance tasks in the background, which is enabled by cloud resources. The paper also explains the need for using immutable files and the implications for data modification queries. Finally, it highlights the operational aspects of building and maintaining a data management system that functions as an online cloud service. The paper concludes by outlining future directions for cloud-based data management systems
Not Quite There Yet: Remaining Challenges in Systems and Software Product Line Engineering as Perceived by Industry Practitioners
No full textThis is a summary of a paper (with the same title) originally published at the 28th ACM International Systems and Software Product Line Conference (SPLC) in 2024 discussing remaining challenges in systems and software product line engineering as perceived by practitioners
UDP and Shared Memory Comparison Performance Comparison Analysis of Data-Exchange Efficiency in Middleware Systems with UDP and Shared Memory
No full textThis work aims at creating a systematic comparison of the data transmission throughput using UDP or Shared Memory. For testing, a middleware as well as a communication test tool were used that transferred data packets at a given frequency. By controlling the frequency and the size of the data packet a maximum possible data throughput was deducted. This was also tested on different hardware architectures to resamble different use case scenarios. The initial hypothesis that Shared Memory is way more performant than UDP will be underlined by this work, although UDP shows to have application areas where the performance differences are minute. Since there is no empirical comparison of these two methods this research aims to fill that gap
Mehr als Nostalgie
No full textVerbotene Streamingplattformen, Investigativ-Journalismus, Einblicke in Ermittlungen und Fragen zu Moral und Urheberrecht: Ein neuer ARD-Podcast hat einiges zu bieten – nicht nur für Fans der Internet-Nostalgie
History-Based Active Learning
No full textIn this demo, we will show how Active Learning (AL) can be used to establish and transfer classification information over partially/loosely related datasets, in particular fine-grained user roles on social media, with many and unbalanced classes, large number of data points as well as different internal structures or drifts over time. The key idea is to incorporate the history of learning steps into the tool, allowing us to analyze, restart, and modify the transfer. We also provide a rich visualization that allows the human oracle to interpret the most critical cases
Lightweight Memory Access Monitoring for Dynamic Data Placement in Tiered Memory Systems
No full textWith a growing number of memory technologies (e.g., PMEM, HBM, CXL memory), hardware characteristics and data access patterns on all available memory tiers must be monitored to make effective data placement decisions. Thus, memory management for optimal data placement across memory tiers becomes more CPU-and resource-demanding. Data placement can be either OS-controlled, e.g., through transparent virtual memory page promotion & demotion across memory tiers; or application-controlled, e.g., through explicit data allocation to specific memory tiers. However, OS-controlled data placement lacks awareness of workload resources, and application-controlled data placement results in additional overhead from managing access metrics and cost models. To facilitate dynamic data placement in tiered memory systems, modern OSs already provide built-in support for lightweight access monitoring to specific memory regions (e.g., through the Data Access MONitoring (DAMON) framework of the Linux Kernel). In this work, we investigate the applicability of the memory access monitoring framework DAMON in the context of database systems in tiered memory systems. To assess the applicability of DAMON as a core building block for data placement, we integrate its monitoring mechanism and memory management into Poseidon, a high-performance graph database system. In our experimental evaluation, we analyze the accuracy & overhead of DAMON through a set of microbenchmarks and by running end-to-end queries from a graph database benchmark against our DAMON-enabled Poseidon DBMS. Our initial results demonstrate the potential of DAMON as a core building block for workload-driven, dynamic data placement in tiered memory systems. Our experiments show a 3% overhead in execution time while achieving an accuracy of over 90% compared to actual access numbers