Database systems are at the core of modern applications, providing essential services for storing, querying, and managing data. However, they do not remain static; they continuously evolve in response to external changes. Two primary external factors drive this evolution. On the one hand, database vendors release new versions of the database software, introducing new features, bug fixes, and security patches, resulting in the ongoing evolution of the database binary. On the other hand, developers modify the database schema, which defines the structure of the stored data, to accommodate new data structures required by client applications. This cumulative dissertation investigates the challenges of binary evolution and schema evolution in database systems. These two types of evolution represent disruptive changes at both ends of the stack of a database system from the perspective of externally driven changes. We examine various families of database systems, including relational, NoSQL document and key-value stores, as well as distributed variants. The state-of-the-art approach to address the binary evolution of a database system involves a restart: The database system is shut down, the binary replaced, and restarted. While this approach may be acceptable for full feature upgrades, it poses significant overhead when fixing bugs or patching vulnerabilities. Each restart leads to service interruptions and the loss of the in-memory state. Moreover, such downtimes are particularly problematic for cloud providers offering managed Database-as-a-Service. They cannot predict when a downtime is acceptable for customers, making it challenging to promptly patch critical vulnerabilities without disrupting service. To overcome these issues, we investigate live patching as a radically novel approach to binary evolution. Live patching allows code changes to be applied in-memory to the running application without necessitating a restart. We provide the first systematic evaluation of live patching for database systems. Using the relational database management system MariaDB as a case study, our comprehensive evaluation of single-instance, multi-threaded database systems demonstrates that live patching is a viable alternative to conventional restarts. We examine the interplay between various query workloads and live patching methods, analyzing their impact on transaction throughput and tail latencies. Additionally, we explore how the size of the in-memory state impacts live patching, using the in-memory key-value store Redis, and evaluate real-world patches applied to both MariaDB and Redis. Our findings provide insights into database-specific factors that influence live patching. These insights enable informed decisions about the latency overhead clients may experience during live patching. While single-instance database systems suffer complete service outages during restarts, database clusters are designed to mitigate such disruptions. When a node is taken offline, its workload can be redistributed to other nodes. The state-of-the-art approach to patch a database cluster is a rolling update, where each node is sequentially shut down, patched, and restarted, with other nodes taking over the workload of the node undergoing maintenance. Ideally, this patching process should (1) incur no downtime, (2) avoid noticeable performance degradations such as latency spikes, (3) maintain stable resource consumption, and (4) be executed promptly. Unfortunately, conventional rolling updates often fail to meet these goals. To address these limitations, we extend live patching to database clusters, demonstrating its feasibility and applicability using the distributed in-memory key-value store Redis Cluster as a case study. In a systematic evaluation, we demonstrate that our live patching approach satisfies all four aforementioned requirements, whereas conventional rolling updates do not. To enable seamless live patching, we develop and evaluate two patch distribution mechanisms. Both automate patch distribution across the cluster, with one prioritizing distribution speed and the other designed to accommodate the elasticity of the cluster. Finally, we offer general guidelines for integrating live patching and automatic patch distribution into distributed database systems, demonstrating their successful application using a primary-replica setup of the relational database management system PostgreSQL. To provide a holistic change management approach for database systems, it is crucial to address not only binary evolution but also the evolution of the database schema. The database schema defines the structure of stored data and serves as a contract between the database system and the applications that interact with it. When it evolves, it raises a critical question: Are the schema changes backward compatible, or do they introduce breaking changes for client applications? Unlike relational database management systems, NoSQL database systems do not follow a unified schema standard; instead, schemas are tailored to the specific data format being stored. For semi-structured data, JavaScript Object Notation (JSON) is widely used, with JSON Schema serving as the de-facto schema language for JSON data. To address the challenge of schema evolution for JSON Schema-based database schemas, we explore the concept of JSON Schema containment. JSON Schema containment can be used to decide whether the language defined by one JSON Schema is a subset of another. Our evaluation of existing JSON Schema containment checkers, applied to a diverse set of real-world schemas, reveals significant limitations. These tools often lack support for advanced JSON Schema features or yield inconsistent results, and therefore lack in consensus. Recognizing the absence of comprehensive schema management solutions for JSON-based NoSQL document stores that use JSON Schema for data validation, we propose a novel DevOps workflow implemented through our prototype tool, Josch. Josch is geared for MongoDB and integrates third-party JSON Schema extraction and containment checking solutions to assist developers and DevOps teams in efficiently managing schema changes. It enables the extraction of JSON Schemas from existing JSON documents when an explicit schema is absent and employs containment checking to ensure that schema changes remain backward compatible, thereby preventing breaking changes for client applications. In summary, our research provides a comprehensive approach to managing externally driven changes in database systems. By unifying our research on live patching for binary evolution with our solutions for schema evolution, we enable seamless updates to the database binary without disrupting services and protect client applications from breaking changes due to schema modifications.

The Industrial Revolution is a crucial development step in human history that started three centuries ago and is still ongoing. It continually influences and shapes the globalized world. Today, industries account for 20% of carbon dioxide emissions worldwide and require more than a third of global energy consumption. Current problems, such as climate change, increasing waste, and pollution, require simultaneous optimization across all industrial domains, infrastructure, and systems as they depend on each other. The global industry faces the immense challenges of providing for a surging world population expected to peak in the mid-2080s with 10.4 billion people, as reported by the United Nations. Hence, industries are expected to become less resource-intensive, sustainable, and more resilient to disrupted supply chains while producing for a growing population for the next decades. The Fourth Industrial Revolution, or Industry 4.0 (I4.0), started around 2010 and is still an ongoing transformation of industrial processes towards digitalization, creating smart factories referring to the digital data integration of the entire manufacturing cycle. I4.0 is incredibly information-intensive and requires immense data to simulate and predict essential operations based on a digital shadow of the factory, a so-called digital twin. The Metaverse is considered a digitalization megatrend merging digital and physical worlds, creating immersive experiences and new opportunities for interaction and innovation across various sectors and industries. The vision of the Metaverse promotes interconnected and interoperable real-time 3D virtual worlds that can be frictionlessly traversed while sustaining ownership of one's assets under a self-sovereign identity in a decentralized environment without platform lock-ins to a specific ecosystem. Therefore, the Metaverse creates an immersive parallel reality with collective virtually shared spaces for entertainment, social interactions, education, and a new working environment. The Industrial Metaverse synthesizes Metaverse concepts with current industrial automation, such as I4.0, to deepen the digital-physical convergence by interconnecting internal and external systems to enable decision-making and predictions based on significantly broader knowledge. An Industrial Metaverse factory is entirely mirrored to integrate digital twins of all types of equipment, assets, and other entities that can communicate vertically and horizontally, as well as the knowledge about relevant external systems and industrial core sectors. Through the comprehensive data integration of the Industrial Metaverse, AI-driven applications can predict future events, reducing system and hardware failures. Furthermore, the interconnected virtual environments create a meta-ecosystem for global collaboration, providing spaces for solving complex problems such as engineering and product design tasks, simulation of product twins, and reduced development time and costs. The connected industrial ecosystems create a token-based digital economy for exchanging data, assets, and services cross-metaverse connecting isolated data silos. Sharing digital twin resources and services with other systems enables new innovative applications and growing ecosystems. The theoretical part of this thesis defines the essential characteristics and key technologies of the Industrial Metaverse to derive a reference architecture for a decentralized system of systems, outlining the fundamental Industrial Metaverse building blocks. Interoperable data exchange, access management, and system communication are critical challenges. Especially interoperability of assets such as 3D files that come in different formats and identities must be ensured to move between virtual environments. The unique fusion of technologies leverages interconnected digital twins in the context of immersion, interaction, and collaboration for secure, autonomous-governed, decentralized industrial applications. Hence, the Industrial Metaverse requires the possibility of exchanging assets, products, and services across all systems in a secure manner. Distributed ledger technology enables tamper-proof transactions of assets and value in a decentralized token economy. Therefore, we investigate the feasibility of current tokenization methods for industrial assets, in particular, Printed Circuit Board (PCB) designs and 3D models. We contribute methods to create unique fingerprints of PCB designs to enable their exchange in the token economy. We investigate how to bind files in different formats and quality representations to the same token. A robust multi-file binding based on the copper layers of a PCB design was achieved by calculating an adaptive perceptual hash of all files. The adaptive perceptual hash was evaluated against numerous tamperings of the routing layout of a PCB, showing decent resistance to layout changes. The resulting adaptive perceptual hash can be used as an additional identification attribute in a tokenized asset. Furthermore, assets must be authenticatable and verifiable by marketplaces, manufacturers, and other participants to create trust in a decentralized environment. While assets can be tampered with to manipulate, for example, cryptographic hashes that link the file to the token, perceptual hashes can compute a perceived or functional similarity of two objects instead of the plain file integrity. Without the possibility of verifying and protecting intellectual property, mass adoption of the Metaverse and Industrial Metaverse is unlikely. Therefore, we contribute to detecting tampering attacks on 3D models by introducing a 3D perceptual hash that is robust to a set of mesh manipulations, enabling the trusted exchange and authentication of 3D data in the Metaverse.

Archival collections contain an underutilized wealth of biodiversity data, encapsulated in government files and other historical documents. In 1845, the Bavarian government conducted a comprehensive national survey on the occurrence of 44 selected vertebrate species across the country. The detailed expert responses from 119 forestry offices, totalling 520 handwritten pages, have been preserved in the Bavarian State Archives. In this study, we digitized, annotated, geographically referenced, and published these historical records, making them widely available as data for research and conservation planning. Our dataset, openly accessible through the Global Biodiversity Information Facility (GBIF) and Zenodo, contains 5,467 species occurrence records from 1845. Besides the binary presence/absence data, we have also published the original textual survey responses, which contain rich qualitative information, such as species abundances, population trends, habitats, forest management practices, and human-nature relationships. This information can be further processed and interpreted to address a range of questions in historical and contemporary ecology.

With the increasing adoption of virtualization technologies across various industries, virtual machines (VMs) offer cost-effective solutions for obtaining computing power without the burden of initial investment or ongoing maintenance. However, the widespread use of VMs also increases the risk of malicious actors attempting to gain unauthorized access due to the possibility of accessing the VMs via standard internet protocols. Virtual Machine Introspection (VMI) and Forensic Memory Analysis (FMA) are two key cybersecurity methods for addressing these threats. While FMA leverages digital forensic techniques to extract and analyse information from system memory to explain security incidents, VMI typically works with live systems, analysing running processes to detect real-time threats. Both approaches face a significant challenge known as the ”semantic gap,” which arises from the need to infer high-level system information from low-level data such as physical memory and CPU registers. This dissertation explores using machine learning to bridge the semantic gap in FMA and VMI applications. The research uses OpenSSH process heap dumps as a use-case to extract high-level structures, such as OpenSSH encryption keys, from raw process memory dumps. The study employs various techniques to isolate relevant memory sections, from basic memory chunking and entropy analysis to more advanced methods utilizing pointers and malloc headers. During this research study, we also identified the need for a foundation model in memory forensics. Foundation models are general purpose models trained on large amounts of data and users can later use these models to perform different tasks by finetuning the model. This research also addresses the challenge of detecting malware by analysing system-level API calls and employing custom feature engineering techniques. Given that the threat landscape is constantly evolving, we also investigate concept drift — a phenomenon where input data distribution changes affect predictive models’ performance. To mitigate the degradation in performance due to concept drift, we introduce a concept drift detection algorithm complemented by a custom sampling method that optimizes training data selection. This approach reduces the training dataset size by one-third, enhancing the efficiency of model training while maintaining high performance.

This study reports the Intraclass Correlation Coefficients of feedback ratings produced by OpenAI's GPT-4, a large language model (LLM), across various iterations, time frames, and stylistic variations. The model was used to rate responses to tasks related to macroeconomics in higher education (HE), based on their content and style. Statistical analysis was performed to determine the absolute agreement and consistency of ratings in all iterations, and the correlation between the ratings in terms of content and style. The findings revealed high interrater reliability, with ICC scores ranging from 0.94 to 0.99 for different time periods, indicating that GPT-4 is capable of producing consistent ratings. The prompt used in this study is also presented and explained.