Occupation coding is the classification of information on occupation that is collected in the context of demographic variables. Occupation coding is an important, but a tedious task for researchers in social science and official statistics that calls for automation. Due to the complexity of the task, currently, researchers carry out hand-coding or computer-assisted coding. However, we argue that, with the rise of transformer-based language models, hand-coding can be displaced by models, such as BERT or GPT3. Hence, we compare these models with state-of-the-art encoding approaches, showing that language models have a clear advantage in Cohen’s kappa compared to related approaches, but also allow for flexible fine-grained coding of single digits. Taking into consideration the hierarchical structure of the occupational group, we also develop an approach that achieves better performance for the classification of different single digit combinations.

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, leading to the conception of spectral clustering algorithms. Spectral clustering algorithms have been one of the most effective in grouping similar data points in graph data models. In this paper, we have compiled 16 spectral clustering algorithms and compared their computational complexities, after an overview of graph data models and graph database models. Furthermore, we provided a broad taxonomy to classify most existing clustering algorithms and discussed the taxonomy in detail.

Searching in sorted arrays of keys is a common task with a broad range of applications. Often searching is part of the performance critical sections of a database query or index access, raising the question what kind of search algorithm to choose and how to optimize it to obtain the best possible performance on real-world hardware. This paper strives to answer this question by evaluating a large set of optimized sequential, binary and k-ary search algorithms on a modern processor. In this context, we consider hardware-sensitive optimization strategies as well as algorithmic variations resulting in an eight-dimensional evaluation space. As a result, we give insights on expected interactions between search algorithms and optimizations on modern hardware. In fact, there is no single best optimized algorithm, leading to a set of advices on which variants should be considered first given a particular array size.

The dynamic evolution of non-volatile memory (NVM) technologies from Read-Only Memory (ROM) to flash memory, and recent innovations in Magnetoresistive RAM (MRAM), Phase Change Memory (PCM), and Resistive RAM (ReRAM) signify a pivotal shift in data storage capabilities and applications. This progression, marked by enhancements in performance and density, fills the gap between Dynamic RAM (DRAM) and flash storage, meeting the demands of data-rich domains such as high-performance computing and databases. The integration of NVM like the formerly commercially available Optane Persistent Memory (PMem) has introduced a paradigm shift in storage system design, addressing challenges related to latency, data proximity, memory constraints, and energy efficiency which are critical in computing. Despite facing hurdles, such as performance asymmetry in Optane PMem, the potential of NVM in data storage systems has been shown with research focusing on speeding up indexing, small data accesses, and cache-less I/O paths. We review existing and emerging NVM technologies based on their physical properties and application in data storage. We underscore the significance of NVM in offering unique access characteristics that address the limitations of traditional storage devices, thereby extending the data storage landscape. We discuss the programming challenges and hardware-software strategies crucial for NVM’s seamless integration and widespread industry adoption. Additionally, we discuss the discontinuation of Optane, the ongoing challenges and the need for storage system designs to adapt to non-block based storage to leverage NVM’s performance benefits. We conclude that while advancements in NVM promise improved performance and endurance, practical availability depends on addressing challenges such as density, heat management, and reliability. Support in the existing software ecosystem is also crucial for its widespread adoption. Furthermore, Compute Express Link (CXL) is highlighted as a significant development that can streamline the memory hierarchy and support the adoption of NVM, enabling more flexible designs and effective usage of storage media in next-generation data storage systems. Overall, CXL offers a promising method to include NVM in future systems. However, in the meantime, data storage systems will rely on NVMe SSDs due to their performance and availability. But, we encourage further research into integrating NVM devices due to data management constraints with conventional block-based data management and advocate for the availability of NVM-ready software to users.

Efficient evaluation of selection predicates is a performance-critical task. With analytical queries getting more complex, the number of selection predicates per query and table rises. Recent approaches to increase performance of main-memory databases aim at exploiting the CPU speed by using accelerated scans. However, scanning each column leaves tuning opportunities open that arise considering all predicates together. We introduce Elf, an index structure that is able to exploit the relation between several selection predicates. Elf features cache sensitivity, an optimized storage layout, fixed search paths, and data compression. We compare its query performance to state-of-the-art approaches and a scan using SIMD. Our results indicate a clear superiority of our approach for queries returning less than 10% of all tuples — a selectivity almost one magnitude larger than observed for related indexing approaches. For TPC-H queries with multi-column selection predicates, we achieve a speedup between factor five and two orders of magnitude, mainly depending on the selectivity of the predicates. Further experiments reveal that for large data sets, these speedup factors are expected to increase. Finally, our results indicate that using a delta-store like concept results in virtually no performance penalty for reasonable sizes of a write-optimized Elf as delta store.