arXiv subjects

Kai-Uwe Sattler

Publications and source records attributed to Kai-Uwe Sattler.

6 recordsLinked to original sources

arXiv2025

Experimental Evaluation of Dynamic Topic Modeling Algorithms

The amount of text generated daily on social media is gigantic and analyzing this text is useful for many purposes. To understand what lies beneath a huge amount of text, we need dependable and effective computing techniques from self-powered topic models. Nevertheless, there are currently relatively few thorough quantitative comparisons between these models. In this study, we compare these models and propose an assessment metric that documents how the topics change in time.

cs.IR↗

arXiv2025

Physics-Guided Sequence Modeling for Fast Simulation and Design Exploration of 2D Memristive Devices

Modeling hysteretic switching dynamics in memristive devices is computationally demanding due to coupled ionic and electronic transport processes. This challenge is particularly relevant for emerging two-dimensional (2D) devices, which feature high-dimensional design spaces that remain largely unexplored. We introduce a physics-guided modeling framework that integrates high-fidelity finite-volume (FV) charge transport simulations with a long short-term memory (LSTM) artificial neural network (ANN) to predict dynamic current-voltage behavior. Trained on physically grounded simulation data, the ANN surrogate achieves more than four orders of magnitude speedup compared to the FV model, while maintaining direct access to physically meaningful input parameters and high accuracy with typical normalized errors <1%. This enables iterative tasks that were previously computationally prohibitive, including inverse modeling from experimental data, design space exploration via metric mapping and sensitivity analysis, as well as constrained multi-objective design optimization. Importantly, the framework preserves physical interpretability via access to detailed spatial dynamics, including carrier densities, vacancy distributions, and electrostatic potentials, through a direct link to the underlying FV model. Our approach establishes a scalable framework for efficient exploration, interpretation, and model-driven design of emerging 2D memristive and neuromorphic devices.

cond-mat.mtrl-sci↗

arXiv2022

Management of Machine Learning Lifecycle Artifacts: A Survey

The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.

cs.DB↗

arXiv2022

Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications

Distributed in-memory data processing engines accelerate iterative applications by caching substantial datasets in memory rather than recomputing them in each iteration. Selecting a suitable cluster size for caching these datasets plays an essential role in achieving optimal performance. In practice, this is a tedious and hard task for end users, who are typically not aware of cluster specifications, workload semantics and sizes of intermediate data. We present Blink, an autonomous sampling-based framework, which predicts sizes of cached datasets and selects optimal cluster size without relying on historical runs. We evaluate Blink on a variety of iterative, real-world, machine learning applications. With an average sample runs cost of 4.6% compared to the cost of optimal runs, Blink selects the optimal cluster size in 15 out of 16 cases, saving up to 47.4% of execution cost compared to average costs.

cs.DC↗

arXiv2021

Updatable Materialization of Approximate Constraints

Modern big data applications integrate data from various sources. As a result, these datasets may not satisfy perfect constraints, leading to sparse schema information and non-optimal query performance. The existing approach of PatchIndexes enable the definition of approximate constraints and improve query performance by exploiting the materialized constraint information. As real world data warehouse workloads are often not limited to read-only queries, we enhance the PatchIndex structure towards an update-conscious design in this paper. Therefore, we present a sharded bitmap as the underlying data structure which offers efficient update operations, and describe approaches to maintain approximate constraints under updates, avoiding index recomputations and full table scans. In our evaluation, we prove that PatchIndexes significantly impact query performance while achieving lightweight update support.

cs.DB↗

arXiv2020

Data Structure Primitives on Persistent Memory: An Evaluation

Persistent Memory (PMem), as already available, e.g., with Intel Optane DC Persistent Memory, represents a very promising, next-generation memory solution with a significant impact on database architectures. Several data structures for this new technology and its properties have already been proposed. However, primarily only complete structures are presented and evaluated. Thus, the implications of the individual ideas and PMem features are concealed. Therefore, in this paper, we disassemble the structures presented so far, identify their underlying design primitives, and assign them to appropriate design goals regarding PMem. As a result of our comprehensive experiments on real PM hardware, we can reveal the trade-offs of the primitives for various access patterns. This allowed us to pinpoint their best use cases as well as vulnerabilities. Besides our general insights regarding PMem-based data structure design, we also discovered new combinations not examined in the literature so far.

cs.DB↗