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MetaConfigurator: A User-Friendly Tool for Editing Structured Data Files

Datenbank-Spektrum 2024年第2期24卷 161-169页

作者： Neubauer, Felix Bredl, Paul Xu, Minye Patel, Keyuriben Pleiss, Jürgen Uekermann, Benjamin Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany Institute of Biochemistry and Technical Biochemistry University of Stuttgart Allmandring 31 Stuttgart70569 Germany

Textual formats to structure data, such as JSON, XML, and YAML, are widely used for structuring data in various domains, from configuration files to research data. However, manually editing data in these formats can be complex and time-consuming. Graphical user interfaces (GUIs) can significantly reduce manual efforts and assist the user in editing the files, but developing a file-format-specific GUI requires substantial development and maintenance efforts. To address this challenge, we introduce MetaConfigurator: an open-source web application that generates its GUI depending on a given schema. Our approach differs from other schema-to-UI approaches in three key ways: 1) It offers a unified view that combines the benefits of both GUIs and text editors, 2) it enables schema editing within the same tool, and 3) it supports advanced schema features, including conditions and constraints. In this paper, we discuss the design and implementation of MetaConfigurator, backed by insights from a small-scale qualitative user study. The results indicate the effectiveness of our approach in retrieving information from data and schemas and in editing them. © The Author(s) 2024.

关键词： Graphical user interfaces

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Enhancing Data Trustworthiness in Explorative Analysis: An Interactive Approach for Data Quality Monitoring

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SN Computer Science 2024年第5期5卷 465页

作者： Behringer, Michael Hirmer, Pascal Villanueva, Alejandro Rapp, Jannis Mitschang, Bernhard Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 Stuttgart 70569 Germany

The volume of data to be analyzed has increased tremendously in recent years. In order to extract knowledge from this data, domain experts gain new insights with the help of graphical analysis tools for explorative analyses. Here, the reliability and trustworthiness of an exploratory analysis is determined by the quality of the underlying data. Existing approaches require manual testing to ensure data quality which is often neglected. This research aims to introduce a novel interactive approach for seamlessly integrating data quality considerations into the process of explorative data analysis conducted by domain experts. We derive requirements, conduct an extensive literature review, and develop an approach that efficiently combines stakeholders’ strengths, allowing unobtrusive data quality integration in interactive analysis. Our approach enhances trustworthiness due to unobtrusive monitoring of data quality within the context of explorative data analysis. Domain experts gain insights more reliably, bridging the gap between technical requirements and domain expertise. In conclusion, our research presents a promising solution for improving the reliability and trustworthiness of explorative data analysis, especially for domain experts who may lack technical knowledge. By seamlessly integrating data quality into the analytical process, we empower domain experts to extract valuable insights from the ever-increasing volume of data, thereby advancing the field of data-driven decision-making. © The Author(s) 2024.

关键词： Data Quality Data Mashups Explorative Data Analysis Human-in-the-Loop

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Research Data Management in Simulation Science: Infrastructure, Tools, and Applications

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Datenbank-Spektrum 2024年第2期24卷 97-105页

作者： Flemisch, Bernd Hermann, Sibylle Herschel, Melanie Pflüger, Dirk Pleiss, Jürgen Range, Jan Roy, Sarbani Takamoto, Makoto Uekermann, Benjamin Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Stuttgart Germany Stuttgart Center for Simulation Science University of Stuttgart Stuttgart Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Institute of Biochemistry and Technical Biochemistry University of Stuttgart Stuttgart Germany NEC Laboratories Europe Heidelberg Germany

Research Data Management (RDM) has gained significant traction in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech). After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques. © The Author(s) 2024.

关键词： Information management

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SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis 25

SDRank: A Deep Learning Approach for Similarity Ranking of D...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Behringer, Michael Treder-Tschechlov, Dennis Voggesberger, Julius Hirmer, Pascal Mitschang, Bernhard Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 StuttgartD-70569 Germany

ISBN: (纸本)9789897586484

Today, data analytics is widely used throughout many domains to identify new trends, opportunities, or risks and improve decision-making. By doing so, various heterogeneous data sources must be selected to form the foundation for knowledge discovery driven by data analytics. However, discovering and selecting the suitable and valuable data sources to improve the analytics results is a great challenge. Domain experts can easily become overwhelmed in the data selection process due to a large amount of available data sources that might contain similar kinds of information. Supporting domain experts in discovering and selecting the best suitable data sources can save time, costs and significantly increase the quality of the analytics results. In this paper, we introduce a novel approach - SDRank - which provides a Deep Learning approach to rank data sources based on their similarity to already selected data sources. We implemented SDRank, trained various models on 4 860 datasets, and measured the achieved precision for evaluation purposes. By doing so, we showed that SDRank is able to highly improve the workflow of domain experts to select beneficial data sources. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Data Analytics

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Unobtrusive Integration of Data Quality in Interactive Explorative Data Analysis 25

Unobtrusive Integration of Data Quality in Interactive Explo...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Behringer, Michael Hirmer, Pascal Villanueva, Alejandro Rapp, Jannis Mitschang, Bernhard Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 StuttgartD-70569 Germany

ISBN: (纸本)9789897586484

The volume of data to be analyzed has increased tremendously in recent years. To extract knowledge from this data, domain experts gain new insights using graphical analysis tools for explorative analyses. Hereby, the reliability and trustworthiness of an explorative analysis are determined by the quality of the underlying data. Existing approaches require a manual inspection to ensure data quality. This inspection is frequently neglected, partly because domain experts often lack the necessary technical knowledge. Moreover, they might need many different tools for this purpose. In this paper, we present a novel interactive approach to integrate data quality into explorative data analysis in an unobtrusive manner. Our approach efficiently combines the strength of different experts, which is currently not supported by state-of-the-art tools, thereby allowing domain-specific adaptation. We implemented a fully working prototype to demonstrate the ability of our approach to support domain experts in explorative data analysis. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Reliability analysis

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First Experiences on the Application of Lakehouses in Industrial Practice 35

First Experiences on the Application of Lakehouses in Indust...

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35th GI-Workshop on Foundations of Database systems, GvDB 2024

作者： Schneider, Jan Lutsch, Arnold Gröger, Christoph Schwarz, Holger Mitschang, Bernhard Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany Robert Bosch GmbH Borsigstraße 4 Stuttgart70469 Germany

In recent years, so-called lakehouses have emerged as a new type of data platform that intends to combine characteristics of data warehouses and data lakes. Although companies started to employ the associated concepts and technologies as part of their analytics architectures, little is known about their practical medium- and long-term experiences as well as proven architectural decisions. Additionally, there is only limited knowledge about how lakehouses can be utilized effectively in an industrial context. Hence, it remains unclear under which circumstances lakehouses represent a viable alternative to conventional data platforms. To address this gap, we conducted a case study on a real-world industrial case, in which manufacturing data needs to be managed and analytically exploited. Within the scope of this case, a dedicated analytics department has been testing and leveraging a lakehouse approach for several months in a productive environment with high data volumes and various types of analytical workloads. The paper at hand presents the results of our within-case analyses and focuses on the industrial setting of the case as well as the architecture of the utilized lakehouse. This way, it provides preliminary insights on the application of lakehouses in industrial practice and refers to useful architectural decisions. © 2024 Copyright for this paper by its authors.

关键词： Data warehouses

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Proximity-based service discovery for distributed digital twin systems

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Discover Internet of Things 2025年第1期5卷 1-24页

作者： Rothermel, Kurt Herzog, Otthein Zhiqiang, Siegfried Wu Institute Of Parallel and Distributed Systems University of Stuttgart Universitaetsstr. 38 Stuttgart70569 Germany College of Architecture and Urban Planning Tongji University 1239 Siping Road Shanghai200092 China Department of Mathematics and Computer Science University of Bremen Bibliothekstrasse 5 Bremen23884 Germany

Over the past decade, there has been a significant increase in interest in digital twin (DT) technology in a variety of domains. While research on DTs of single assets was initially prevalent, there has been a notable shift towards distributed systems of DTs, which connect to each other to collaborate. Typically, collaboration is enabled by DTs providing services that can be consumed by other DTs. In service-oriented systems, a service is typically identified by type information. However, this is not sufficient in distributed DT systems, where DTs associated with different physical entities may provide the same type of service. Consequently, selecting the appropriate service depends not only on the service type, but also on the associated physical entity. However, requiring DTs to know the mapping of services to their physical environment is not feasible for large dynamic systems. This paper presents a novel proximity-based service discovery method that allows DTs to select services based on service type and their proximity to other objects. That is, service specifications are fully abstracted from the mapping of services to physical objects, relieving DTs from maintaining information about this mapping. Furthermore, service discovery is robust to changes in the physical environment and service population. The proposed service discovery method has been implemented on top of a spatial DBMS. We argue that this implementation is optimal in terms of network utilization and latency, and perform comprehensive evaluations to show the performance of discovery queries as a function of their complexity. © The Author(s) 2025.

关键词： Location based services

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Metrics and Algorithms for Locally Fair and Accurate Classifications using Ensembles

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Datenbank-Spektrum 2022年第1期22卷 23-43页

作者： Lässig, Nico Oppold, Sarah Herschel, Melanie Institute for Parallel and Distributed Systems – Data Engineering University of Stuttgart Universitätsstr. 38 Stuttgart70569 Germany

To obtain accurate predictions of classifiers, model ensembles comprising multiple trained machine learning models are nowadays used. In particular, dynamic model ensembles pick the most accurate model for each query object, by applying the model that performed best on similar data. Dynamic model ensembles may however suffer, similarly to single machine learning models, from bias, which can eventually lead to unfair treatment of certain groups of a general population. To mitigate unfair classification, recent work has thus proposed fair model ensembles, that instead of focusing (solely) on accuracy also optimize global fairness. While such global fairness globally minimizes bias, imbalances may persist in different regions of the data, e.g., caused by some local bias maxima leading to local unfairness. Therefore, we extend our previous work by including a framework that bridges the gap between dynamic model ensembles and fair model ensembles. More precisely, we investigate the problem of devising locally fair and accurate dynamic model ensembles, which ultimately optimize for equal opportunity of similar subjects. We propose a general framework to perform this task and present several algorithms implementing the framework components. In this paper we also present a runtime-efficient framework adaptation that keeps the quality of the results on a similar level. Furthermore, new fairness metrics are presented as well as detailed informations about necessary data preparations. Our evaluation of the framework implementations and metrics shows that our approach outperforms the state-of-the art for different types and degrees of bias present in training data in terms of both local and global fairness, while reaching comparable accuracy. © 2022, The Author(s).

关键词： Dynamic models

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A HPX Communication Benchmark: distributed FFT using Collectives

arXiv

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arXiv 2025年

作者： Strack, Alexander Pflüger, Dirk Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart70569 Germany

Due to increasing core counts in modern processors, several task-based runtimes emerged, including the C++ Standard Library for Concurrency and parallelism (HPX). Although the asynchronous many-task runtime HPX allows implicit communication via an Active Global Address Space, it also supports explicit collective operations. Collectives are an efficient way to realize complex communication patterns. In this work, we benchmark the TCP, MPI, and LCI communication backends of HPX, which are called parcelports in HPX terms. We use a distributed multi-dimensional FFT application relying on collectives. Furthermore, we compare the performance of the HPX all-to-all and scatter collectives against an FFTW3 reference based on MPI+X on a 16-node cluster. Of the three parcelports, LCI performed best for both scatter and all-to-all collectives. Furthermore, the LCI parcelport was up to factor 3 faster than the MPI+X reference. Our results highlight the potential of message abstractions and the parcelports of HPX. Copyright © 2025, The Authors. All rights reserved.

关键词： C++ (programming language)

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Assessing the Lakehouse: Analysis, Requirements and Definition 25

Assessing the Lakehouse: Analysis, Requirements and Definiti...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Schneider, Jan Gröger, Christoph Lutsch, Arnold Schwarz, Holger Mitschang, Bernhard Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany Robert Bosch GmbH Borsigstraße 4 Stuttgart70469 Germany

ISBN: (纸本)9789897586484

The digital transformation opens new opportunities for enterprises to optimize their business processes by applying data-driven analysis techniques. For storing and organizing the required huge amounts of data, different types of data platforms have been employed in the past, with data warehouses and data lakes being the most prominent ones. Since they possess rather contrary characteristics and address different types of analytics, companies typically utilize both of them, leading to complex architectures with replicated data and slow analytical processes. To counter these issues, vendors have recently been making efforts to break the boundaries and to combine features of both worlds into integrated data platforms. Such systems are commonly called lakehouses and promise to simplify enterprise analytics architectures by serving all kinds of analytical workloads from a single platform. However, it remains unclear how lakehouses can be characterized, since existing definitions focus almost arbitrarily on individual architectural or functional aspects and are often driven by marketing. In this paper, we assess prevalent definitions for lakehouses and finally propose a new definition, from which several technical requirements for lakehouses are derived. We apply these requirements to several popular data management tools, such as Delta Lake, Snowflake and Dremio in order to evaluate whether they enable the construction of lakehouses. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Data warehouses

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