@inproceedings{64823,
abstract = {{Current legal frameworks enforce that Android developers accurately report the data their apps collect. However, large codebases can make this reporting challenging. This paper employs an empirical approach to understand developers' experience with Google Play Store's Data Safety Section (DSS) form.
We first survey 41 Android developers to understand how they categorize privacy-related data into DSS categories and how confident they feel when completing the DSS form. To gain a broader and more detailed view of the challenges developers encounter during the process, we complement the survey with an analysis of 172 online developer discussions, capturing the perspectives of 642 additional developers. Together, these two data sources represent insights from 683 developers.
Our findings reveal that developers often manually classify the privacy-related data their apps collect into the data categories defined by Google-or, in some cases, omit classification entirely-and rely heavily on existing online resources when completing the form. Moreover, developers are generally confident in recognizing the data their apps collect, yet they lack confidence in translating this knowledge into DSS-compliant disclosures. Key challenges include issues in identifying privacy-relevant data to complete the form, limited understanding of the form, and concerns about app rejection due to discrepancies with Google's privacy requirements.
These results underscore the need for clearer guidance and more accessible tooling to support developers in meeting privacy-aware reporting obligations. }},
author = {{Khedkar, Mugdha and Schlichtig, Michael and Soliman, Mohamed Aboubakr Mohamed and Bodden, Eric}},
booktitle = {{Proceedings of the IEEE/ACM 13th International Conference on Mobile Software Engineering and Systems (MOBILESoft '26). Association for Computing Machinery, New York, NY, USA, 65–68.}},
keywords = {{static analysis, data collection, data protection, privacy-aware reporting}},
location = {{Rio de Janeiro, Brazil}},
title = {{{Challenges in Android Data Disclosure: An Empirical Study.}}},
year = {{2026}},
}
@inproceedings{63754,
abstract = {{Data spaces are receiving an emerging interest in Information Systems Research and industry practice. They are central to many European research initiatives and shape the data economy in Industry 4.0. Generally, they aim to create secure environments for cross-organizational data management and sharing. Currently, there is considerable interest in developing new data spaces in Industry 4.0, also accelerated through regulatory changes. However, key questions about what precisely characterizes a data space in Industry 4.0 remain unresolved. Against this backdrop, we build a taxonomy of data spaces in the Industry 4.0 context. We identified nine distinctive dimensions and 40 corresponding characteristics among the 19 data spaces analyzed. The taxonomy enables clearer classification and nomenclature of data spaces in this context. This short paper will ignite planned further research on data spaces in Industry 4.0 and contribute to a conceptualization of a taxonomic theory for interested researchers.}},
author = {{Werth, Oliver and Koldewey, Christian and Uslar, Mathias and Zerbin, Julian}},
booktitle = {{Lecture Notes in Business Information Processing}},
isbn = {{9783032145178}},
issn = {{1865-1348}},
keywords = {{Industry 4.0, Taxonomy, Data spaces, Characterization}},
location = {{Stuttgart, Germany}},
publisher = {{Springer Nature Switzerland}},
title = {{{What Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding}}},
doi = {{10.1007/978-3-032-14518-5_3}},
year = {{2026}},
}
@inproceedings{66288,
abstract = {{ABSTRACT:
Engineers simulate system behavior to support decisions in product engineering. Leveraging such engineering simulation data in strategic product planning can support idea generation and early evaluation of design alternatives and limitations. However, limited resources and expertise hinder broader uptake in strategic product planning. This paper investigates simulator integration into automated workflows and key processing components to enable simulation without in-depth expertise. This approach improves strategic product planning by creating data-based decision support.}},
author = {{Gräßler, Iris and Döhner, Niklas}},
booktitle = {{Proceedings of the Design Society}},
issn = {{2732-527X}},
keywords = {{simulation-based design, design tools, multi-/cross-/trans-disciplinary approaches, simulation data reuse}},
pages = {{357--366}},
publisher = {{Cambridge University Press (CUP)}},
title = {{{Leveraging extreme-scale simulation data: a workflow framework for multidisciplinary simulator integration}}},
doi = {{10.1017/pds.2026.10394}},
volume = {{6}},
year = {{2026}},
}
@techreport{66548,
abstract = {{The Circular Economy is considered a key approach to addressing resource scarcity, rising raw material prices, and the need for more resilient value chains. However, its practical implementation is often hindered by the lack of suitable and standardized metrics, as well as by high requirements for horizontal and vertical data integration. This white paper illustrates, through selected Manufacturing-X projects, how federated data ecosystems can help establish Circular Economy metrics as an effective management instrument for growth, margin protection, and resilience.
Based on literature, industrial practice, and demonstrators, Circular Economy metrics are structured into four groups: overarching assessment approaches, disassembly-related indicators, lifetime-oriented metrics, and end-of-life and recycling-focused indicators. Use cases from Construct-X, Decide4ECO, Fluid 4.0, Chem-X, and other Manufacturing-X initiatives demonstrate the benefits of data-driven approaches for product, component, and material loops. At the same time, they reveal a current lack of interoperable, cross-industry standards for both metrics and data models.
The white paper concludes that data ecosystems such as Manufacturing-X are a key enabler for scaling circular business models. In particular, shared cross-sector frameworks for metrics, standardized information models based for example on the Asset Administration Shell, and the integration of circular metrics into existing systems and processes are essential. The work presented therefore marks an important step toward a data-driven and economically viable Circular Economy.}},
author = {{Pottebaum, Jens and Dietrich, Katrin and Schmidt, Lara and Biglari, Mostafa and Schmidt, Michael-Georg and Pistillo, Alessandro and Gravina, Nadja and Schmidt, Franziska}},
keywords = {{Circular Economy, Circular Economy Metrics, Manufacturing-X, Data Ecosystems}},
publisher = {{Manufacturing-X Guidance Board}},
title = {{{Understanding the Value of Data Ecosystems for Circular Economy Metrics}}},
doi = {{10.24406/PUBLICA-9382}},
year = {{2026}},
}
@article{66070,
abstract = {{Der Beitrag stellt einen Online-Quick-Check vor, der KMU einen niedrigschwelligen Einstieg in industrielle Datenräume ermöglicht. Der Check kombiniert eine zweidimensionale Einschätzung der Readiness (strategisch/technologisch) mit einer Mehrwert-Priorisierung zur Ableitung passender Use Cases. In einer Pilotierung (n = 19) zeigen sich v. a. Defizite bei der Vernetzung, den datenbasierten Entscheidungen und Datenrollen; priorisiert werden Mehrwerte in Planung, Qualität und Effizienz.}},
author = {{Zerbin, Julian and Grünwoldt, Max and Koldewey, Christian and Dumitrescu, Roman}},
issn = {{2511-0896}},
journal = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
keywords = {{KMU, Data Space, Data Sharing, Industrie 4.0, Datensouveränität, Readiness}},
number = {{6}},
pages = {{458--462}},
publisher = {{Walter de Gruyter GmbH}},
title = {{{Ein Quick-Check für Industrielle Datenräume – Voraussetzungen und Mehrwerte}}},
doi = {{10.1515/zwf-2026-1077}},
volume = {{121}},
year = {{2026}},
}
@inproceedings{66071,
abstract = {{Data ecosystems offer new opportunities for value creation by enabling companies to combine and reuse
data beyond their own boundaries. Coopetition, i.e., simultaneous cooperation and competition, is an
important dynamic within data ecosystems that must be understood and managed well to leverage the
non-rivalrous nature of data. A systematic literature review of 38 peer-reviewed studies on ecosystems
and coopetition was conducted to clarify how coopetition affects data ecosystems. Using qualitative
content analysis, the study summarizes how coopetition is calibrated, what results it produces, and what
challenges are associated with it. The resulting Data Ecosystem Coopetition (DECo) model specifies
four management levers, as well as the associated outcomes and common challenges. In addition, six
cycles were identified that link levers, challenges, and outcomes. These cycles provide concise guidance
on elements that tend to vary together and highlight areas that are prone to specific tensions.}},
author = {{Zerbin, Julian and Koldewey, Christian and Dumitrescu, Roman}},
booktitle = {{ECIS 2026 Proceedings}},
keywords = {{data sharing, interfirm relations, paradox theory, strategic alliances, network governance}},
location = {{Mailand}},
title = {{{Towards An Understanding Of Coopetition In Data Ecosystems: A Systematic Literature Review}}},
year = {{2026}},
}
@inproceedings{66073,
abstract = {{Data-driven business models are transforming industries, yet small and medium-sized enterprises (SMEs) often lack the resources to participate in emerging data sharing initiatives. Data spaces provide a federated infrastructure for sovereign data sharing, but SME participation remains limited due to complex onboarding processes involving technical, organizational, legal, and economic barriers. Data trustees have been proposed as intermediaries to facilitate data sharing, but their specific role in supporting SME onboarding has not been systematically explored. Here we present a reference process for SME data space onboarding and derive four design principles, structured along the TELO framework, describing how data trustees can address these barriers. Our findings reveal that SMEs require comprehensive support throughout the onboarding journey, with particular emphasis on business case development. The results offer actionable guidance for data space operators and policymakers seeking to broaden ecosystem participation as the European Union advances its data strategy.}},
author = {{Hupperz, Marius Johannes and Zerbin, Julian and Kunert, Julia}},
booktitle = {{AMCIS 2026 Proceedings}},
keywords = {{Data spaces, data trustees, SME Onboarding, data sharing}},
location = {{Reno - Nevada}},
title = {{{Toward Design Principles for Data Trustee-Supported SME Onboarding in Data Spaces}}},
year = {{2026}},
}
@inproceedings{66072,
abstract = {{Data Spaces are establishing themselves as a decentralized alternative for sovereign data sharing across organizations. However, their adoption faces significant challenges, particularly the lack of proven business models that encourage participation and value creation. A data space business model captures value differently than either traditional business models or platform business models. This paper addresses this challenge by developing the Data Space Canvas, a structured framework for designing and aligning business models in data spaces consisting of nine building blocks. The canvas was developed following the design science research methodology. The modular and role-specific structure of the Data Space Canvas facilitates shared understanding and supports business model development across participants. This research contributes a practical tool that enables organizations to conceptualize, communicate, and refine their role and business model in data spaces.}},
author = {{Zerbin, Julian and Koldewey, Christian and Dumitrescu, Roman}},
booktitle = {{ECIS 2026 Proceedings}},
keywords = {{data ecosystems, business model, design science, resource orchestration theory, data sharing}},
location = {{Mailand}},
title = {{{The Data Space Canvas: A Framework For Structuring Business Models In Data Spaces}}},
year = {{2026}},
}
@inproceedings{67552,
abstract = {{Predictive Maintenance is a key element of data-driven maintenance strategies in Industry 4.0. It promises improved asset availability and operational efficiency. In addition to data analytics and machine learning, simulation-based assessment and digital representations of production systems increasingly complement Predictive Maintenance by enabling the evaluation of maintenance decisions and failure risks before physical intervention. However, small and medium-sized enterprises (SMEs) often face challenges in realizing these benefits due to insufficient employee competencies across all domains. As a prerequisite, a clear understanding of competency gaps that hinder employees to use relevant key technologies such as artificial intelligence and data analytics is needed. This paper presents competency profiles that define relevant competencies for the use of simulation-based Predictive Maintenance and map these competencies to relevant roles that need them. The study is based on a systematic literature review about competencies for digitalization processes in industry focusing on maintenance and, more specifically, data technologies in maintenance. The competency profile is validated through a Delphi-Study with industry representatives assessing the identified competency levels. The results support a more holistic understanding of Predictive Maintenance and provide guidance for targeted competency development in SMEs.}},
author = {{Gräßler, Iris and Niemeyer, Marcel}},
booktitle = {{24th INTERNATIONAL INDUSTRIAL SIMULATION CONFERENCE}},
editor = {{Gonzalez, Jose David Nunez and Romay, Manuel Grana}},
isbn = {{ 978-9-492859-40-2}},
keywords = {{Predictive Maintenance, Artificial Intelligence, Data Processing, Competencies, Role Profiles}},
location = {{San Sebastian, Spain}},
pages = {{91--97}},
title = {{{COMPETENCY PROFILES FOR THE USE OF PREDICTIVE MAINTENANCE IN SMES}}},
volume = {{24}},
year = {{2026}},
}
@book{66478,
abstract = {{Für Lehrer*innen ist es wichtig, über die Qualität ihrer Arbeit informiert zu sein, um sich an Schul- und Unterrichtsentwicklung beteiligen zu können. Das vorliegende Lehrbuch bietet anhand eines erprobten Modells – eines Forschungszyklus in Verbindung mit einem ergänzenden Innovationszyklus – konkrete Hilfestellungen, um die eigene Tätigkeit zu reflektieren und weiterzuentwickeln.
Der zugrundeliegende Ansatz verbindet erfolgreiche Verfahren aus der niederländischen Lehrerinnen- und Lehrerbildung mit etablierten Methoden der schulischen Praxisforschung im deutschsprachigen Raum. Zahlreiche alltagsbezogene Beispiele illustrieren die Grundsätze und Verfahrensschritte, während Übungsaufgaben zu Vertiefung und Wiederholung anregen.}},
author = {{Textor, Annette and Kullmann, Harry and van der Donk, Cyrilla and van Lanen, Bas}},
isbn = {{978-3-7799-4739-4}},
keywords = {{Praxisforschung, Schulentwicklung, Inklusion, Data Literacy}},
pages = {{343}},
publisher = {{Beltz Juventa}},
title = {{{Praxisforschung in Schule und Unterricht}}},
year = {{2026}},
}
@techreport{66449,
abstract = {{This paper evaluates the forecasting performance of an expanded class of (semi-)parametric
GARCH models belonging to the EGARCH family (EGF), including recently introduced long
and short memory specifications and their semiparametric extensions. The semiparametric
variants employ a multiplicative volatility decomposition into conditional and slowly varying
unconditional components, where the latter is estimated via a data-driven local polynomial
smoother to accommodate non-stationarities commonly observed in financial time series. Based
on the revised Basel Committee framework for market-risk assessment, all models are capable
of producing rolling one-day-ahead forecasts for Value at Risk (VaR) and Expected Shortfall
(ES) under a wide range of symmetric and skewed innovation distributions. Their forecasting
accuracy is examined using the regulatory traffic light tests for VaR and the recently developed
ES-specific traffic light procedure, complemented by the regulatory loss function. In addition,
model selection incorporates both a recently proposed corrected firm-oriented loss function that
accounts for opportunity costs and the Weighted Absolute Deviation (WAD) criterion. The
empirical comparison demonstrates that (semiparametric) long memory GARCH models -
particularly those combining fractional dynamics with nonparametric scale adjustments - can
serve as valuable alternatives to traditional parametric short memory models, offering more
stable volatility estimates and improved tail-risk forecasts for practical risk management
applications.}},
author = {{Hanke, Dominik Christian and Uhde, André and Feng, Yuanhua}},
keywords = {{semiparametric GARCH extension, data-driven local polynomial smoother, long memory, GARCH models, Value at Risk, Expected Shortfall, traffic light test, backtesting, Basel III, market risk}},
title = {{{Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III}}},
year = {{2026}},
}
@misc{67427,
abstract = {{The successful provision of research data does not depend solely on the technical quality of individual systems. Rather, what is crucial is how technical, organizational, and communicative dimensions are interconnected and whether this results in recognizable added value for researchers in their day-to-day work. In this context, both intrinsic and extrinsic motivational factors influence the willingness to actively and continuously use research data management (RDM) systems.This article synthesizes experiences from two collaborating subprojects on information infrastructure (INF) within two Collaborative Research Centers. Both develop and operate RDM structures for interdisciplinary research consortia in the field of engineering and face comparable challenges: Adhering to the rules of good scientific practice when handling data is under pressure in today’s world; the benefits of individual data entries sometimes remain abstract, and additional process steps can reduce acceptance of technical solutions. The effort involved is primarily evaluated based on whether, from the researchers’ perspective, it yields a recognizable benefit. Based on this, specific approaches for user-oriented integration of different RDM levels are presented. These include the integration of RDM tools into existing research tools and workflows, cross-subproject access to information, as well as SPARQL-based search capabilities and future approaches to knowledge exploration. One concrete example is an implemented adapter that automatically transfers data sets from existing Excel files and electronic lab notebook entries from eLabFTW into the RDM system, thereby reducing the number of required process steps. Since the adapter was deployed, a measurable increase in the number of datasets imported into the RDM system has been observed. Such technical and procedural measures are complemented by workshops, personalized support, systematic feedback, and the continuous adaptation of the systems to the needs of researchers.This poster illustrates that RDM systems must be understood as sociotechnical systems. It is only through the interplay of infrastructure, process integration, communication, and perceived benefits that individual RDM developments can be transformed into a practical service. The collaboration between the two INF subprojects makes it possible to pool experiences, solution approaches, and transferable design principles across institutions and research consortia.}},
author = {{Stichternath, Jasper and Müller, Laura and Schultz, Andreas Maximilian and Koepler, Oliver and Mozgova, Iryna}},
keywords = {{Research Data Management, Research Data Management Systems, User Acceptance, Sociotechnical Systems, Perceived Usefulness, Collaborative Research Centres}},
publisher = {{LibreCat University}},
title = {{{Der Erfolg von FDM-Systemen: Die Rolle von wahrgenommenem Nutzen und Motivation der Anwendenden}}},
doi = {{10.5281/ZENODO.23187775}},
year = {{2026}},
}
@inproceedings{64787,
abstract = {{This study proposes a fault diagnostics methodology that addresses the challenges posed by highly imbalanced datasets typical of railway applications, where faulty conditions constitute the minority class. Fault diagnostics is performed from the component level upward, considering each sensor’s proximity to its respective critical component. Advanced signal analysis, feature engineering, and automated data-driven model generation techniques were explored to achieve comprehensive diagnostics, such that the model development process accounts for variations in the operating conditions and differing levels of information availability. The proposed methodology is evaluated on datasets from the MONOCAB, for scenarios with limited faulty instances and on the Beijing 2024 IEEE PHM Conference data challenge, which focused on fault diagnostics of railway systems under various fault modes and operating conditions.}},
author = {{Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Hanselle, Raphael and Rief, Thomas and Beck, Maximilian and Sextro, Walter}},
booktitle = {{PHM Society Asia-Pacific Conference}},
keywords = {{MONOCAB, Beijing Data Challenge, Diagnostics of railway systems}},
title = {{{Multilevel fault diagnostics for railway applications using limited historical data}}},
doi = {{10.36001/phmap.2025.v5i1.4449}},
volume = {{5}},
year = {{2025}},
}
@article{62643,
author = {{Schwabe, Tobias and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and Scheytt, J. Christoph}},
journal = {{Journal of Lightwave Technology}},
keywords = {{Integrated circuit modeling, Capacitance, Silicon, Modulation, Adaptation models, Semiconductor device modeling, Bandwidth, Data communication, electrooptical transmitter, equalization, free-carrier-plasma dispersion effect, modelling, optical modulator, phase shifter, silicon photonics}},
number = {{1}},
pages = {{255--270}},
title = {{{Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology}}},
doi = {{10.1109/JLT.2024.3450949}},
volume = {{43}},
year = {{2025}},
}
@article{62644,
author = {{Schwabe, Tobias and Kress, Christian and Sadiye, Babak and Kruse, Stephan and Scheytt, J. Christoph}},
journal = {{IEEE Access}},
keywords = {{Optical attenuators, Equalizers, Phase shifters, Optical modulation, Electro-optic modulators, Optical amplifiers, Circuits, Silicon photonics, Optical saturation, Integrated circuit modeling, Data communication, equalization, electro-optical transmitter, silicon photonics, phase shifter, optical modulator, free-carrier plasma dispersion effect, driver architectures, biasing schemes}},
pages = {{192433--192450}},
title = {{{Analysis and Design of Forward Biased Silicon Photonics Phase Shifter Equalizer Circuits}}},
doi = {{10.1109/ACCESS.2025.3629385}},
volume = {{13}},
year = {{2025}},
}
@techreport{62697,
abstract = {{Urged by the European Energy Crisis and the threatening consequences of severe
natural gas shortages, energy providers launched gas-saving initiatives incor-
porating financial incentives to reduce residential natural gas consumption. In
collaboration with one of Germany’s largest energy providers, we conducted
a natural field experiment (N = 2,598) to evaluate the effectiveness of a
behaviorally-guided co-design of such a gas-saving initiative by implementing
two established behavioral instruments – reminders of gas saving intentions and
descriptive norm feedback. Our findings show limited effectiveness of the behav-
ioral instruments during the high-price period. The feedback risks a “boomerang
effect” among households with above-average initial savings, who reduce their
conservation efforts in response. The reminder does not significantly enhance sav-
ings in our main specifications, yet, realizes 1 percentage point savings in alternate
models refining for outliers. Potential mechanisms include a significant intention-
action gap and misperceived effectiveness of energy-saving actions, which are not
alleviated by the reminder.}},
author = {{Tinnefeld, Vicky and Kesternich, Martin and Werthschulte, Madeline}},
keywords = {{Residential energy savings, energy crisis, behavioral interventions, survey data, field experiment}},
publisher = {{ZEW Discussion Paper No. 25-60}},
title = {{{Do Energy-Saving Nudges Deliver During High-Price Periods? Field Experimental Evidence From the European Energy Crisis}}},
year = {{2025}},
}
@inbook{61237,
abstract = {{In diesem Beitrag wird zunächst die historische Entstehung von Open Science kurz skizziert und definiert, was unter diesem Begriff zu verstehen ist. Daran anschließend werden die Open-Science-Praktiken Open Data, Open Access, Open Source, Open Methodology und Open Peer Review dargestellt und diskutiert, welche Forschungserkenntnisse zu Open Science vorhanden sind. Im Schluss werden Forschungsdesiderate aufgegriffen und die Implikationen von Open Science für die Wissenschaft erläutert.}},
author = {{Steinhardt, Isabel and Röwert, Ronny}},
booktitle = {{Hochschulforschung}},
editor = {{Pasternack, Peer and Reinmann, Gabi and Schneijderberg, Christian }},
isbn = {{9783748943334}},
keywords = {{Open Data, Open Access, Open Source, Open Methodology, Open Peer Review}},
pages = {{487--496}},
publisher = {{Nomos}},
title = {{{Open Science}}},
doi = {{10.5771/9783748943334-487}},
year = {{2025}},
}
@article{63498,
author = {{Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}},
journal = {{IEEE Transactions on Power Electronics}},
keywords = {{Mathematical models, Estimation, Data models, Convolutional neural networks, Accuracy, Magnetic hysteresis, Magnetic cores, Temperature measurement, Magnetic domains, Temperature distribution, Convolutional neural network (CNN), machine learning (ML), magnetics}},
number = {{2}},
pages = {{3326--3335}},
title = {{{HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}}},
doi = {{10.1109/TPEL.2024.3488174}},
volume = {{40}},
year = {{2025}},
}
@article{58650,
abstract = {{Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.}},
author = {{Gräßler, Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter and Wiederkehr, Petra}},
issn = {{2942-6170}},
journal = {{Industry 4.0 Science}},
keywords = {{AI, artificial intelligence, Data Science, decision support, extreme data, Künstliche Intelligenz, product creation, product development}},
number = {{1}},
publisher = {{GITO mbH Verlag}},
title = {{{Hybrid Decision Support in Product Creation - Improving performance with data science and artificial intelligence}}},
doi = {{10.30844/i4sd.25.1.18}},
volume = {{2025}},
year = {{2025}},
}
@inproceedings{60497,
abstract = {{Despite the advantages that the virtual knowledge graph paradigm has brought to many application domains, state-of-the-art systems still do not support popular graph database management systems like Neo4j. Their query rewriting algorithms focus on languages like conjunctive queries and their unions, which were developed for relational data and are poorly suited for graph data. Moreover, they also limit the expressiveness of the ontology languages that admit rewritings, restricting them to those that enjoy the so-called FO-rewritability property. Rewritings have thus focused on the DL-Lite family of Description Logics. In this paper, we propose a technique for rewriting a family of navigational queries for a suitably tailored fragment of ELHI. Leveraging navigational features in the target query language, we can include some widely-used axiom shapes not supported by DL-Lite. We implemented a proof-of-concept prototype that rewrites into Cypher queries, and tested it on a real-world cognitive neuroscience use case with promising results.}},
author = {{Löhnert, Bianca and Augsten, Nikolaus and Okulmus, Cem and Ortiz, Magdalena}},
booktitle = {{The Semantic Web - 22nd European Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025, Proceedings, Part {I}}},
isbn = {{9783031945748}},
issn = {{0302-9743}},
keywords = {{Ontology-based Data Access, Property Graphs, Navigational Queries}},
location = {{Portorož, Slovenia}},
pages = {{342----361}},
publisher = {{Springer Nature Switzerland}},
title = {{{Towards Practicable Algorithms for Rewriting Graph Queries Beyond DL-Lite}}},
doi = {{10.1007/978-3-031-94575-5_19}},
volume = {{15718}},
year = {{2025}},
}