arXiv subjects

Piotr Sowinski

Publications and source records attributed to Piotr Sowinski.

15 recordsLinked to original sources

arXiv2026

LinkML-Scala: a Robust, Fast, and Portable Implementation of LinkML

LinkML is a unified framework for data and domain modeling that spans diverse formats and ecosystems: JSON, RDF, CSV, SQL, spreadsheets and more. However, until now it had only one fully-featured implementation, written in Python, which is limited in terms of performance, portability, and behavior consistency. This restricts LinkML's usability in settings such as real-time schema editing and enterprise server applications. To address these issues, we present LinkML-Scala: a robust, fast, and portable implementation of LinkML that covers the metamodel, runtime support, schema derivation, and generators for JSON Schema, SHACL, RDFS, and Table Schema. Written in Scala 3, it runs in the browser (JavaScript transpilation), on the JVM, and as native binaries. LinkML-Scala is distributed as an in-browser playground, a CLI application, a GitHub CI Action, and JVM / JavaScript libraries. In our benchmarks, it outperforms the Python implementation in every tested scenario, on average by 22.9--38.5x. We consider LinkML-Scala an important contribution toward increasing LinkML's adoption and we outline a plan for further work to ensure implementation interoperability and stability.

cs.DB↗

arXiv2026

It's Time to Standardize RDF Messages

RDF-based systems increasingly operate in event-driven and streaming settings, where producers and consumers exchange data as discrete units of communication rather than as freely mergeable RDF statements. As existing RDF semantics and tooling do not provide an interoperable notion of what statements belong together as one message, developers often rely on out-of-standard techniques, transport-level assumptions, or heuristics, leading to interoperability problems and inefficiencies. We propose the concept of an RDF Message as an RDF Dataset intended to be interpreted atomically as a single communicative act, laying the foundation for defining RDF Message Streams and RDF Message Logs. The proposal makes message boundaries explicit across serializations, transport, and storage systems, which in turn enables incremental consumption and reproducible replay in use cases such as IoT observations, archived RDF streams, nanopublications, or processing SPARQL CONSTRUCT results. Building on this, RDF Message Profiles, such as Linked Data Event Streams or ActivityStreams, then provide the terms for describing pagination, message structure, ordering, or retention policies. As part of the W3C Community Group on RDF Stream Processing, we are now seeking broader support and comments on the proposal from the Semantic Web community.

cs.DB↗

arXiv2025

Jelly-Patch: a Fast Format for Recording Changes in RDF Datasets

Recording data changes in RDF systems is a crucial capability, needed to support auditing, incremental backups, database replication, and event-driven workflows. In large-scale and low-latency RDF applications, the high volume and frequency of updates can cause performance bottlenecks in the serialization and transmission of changes. To alleviate this, we propose Jelly-Patch -- a high-performance, compressed binary serialization format for changes in RDF datasets. To evaluate its performance, we benchmark Jelly-Patch against existing RDF Patch formats, using two datasets representing different use cases (change data capture and IoT streams). Jelly-Patch is shown to achieve 3.5--8.9x better compression, and up to 2.5x and 4.6x higher throughput in serialization and parsing, respectively. These significant advancements in throughput and compression are expected to improve the performance of large-scale and low-latency RDF systems.

cs.DB↗

arXiv2025

Jelly: a Fast and Convenient RDF Serialization Format

Existing RDF serialization formats such as Turtle, N-Quads, and JSON-LD are widely used for communication and storage in knowledge graph and Semantic Web applications. However, they suffer from limitations in performance, compression ratio, and lack of native support for RDF streams. To address these shortcomings, we introduce Jelly, a fast and convenient binary serialization format for RDF data that supports both batch and streaming use cases. Jelly is designed to maximize serialization throughput, reduce file size with lightweight streaming compression, and minimize compute resource usage. Built on Protocol Buffers, Jelly is easy to integrate with modern programming languages and RDF libraries. To maximize reusability, Jelly has an open protocol specification, open-source implementations in Java and Python integrated with popular RDF libraries, and a versatile command-line tool. To illustrate its usefulness, we outline concrete use cases where Jelly can provide tangible benefits. We consider that by combining practical usability with state-of-the-art efficiency, Jelly is an important contribution to the Semantic Web tool stack.

cs.DB↗

arXiv2025

Representing and querying data tensors in RDF and SPARQL

Embedding tensors in databases has recently gained in significance, due to the rapid proliferation of machine learning methods (including LLMs) which produce embeddings in the form of tensors. To support emerging use cases hybridizing machine learning with knowledge graphs, a robust and efficient tensor representation scheme is needed. We introduce a novel approach for representing data tensors as literals in RDF, along with an extension of SPARQL implementing specialized functionalities for handling such literals. The extension includes 36 SPARQL functions and four aggregates. To support this approach, we provide a thoroughly tested, open-source implementation based on Apache Jena, along with an exemplary knowledge graph and query set.

cs.DB↗

arXiv2024

Realizing a Collaborative RDF Benchmark Suite in Practice

Collaborative mechanisms allow benchmarks to be updated continuously and adjust to the changing requirements and new use cases. This paradigm is employed for example in the field of machine learning, but up until now there were no examples of truly open and collaborative benchmarks for RDF systems. In this demo paper we present the collaboration functionalities of RiverBench, an open, multi-task RDF benchmark suite. Owing to its fully open and community-driven design, RiverBench allows any researcher or practitioner to submit a new dataset or benchmark task, report performed benchmark runs, and edit any resource in the suite. RiverBench's collaboration system is itself based on RDF and Linked Data mechanisms, and every resource in the suite has machine-readable RDF metadata. The showcased functionalities together make up a first-of-a-kind fully open and collaborative RDF benchmark suite. These features are meant to encourage other researchers to contribute to RiverBench, and make it a long-term project sustained by the community.

cs.DB↗

arXiv2024

Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future

The landscape of computing technologies is changing rapidly, straining existing software engineering practices and tools. The growing need to produce and maintain increasingly complex multi-architecture applications makes it crucial to effectively accelerate and automate software engineering processes. At the same time, artificial intelligence (AI) tools are expected to work hand-in-hand with human developers. Therefore, it becomes critical to model the software accurately, so that the AI and humans can share a common understanding of the problem. In this contribution, firstly, an in-depth overview of these interconnected challenges faced by modern software engineering is presented. Secondly, to tackle them, a novel architecture based on the emerging WebAssembly technology and the latest advancements in neuro-symbolic AI, autonomy, and knowledge graphs is proposed. The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins, which model the software throughout all stages of its lifecycle. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user. The concept takes a leap beyond the current software engineering approaches, addressing some of the most urgent issues in the field. Finally, the efforts towards realizing the proposed approach as well as future research directions are summarized.

cs.SE↗

arXiv2024

Not All RDF is Created Equal: Investigating RDF Load Times on Resource-Constrained Devices

As the role of knowledge-based systems in IoT keeps growing, ensuring resource efficiency of RDF stores becomes critical. However, up until now benchmarks of RDF stores were most often conducted with only one dataset, and the differences between the datasets were not explored in detail. In this paper, our objective is to close this research gap by experimentally evaluating the load times of eight diverse RDF datasets from the RiverBench benchmark suite. In the experiments, we use five different RDF store implementations and several resource-constrained hardware platforms. To analyze the results, we introduce the notion of relative loading speed (RLS), allowing us to observe that the loading speed can differ between datasets by as much as a factor of 9.01. This serves as clear evidence that ``not all RDF is created equal'' and stresses the importance of using multiple benchmark datasets in evaluations. We outline the possible reasons for this drastic difference, which should be further investigated in future work. To this end, we published the data, code, and the results of our experiments.

cs.DB↗

arXiv2024

RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice

Over the years, RDF streaming was explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming systems, due to the lack of a common language. This work attempts to address this critical research gap, by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided, to foster the adoption of the ontology. Three use cases for the ontology are presented with accompanying competency questions, demonstrating the usefulness of the resource. Additionally, this work introduces a novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The results of a multifaceted evaluation of the resource are presented, testing its logical validity, use case coverage, and adherence to the community's best practices, while also comparing it to other works. RDF-STaX is expected to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.

cs.DB↗

arXiv2023

RiverBench: an Open RDF Streaming Benchmark Suite

RDF streaming has been explored by the Semantic Web community from many angles, resulting in multiple task formulations and streaming methods. However, for many existing formulations of the problem, reliably benchmarking streaming solutions has been challenging due to the lack of well-described and appropriately diverse benchmark datasets. Existing datasets and evaluations, except a few notable cases, suffer from unclear streaming task scopes, underspecified benchmarks, and errors in the data. To address these issues, we propose RiverBench, an open and collaborative RDF streaming benchmark suite. RiverBench leverages continuous, community-driven processes, established best practices (e.g., FAIR), and built-in quality guarantees. The suite distributes datasets in a common, accessible format, with clear documentation, licensing, and machine-readable metadata. The current release includes a diverse collection of non-synthetic datasets generated by the Semantic Web community, representing many applications of RDF data streaming, all major task formulations, and emerging RDF features (RDF-star). Finally, we present a list of research applications for the suite, demonstrating its versatility and value even beyond the realm of RDF streaming.

cs.DB↗

arXiv2023

Autonomous Choreography of WebAssembly Workloads in the Federated Cloud-Edge-IoT Continuum

The concept of the federated Cloud-Edge-IoT continuum promises to alleviate many woes of current systems, improving resource use, energy efficiency, quality of service, and more. However, this continuum is still far from being realized in practice, with no comprehensive solutions for developing, deploying, and managing continuum-native applications. Breakthrough innovations and novel system architectures are needed to cope with the ever-increasing heterogeneity and the multi-stakeholder nature of computing resources. This work proposes a novel architecture for choreographing workloads in the continuum, attempting to address these challenges. The architecture that tackles this issue comprehensively, spanning from the workloads themselves, through networking and data exchange, up to the orchestration and choreography mechanisms. The concept emphasizes the use of varied AI techniques, enabling autonomous and intelligent management of resources and workloads. Open standards are also a key part of the proposition, making it possible to fully engage third parties in multi-stakeholder scenarios. Although the presented architecture is promising, much work is required to realize it in practice. To this end, the key directions for future research are outlined.

cs.DC↗

arXiv2023

Towards Edge-Cloud Architectures for Personal Protective Equipment Detection

Detecting Personal Protective Equipment in images and video streams is a relevant problem in ensuring the safety of construction workers. In this contribution, an architecture enabling live image recognition of such equipment is proposed. The solution is deployable in two settings -- edge-cloud and edge-only. The system was tested on an active construction site, as a part of a larger scenario, within the scope of the ASSIST-IoT H2020 project. To determine the feasibility of the edge-only variant, a model for counting people wearing safety helmets was developed using the YOLOX method. It was found that an edge-only deployment is possible for this use case, given the hardware infrastructure available on site. In the preliminary evaluation, several important observations were made, that are crucial to the further development and deployment of the system. Future work will include an in-depth investigation of performance aspects of the two architecture variants.

cs.CV↗

arXiv2022

Efficient RDF Streaming for the Edge-Cloud Continuum

With the ongoing, gradual shift of large-scale distributed systems towards the edge-cloud continuum, the need arises for software solutions that are universal, scalable, practical, and grounded in well-established technologies. Simultaneously, semantic technologies, especially in the streaming context, are becoming increasingly important for enabling interoperability in edge-cloud systems. However, in recent years, the field of semantic data streaming has been stagnant, and there are no available solutions that would fit those requirements. To fill this gap, in this contribution, a novel end-to-end RDF streaming approach is proposed (named Jelly). The method is simple to implement, yet very elastic, and designed to fit a wide variety of use cases. Its practical performance is evaluated in a series of experiments, including end-to-end throughput and latency measurements. It is shown that Jelly achieves vastly superior performance to the currently available approaches. The presented method makes significant progress towards enabling high-performance semantic data processing in a wide variety of applications, including future edge-cloud systems. Moreover, this study opens up the possibility of applying and evaluating the method in real-life scenarios, which will be the focus of further research.

cs.DB↗

arXiv2022

Ontology Reuse: the Real Test of Ontological Design

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect potential issues are proposed, including natural language processing and network analysis. Moreover, several suggestions for improving ontologies and their quality assurance processes are presented. It is argued that while the advancing automatic tools for ontology quality assurance are crucial for ontology improvement, they will not solve the problem entirely. It is ontology reuse that is the ultimate method for continuously verifying and improving ontology quality, as well as for guiding its future development. Specifically, multiple issues can be found and fixed primarily through practical and diverse ontology reuse scenarios.

cs.SE↗

arXiv2022

Topical Classification of Food Safety Publications with a Knowledge Base

The vast body of scientific publications presents an increasing challenge of finding those that are relevant to a given research question, and making informed decisions on their basis. This becomes extremely difficult without the use of automated tools. Here, one possible area for improvement is automatic classification of publication abstracts according to their topic. This work introduces a novel, knowledge base-oriented publication classifier. The proposed method focuses on achieving scalability and easy adaptability to other domains. Classification speed and accuracy are shown to be satisfactory, in the very demanding field of food safety. Further development and evaluation of the method is needed, as the proposed approach shows much potential.

cs.CL↗