Logistics organizations depend on software whose quality determines whether digital transformation delivers its operational benefits, yet formal quality evaluation against internationally standardized frameworks is systematically absent in both research and practice. Three interconnected gaps define this research problem: logistics IS practice focuses on functional capabilities rather than quality characteristics; ISO/IEC 25010:2023, the authoritative international standard for software product quality, has never been comprehensively applied to logistics software; and security evaluation remains insufficiently addressed despite mandatory requirements introduced by the EU Cyber Resilience Act. This study addresses these gaps through a systematic literature review conducted under PRISMA 2020 guidelines, encompassing 224 articles from 554 raw records identified across six bibliographic databases — 63% published between 2019 and 2026 — organized into eight thematic groups: blockchain, IoT, Industry 4.0, IT platforms, service quality, cybersecurity, software quality standards, and supply chain digitalization. Of these, only 11 explicitly reference ISO/IEC 25010, and only five apply it directly to logistics software. The review establishes a consistent hierarchy of quality imperatives: compatibility and functional suitability appear in 60% of articles each, reliability in 46%, maintainability in 45%, and interaction capability in 36%, while security appears in only 21% despite its cross-cutting regulatory relevance. These findings provide logistics organizations with an evidence-based quality checklist for software evaluation; developers with design guidance calibrated to logistics demands; and regulators with the empirical foundation for domain-adapted evaluation instruments under the EU Cyber Resilience Act. The study contributes the first systematic literature review explicitly applying a dual logistics-domain and ISO/IEC 25010:2023 lens. On this basis, it proposes a preliminary logistics software quality profile that prioritizes the ISO/IEC 25010 characteristics and links them to logistics indicators, candidate metrics, and evidence types across the principal logistics system types.
Personally identifiable information (PII) detection is foundational to privacy-preserving analytics, compliant data sharing, and safe model training. Traditional rule-based and supervised named entity recognition (NER) pipelines excel on templated fields but are brittle under domain drift and long-tail variants, motivating large language models (LLMs) that generalize via in-context learning. Despite rapid LLM progress, we lack systematic and cross-domain evaluations that reflect redaction-centric error costs and practical deployment constraints. This paper presents an LLM-centric benchmark of PII detection across three textual domains and introduces a carefully annotated multimodal PII dataset to extend beyond text-only settings. Our experiments examine zero-shot and few-shot prompting, quantify prompt sensitivity, and characterize privacy-relevant error modes. We further assess a privacy-oriented, cost-aware baseline that combines a small open-source model with a multi-agent retrieval-augmented workflow, analyzing performance and feasibility for resource-constrained environments. Results highlight domain and modality effects, consistent gains from few-shot prompting (with saturation effects), and precision–recall trade-offs that vary across tasks and models. The proposed agentic retrieval augmented generation (RAG) pipeline approaches, and in some cases exceed the performance of large proprietary models while improving privacy posture and operational cost. The prompts and the multimodal dataset are made publicly available to support reproducibility and future benchmarking.
Ensuring semantic consistency between formal system models and natural-language requirements remains a major bottleneck for scalable Model-Based Systems Engineering (MBSE). While SysML v2 introduces enhanced expressiveness and richer semantics, existing validation approaches primarily focus on syntactic correctness and provide limited support for assessing standards-aligned requirement satisfaction.This paper presents a deterministic multi-agent framework for the automated semantic validation of SysML v2 models, explicitly grounded in requirements engineering standards. The approach decomposes the evaluation process into five specialised reasoning agents covering model abstraction, requirement normalisation, semantic judgement aligned with ISO/IEC/IEEE 29148:2018 “Systems and software engineering — Life cycle processes — Requirements engineering” quality attributes, coverage metric aggregation, and feedback generation with code-level patch synthesis. The architecture is implemented as a typed state machine using LangGraph, ensuring traceable, auditable, and reproducible evaluation workflows.The framework is evaluated on a curated dataset of 14 SysML v2 models and their associated requirements, derived from official specifications. A comparative analysis using two Large Language Models (GPT-4o-mini and DeepSeek-R1) shows that the two backends agree on well-specified models and diverge on under-specified ones, where they exhibit different degrees of strictness when detecting missing or partially satisfied requirements. Results show that the proposed pipeline reliably detects semantic inconsistencies, partial fulfilment, and missing requirements.Beyond evaluation, the system generates standards-aligned diagnostic reports and actionable model improvements, supporting iterative refinement. The proposed approach contributes a scalable and interpretable method for standards-driven semantic validation, bridging the gap between informal requirements and formal system models within modern software and systems engineering workflows.
With the rapid advancement of cloud computing and IoT, outsourcing spatial data computation has become essential yet raises critical privacy concerns, particularly for high-dimensional data with multi-keyword constraints. To tackle these challenges, we propose PPFS, a cloud-edge-end privacy-preserving framework for queries over high-dimensional geospatial data. This framework incorporates a sophisticated enhanced asymmetric scalar-product-preserving encryption (EASPE) algorithm with spatial dimensionality-reduction encoding and enforces fine-grained access control via ciphertext-policy attribute-based encryption, thereby enabling expeditious multi-keyword queries over encrypted high-dimensional data. To further bolster scalability and enhance query efficiency, specifically, we develop an extended scheme, termed PPFS+, which leverages a tree-based index to achieve sub-linear query latency and superior performance. Comprehensive evaluation using large-scale, real-world geospatial datasets demonstrates that our framework achieves retrieval speeds nearly 4 & times; faster than current advanced schemes and realizes higher scalability. Furthermore, rigorous security proofs verify that the proposed schemes achieve IND-CPA and IND-CKA resilience in the generic bilinear group model. These empirical and theoretical results validate that PPFS/PPFS+ provide practical, efficient, and provably secure solutions for authorized high-dimensional geospatial data queries in mobile-edge environments.
This review highlights the findings of studies conducted during 2020-2026 on the application of graph neural networks (GNNs), large language models (LLMs), knowledge graphs, and graph-based retrieval approaches (GraphRAGs) to intelligent information systems. These findings have been grouped into a single study framework, namely the framework for intelligent information systems integration, which covers various aspects of integration. The paper provides a unified systems-level approach to linking architectural design decisions with retrieval and reasoning behavior in graph-language systems. Based on a carefully selected set of system-level studies, the authors show that across the reviewed studies, graph-preserving approaches are reported to improve multi-hop reasoning, evidence localization, and interpretability compared with text-only or graphserialization baselines in several benchmark settings. Good performance is highly dependent on high-quality graph construction, proper retrieval, and the costs of the entire process. The use of graph-language synergy is most effective when the graphs serve as functionally involved parts of retrieval and reasoning, rather than merely as background information.
