While knowledge graphs have emerged as a powerful paradigm for representing and querying interconnected data, distributing them across multiple untrusted nodes introduces significant challenges in guaranteeing query result integrity. We propose a verifiable query processing framework for distributed property graphs that constructs a Merkle Patricia Trie (MPT) over distributed data chunks, enabling efficient cryptographic proof generation and client-side verification without requiring full data retrieval. Experimental evaluation confirms that the verification overhead remains practical, and the framework scales effectively to realistic graph workloads.
Evaluating machine learning models that generate time-dependent predictions in clinical settings remains challenging, as conventional metrics such as time-dependent AUC rely on observed event information and do not account for undocumented intermediate clinical events. In this work, we propose a novel evaluation framework based on class–cluster alignment. The method uses over-time clustering to identify structural patterns in such predictions and introduces alignment-based scores that quantify temporal separation between outcome groups. We evaluate the proposed framework on real-world clinical data from mortality prediction after allogeneic hematopoietic stem cell transplantation. Results show that the proposed scores capture temporally consistent separation patterns that complement conventional evaluation metrics. Overall, the proposed framework provides a novel perspective for evaluating time-dependent risk estimates in longitudinal clinical settings.
Join order (JO) is a fundamental task of query optimizers, as it significantly influences query response time. Determining the optimal JO is an NP-hard problem. Several cost-based algorithms have been proposed. The rise of AI has led to learned-based JO techniques, but their limitations with dynamic workloads have prompted the development of hybrid approaches that combine the strengths of both cost-based and learned methods. This evolution has prompted researchers to investigate the effectiveness of learned solutions compared to cost-based ones, from only a response time perspective. Meanwhile, the database research community is increasingly focusing on energy efficiency (EE) as a critical aspect of system design and optimization. In light of this trend, we argue that a comparative analysis of cost-based JO (CBJO), learned-based JO (LBJO), and hybrid JO from an EE perspective is essential. In this paper, we first present the ecosystem of JO selection, highlighting its major components. Next, we provide an end-to-end experimental study of AlphaJoin (a representative LJO), HybridQO, and PostgreSQL, using the STACK and JOB datasets to evaluate both query response time and energy consumption. We conclude by emphasizing the main findings: LJO methods often trade fast query response for higher energy use and exhibit less maturity and stability than CBJO approaches. HybridQO demonstrates adaptability by switching to CBJO when needed, thus balancing efficiency and robustness across varying workloads.
This paper presents a novel approach to automatic classification of tattoo descriptions extracted from historical prison documents and investigates their utility in record linkage. We utilize transformer-based models to generate both binary and multi-label classification systems for these unique textual descriptions. The generated tattoo labels are then integrated into a comprehensive record linkage workflow, serving as an additional feature alongside existing attributes. We evaluate this integration within both a classical machine learning framework (random forests) and a deep learning record linkage system (Ditto), and find that tattoo labels can contribute positively to record linkage performance in certain configurations. In addition, tattoo labels show potential as a supplement to traditional blocking keys, offering a path toward more efficient record linkage without sacrificing pair quality and pair completeness. This research demonstrates how automatically classified textual data can be incorporated into historical record linkage pipelines, and highlights the broader potential of leveraging unconventional descriptive attributes in this domain.
Entity Resolution (ER) is the task of identifying records that refer to the same real-world entity. A fundamental challenge in data lakes, where heterogeneous datasets lack shared identifiers, producing duplicates and inconsistencies that undermine analytics and data governance. Existing ER approaches treat attribute contributions implicitly, offering no transparent or auditable reasoning over attribute-level evidence. We revisit ER through the lens of Kolmogorov-Arnold Networks (KAN) and propose KAN-ER, a framework operating on attribute-level similarity vectors that learns nonlinear matching decisions. Experiments on twelve datasets show that KAN-ER outperforms DeepMatcher on structured data and achieves competitive F1-scores with DITTO. KAN’s spline functions provide interpretable per-attribute insights critical for data governance. To the best of our knowledge, this is the first application of KAN to the ER task.
