Hassan Reza

Mohamad Zamini, Hassan Reza, Minou Rabiei

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. View Full-Text

Ismail Gargouri, Hassan Reza

Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anomalies. In contrast, both a manually expanded comparator and the proposed LLM-augmented configuration detected all 16, representing a 128.57% relative improvement in detection rate over the baseline. Post-migration cross-store validation confirmed exact agreement across all three curated tables. Of 25 LLM-generated test assertions, 9 were classified as useful, 4 as redundant, and 12 as executable but low-value. The complete workflow executed in 106.58 seconds across eight instrumented pipeline stages. These results demonstrate that LLM-driven semantic test synthesis can materially strengthen validation coverage while remaining operationally practical for production ELT environments.

Reid Anderson, Hassan Reza

The design of a system and its implementation are two tasks often carried out by different individuals on a development team, and can occur weeks or months apart. This creates a potential for divergence between real behavior and the designed model that an implementation is intended to match. Particularly as time passes and individuals who were present for the original conception of the design leave, a system can lose coherence and drift from intended design principles. Even with a robust system design, more is needed to ensure that the key implementation details match the design and that adherence to a particular strategy is not lost over time. This paper proposes an approach to address that concern for distributed systems using conformance checking, a methodology borrowed from process mining. Distributed traces produced by instrumented applications are evaluated for conformance by comparison to design traces. The resulting conformance percentage is a quantitative metric that can be tracked over time to determine how closely a concrete implementation corresponds to the key attributes of the expected design model. This analysis is done using the dominant industry standard, OpenTelemetry, and so should apply to a wide range of distributed systems.