Database Systems Journal

ISSN 2069 - 3230

The journal is published under the sponsorship of
The Bucharest University of Economic Studies
and it is produced by the university's own publishing division,
The Bucharest University of Economic Studies Publishing House


Database Systems Journal, Vol. XVII, 2026


Open PDF Journal


CONTENTS


1. A Hybrid Approach to Real-Time Recommendations using Heterogeneous Data Processing and Distributed Predictive Models (p. 1-9)
Diana-Andreea CAUNIAC, The Bucharest University of Economic Studies, Romania
Simona-Vasilica OPREA, The Bucharest University of Economic Studies, Romania
As e-commerce platforms scale, the gap between what users expect and what static recommendation models can deliver has become hard to ignore. This paper describes the design and implementation of a hybrid recommendation system built on a distributed cloud infrastructure, tested on a dataset of over 3.7 million products, 1.9 million reviews, and 1.1 million user interactions. The system combines collaborative filtering through matrix factorization with sentiment analysis, emotion detection, and topic modeling applied to user reviews, identifying six recurring themes that reflect real purchasing experiences. Geographic proximity and recent behavioral signals are incorporated as contextual features to further refine recommendations. The stack includes Google BigQuery, Vertex AI Feature Store, Pinecone, Apache Kafka, Apache Flink, and BigQuery ML. A feedback loop ties user interactions back to model updates, keeping recommendations relevant as behavior changes. Results suggest that decoupling the processing pipelines reduces latency without sacrificing recommendation quality.
Keywords: recommendation systems, real-time processing, hybrid architecture, collaborative filtering, heterogeneous data, sentiment analysis, topic modeling, distributed computing
2. Data Preparation with Oracle Cloud (p. 10-19)
Cristiana COSTAN, The Bucharest University of Economic Studies, Romania
This article examines the Extract-Transform-Load (ETL) process as an essential method for preparing data from warehouses for analysis. It also covers the difficulties of combining data from numerous external sources, converting it for consistency, and storing it in the cloud for effective querying. Oracle Cloud Infrastructure (OCI) provides a range of services for ETL workflows, including Oracle Data Integrator (ODI), which offers a variety of code-based data processing options, and Data Transforms, also known as ODI Web, a modern no-code solution designed to simplify and automate data transformations and machine learning tasks. A practical example using a League of Legends dataset illustrates how Data Transforms and Autonomous Data Warehouse can be used for effective data preparation and analysis. The goal of this paper is to assist data scientists in simplifying and improving data workflows through the use of modern cloud ETL solutions.
Keywords: Data Warehouse, ETL, Oracle Cloud, Data Integration, Machine Learning
3. Data Visualization in Business Intelligence: A Comparison Between Power BI and Qlik Cloud Analytics (p. 20-28)
Anda-Elena SPATARU, The Bucharest University of Economic Studies, Romania
Florin-Răzvan SOARE, The Bucharest University of Economic Studies, Romania
The visualization component within Business Intelligence (BI) systems plays an essential role in transforming operational data into actionable insights for the decision-making process. BI tools facilitate data interpretation and the formulation of evidence-based conclu-sions, supporting organizations in monitoring performance through clear metrics and adapt-ing rapidly to changes in the competitive environment, including in low-latency information contexts. From this perspective, the article presents general BI concepts and proposes a comparative analysis of two representative platforms, Microsoft Power BI and Qlik Cloud Analytics, highlighting their capabilities and limitations based on relevant evaluation criteria.
Keywords: Business Intelligence, Microsoft Power BI, Qlik Cloud Analytics, Analytics and Reporting Platforms, BI Architecture, Data Warehouse, Data Mart, Comparative Analysis
4. An Intelligent Recommendation System Built on Emotional Analysis in a Kappa Architecture (p. 29-35)
Diana-Andreea CAUNIAC, The Bucharest University of Economic Studies, Romania
Adela BARA, The Bucharest University of Economic Studies, Romania
Recommendation systems have become a cornerstone of modern e-commerce, directly shaping user experience and conversion rates. Yet most conventional approaches rely solely on behavioral history, clicks, views, purchases, without any awareness of how a user is feeling in the moment of decision. This paper presents an intelligent recommendation system that fills that gap by weaving emotional analysis of text reviews into a real-time data-processing pipeline. The solution is built on a Kappa architecture and leverages Apache Kafka, Google Cloud BigQuery, Bigtable, BigQuery ML, and a RoBERTa-based NLP model for emotion detection. Users are grouped into clusters through K-Means segmentation according to their emotional profiles and recommendations are then derived from well-established correlations between emotional states and product categories. In controlled evaluation, the system achieved a Precision@5 of 0.98, a Recall@5 of 0.94, and an F1-score of 0.96, confirming the strength of the proposed approach.
Keywords: Recommendation systems, sentiment analysis, BigQuery ML, Apache Kafka, Kappa architec-ture, RoBERTa, natural language processing, personalized recommendations
5. SQL vs NoSQL in Polyglot Persistence Architectures (p. 36-53)
Florin-Răzvan SOARE, The Bucharest University of Economic Studies, Romania
Anda-Elena SPATARU, The Bucharest University of Economic Studies, Romania
Miruna SOSEA, The Bucharest University of Economic Studies, Romania
Polyglot persistence is increasingly adopted because large-scale applications combine correctness-critical state with high-throughput, latency-sensitive workloads that cannot be served efficiently by a single datastore. This paper contrasts SQL and NoSQL systems in terms of transactional guarantees, recovery behavior, data modeling, query expressiveness, schema evolution, and operational scaling constraints. Based on this comparison, we derive datastore selection criteria that distinguish system-of-record components from derived serving models. We then discuss integration mechanisms for polyglot architectures, emphasizing explicit data ownership, change propagation via CDC and log-based replication, and saga-style coordination with compensations to manage cross-store failures.
Keywords: Polyglot persistence, SQL, NoSQL, scalability, CDC, sagas, ACID, BASE