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"relational database"
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Exploring Large Language Models’ Ability to Describe Entity-Relationship Schema-Based Conceptual Data Models
In the field of databases, Large Language Models (LLMs) have recently been studied for generating SQL queries from textual descriptions, while their use for conceptual or logical data modeling remains less explored. The conceptual design of relational databases commonly relies on the entity-relationship (ER) data model, where translation rules enable mapping an ER schema into corresponding relational tables with their constraints. Our study investigates the capability of LLMs to describe in natural language a database conceptual data model based on the ER schema. Whether for documentation, onboarding, or communication with non-technical stakeholders, LLMs can significantly improve the process of explaining the ER schema by generating accurate descriptions about how the components interact as well as the represented information. To guide the LLM with challenging constructs, specific hints are defined to provide an enriched ER schema. Different LLMs have been explored (ChatGPT 3.5 and 4, Llama2, Gemini, Mistral 7B) and different metrics (F1 score, ROUGE, perplexity) are used to assess the quality of the generated descriptions and compare the different LLMs.
Journal Article
Oracle 12c for dummies
Discover why Oracle is the enterprise database of choice, understand its architecture, and learn to create, implement, and fine-tune an Oracle database. You'll learn what to expect as an Oracle database administrator, and how to utilize the latest security features that ensure data privacy.
Book
Query-based denormalization using hypergraph (QBDNH): a schema transformation model for migrating relational to NoSQL databases
With the emergence of NoSQL databases, many large applications have migrated from relational databases (RDB) due to their superior flexibility and performance. Database migration from RDB to NoSQL databases involves schema transformation and data migration, which is not straightforward. The challenge lies in that RDB stores data in normalized form, whereas NoSQL supports denormalization. To address the challenge of schema transformation, this paper proposes a model called query-based denormalization using hypergraph (QBDNH) from RDB to the NoSQL database. The model takes the inputs from existing relational tables and queries and transforms them into the denormalized NoSQL model using hypergraphs. The approach overcomes limitations like complex relationship representation and data access pattern coverage of existing graph-based denormalization techniques. The proposed model reduces the overall time, cost, and effort needed to transform the schema manually. To validate the effectiveness of QBDNH, the experiments are conducted on the TPC-H dataset, and the performance of QBDNH is compared to existing graph-based denormalization models such as TLD, CLDA, and Kuszera. The evaluation is carried out in two parts: the first part analyzed the query speedup factor, while the second part measured efficiency improvement based on query pipeline execution. The results revealed that QBDNH achieved a notable query performance improvement with speedup factors of 1.29, 1.35, and 1.40 compared to existing TLD, CLDA, and Kuszera models. Furthermore, QBDNH significantly enhanced pipeline utilization compared to TLD and Kuszera.
Journal Article
The Berlin SPARQL Benchmark
The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).
Journal Article
A novel secure data outsourcing scheme based on data hiding and secret sharing for relational databases
Data encryption‐based and secret sharing‐based data outsourcing schemes protect the confidentiality of sensitive attributes but not their secrecy. Ciphertexts/shares generated by a data encryption/secret sharing scheme can attract the attention of interceptors. Thus, it is desired to hide the existence of highly‐sensitive attributes (as secret attributes) in the outsourced relations in addition to protecting their contents. This paper proposes a novel scheme that integrates data hiding with secret sharing for relational databases to protect both the secrecy and confidentiality of secret attributes. It embeds one or multiple secret attributes in a relation into one or multiple cover attributes in the same relation. A set of share (and possibly index) columns are constructed such that they are pretended to be associated with only the cover attributes, while those share columns and some virtual share columns can be used to recover both the secret and cover attributes. What interceptors observe in each relation include the attributes stored in plaintext and the share (and possibly index) columns associated with the cover attributes but not any extra column. Thus, they find nothing suspicious. This is the first effective data hiding scheme for relational databases that protects the secrecy of secret attributes. This paper proposes a novel scheme that integrates data hiding with secret sharing for relational databases to protect both secrecy and confidentiality of secret attributes. To the best of our knowledge, this is the first work that protects the secrecy of secret attributes in relational databases by using data hiding. In addition, it protects the confidentiality of the hidden attributes using secret sharing.
Journal Article
Conceptual Data Modeling Use: A Study of Practitioners
Conceptual data modeling is widely viewed in academia as a critical part of relational database development, essential for reducing project failure risks. Although empirical studies have explored various aspects of its use, research about how frequently Conceptual data modeling is applied in practice and the reasons for its adoption or avoidance are lacking. This paper addresses this gap by presenting a study that evaluates the adoption of conceptual data modeling in the industry. The study begins with practitioner discussions to understand real-world project experiences, revealing a potential discrepancy between what is taught in academic settings and what is practiced in industry. Next, a survey of 485 database professionals is conducted and supplemented by follow-up interviews with 34 professionals. Findings indicate that fewer than 40% of practitioners consistently use formal conceptual data modeling, even in cases when they would like to do so. The survey identifies reasons for not using conceptual data modeling, and the follow-up interviews provide the practitioners with clarifications of the identified barriers. This research finds a positive association between conceptual modeling use and overall satisfaction with the outcome of the database development process. Lastly, the findings of this research offer important implications for both database practitioners and educators.
Journal Article