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About 3,928 papers for “Database normalization”

Mexican Scientific Diaspora Normalization Database

Rafael Marmolejo · Figshare · 2015

This is the Normalization Database employed for the Plos One Paper: Mobility and international collaboration: case of the Mexican scientific diaspora

Cited by 0Open access

Mexican Scientific Diaspora Normalization Database

Rafael Marmolejo · Figshare · 2015

This is the Normalization Database employed for the Plos One Paper: Mobility and international collaboration: case of the Mexican scientific diaspora

Cited by 0Open access

Technical debt in relational database normalization

Mashel Albarak · University of Birmingham Institutional Research Archive (University of Birmingham) · 2020

The technical debt metaphor describes trade-offs between short-term goals and applying optimal design and development practices. In this thesis, we explore the concept of technical debt in database design and provide an understanding of its causes and types. We focus on a specific type of debt related to database normalization. Database normalization is one of the main principles for designing relational databases, with the objective of improving data and system qualities. However, developers often ignore the normalization process due to the time and expertise it requires, introducing technical debt into the system. Paying the debt and refactoring the database for normalization can be costly, if the benefits of the exercise are not justified. We consider database normalization debts are likely to be incurred for tables below the fourth normal form. To manage the debt, we propose a systematic framework to rank the candidate tables for normalization to the fourth normal form. The ranking is based on the tables estimated impact on data quality, performance, maintainability, and cost. We also propose two approaches to identify potential debt tables below fourth normal form. The techniques are evaluated using an industrial case study of a database-backed web application for human resource management. The results show that the debt-aware approach can provide an informed justification for the inclusion of critical tables to be normalized, while reducing the effort and cost of normalization.

Cited by 0

WikiTND24: A Chinese Text Normalization Database

Wu-Hao Li, Chen-Yu Chiang · 2024

Labeling large datasets for constructing a text normalization system is cumbersome and time-consuming. Although some self-supervised learning models can reduce the amount of training data needed, labeling sufficient data still requires significant labor and time. To address this issue, we used a self-developed rule-based text normalization system to nor-malize Chinese Wikipedia texts, generating a large amount of training data to develop a non-standard word recognition model. Besides verifying the performance of our model with the FlatTN database test set, we also demonstrated that it can help detect errors in the rule-based system and accelerate the development of a data-driven or machine-learning-based text normalization system. Additionally, we are making this database, WikiTND24, publicly available for everyone to use.

Cited by 0

Biological Database Normalization by Sequence Alignment

2009

The Michigan Molecular Interactions (MiMI) database contains protein interaction data from many distinct sources. Frequently, the same protein is referred to in different external databases by different identifiers, so it is difficult to determine when different records refer to the same object. The normalization problem is addressed by creating an extended MiMI database that integrates MiMI with the large publicly available protein sequence databases and using BLAST to enable the user to find interactions for proteins with high similarity to any given query protein. I discuss the challenges of rigorously evaluating this approach but conclude that it should prove to be a useful complement to MiMI. A prototype of the system is available for use at

Cited by 0

Scalable Database Normalization Powered by the Crowd

GSS Aditya Sairam, Parasuram Kolli, Anudeep Immidisetty et al. · 2020

In the age of information, there is a growing need to process data, both structured and unstructured, with ease and accuracy. Such an intensive task might be achieved through machine learning based applications, but not always. Moreover, the major problems with existing methodologies are scalability and complexity. Crowdsourcing is an alternate solution that deploy mechanisms for breaking the major task at hand into smaller segments (microtasks) requiring quick (often binary) decisions to be exercised by the amateur human participants. The use of such collective and collaborative intelligence can simplify few, if not most, of the difficult database management tasks. In this paper, we address one such problem, precisely determining functional dependencies (FDs) in a database. Our motivation is to pursue scalable normalization of a database through a game-like crowdsourcing approach. The FD realization is an interesting outcome of our research.

Cited by 0

Exploring Database Normalization Effects on SQL Generation

Ryosuke Kohita · 2025

Schema design, particularly normalization, is a critical yet often overlooked factor in natural language to SQL (NL2SQL) systems. Most prior research evaluates models on fixed schemas, overlooking the influence of design on performance. We present the first systematic study of schema normalization's impact, evaluating eight leading large language models on synthetic and real-world datasets with varied normalization levels. We construct controlled synthetic datasets with formal normalization (1NF-3NF) and real academic paper datasets with practical schemes. Our results show that denormalized schemas offer high accuracy on simple retrieval queries, even with cost-effective models in zero-shot settings. In contrast, normalized schemas (2NF/3NF) introduce challenges such as errors in base table selection and join type prediction; however, these issues are substantially mitigated by providing few-shot examples. For aggregation queries, normalized schemas yielded better performance, mainly due to their robustness against the data duplication and NULL value issues that cause errors in denormalized schemas. These findings suggest that the optimal schema design for NL2SQL applications depends on the types of queries to be supported. Our study demonstrates the importance of considering schema design when developing NL2SQL interfaces and integrating adaptive schema selection for real-world scenarios.

Cited by 0

Normalization of plant database of a Herbarium

Omolehin, J. O., Oladipupo, O. O., Oyelade, O. J. · Covenant University Repository (Covenant University)

In the present world, Relational Database model is becoming more focusing model among others; just because of it’s interesting. When features designing a relational database, we need to be aware of all kind of anomalous situations that can arise as a result of the structure of data we are working with. These anomalies can seriously affect the integrity of the database. The process of normalization can minimize the impact of the various anomalies.

Cited by 0

Normative database normalization with 46 healthy eyes.

Juan Xu (225010), Hiroshi Ishikawa (282324), Gadi Wollstein (169992) et al. · Figshare · 2015

<p>(A, B) mean and standard deviation (SD) of retinal nerve fiber layer (RNFL) thickness measurement at each sampling point (A-scan), without normalization. (C, D) mean and SD of RNFL thickness measurement after normalizing individual’s retinal nerve fiber bundle path location to population’s average location. The variations of RNFL thickness were larger at superior temporal and inferior temporal regions (brighter blue in B) because of the population variation of the bundle locations. After aligning the bundle locations and normalizing the RNFL thickness map, the RNFL thickness variations at these two regions were markedly reduced (dark blue in D).</p>

Cited by 0Open access

Database Normalization via Dual-LLM Self-Refinement

Jo, Eunjae, Lee, Nakyung, Kim, Gyuyeong · ArXiv.org · 2025

Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed manually by data engineers. To this end, we present Miffie, a database normalization framework that leverages the capability of large language models. Miffie enables automated data normalization without human effort while preserving high accuracy. The core of Miffie is a dual-model self-refinement architecture that combines the best-performing models for normalized schema generation and verification, respectively. The generation module eliminates anomalies based on the feedback of the verification module until the output schema satisfies the requirement for normalization. We also carefully design task-specific zero-shot prompts to guide the models for achieving both high accuracy and cost efficiency. Experimental results show that Miffie can normalize complex database schemas while maintaining high accuracy.

Cited by 0Open access