Related papers: On the effects of logical database design on datab…
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…
Database normalization is the one of main principles for designing relational databases. The benefits of normalization can be observed through improving data quality and performance, among the other qualities. We explore a new context of…
In modern databases, the practice of data normalization continues to be important in improving data integrity, minimizing redundancies, and eliminating anomalies. However, since its inception and consequent improvements, there have been no…
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…
Normalized relational databases are a common method for storing data, but pulling out usable denormalized data for consumption generally requires either direct access to the source data or creation of an appropriate view or table by a…
Many sorts of structured data are commonly stored in a multi-relational format of interrelated tables. Under this relational model, exploratory data analysis can be done by using relational queries. As an example, in the Internet Movie…
Natural Language Interfaces for Databases (NLIDBs) aim to make database querying accessible by allowing users to ask questions in everyday language rather than using formal SQL queries. Despite significant advancements in translation…
Traditionally, it was accepted that a relational database can be normalized step-by-step, from a set of un-normalized tables to tables in $1NF$, then to $2NF$, then to $3NF$, then (possibly) to $BCNF$. The rule applied to a table in $1NF$…
Context: Information Technology consumes up to 10\% of the world's electricity generation, contributing to CO2 emissions and high energy costs. Data centers, particularly databases, use up to 23% of this energy. Therefore, building an…
One of the distinctive features of Information Retrieval systems comparing to Database Management systems, is that they offer better compression for posting lists, resulting in better I/O performance and thus faster query evaluation. In…
With the continuous increase of online services as well as energy costs, energy consumption becomes a significant cost factor for the evaluation of data center operations. A significant contributor to that is the performance of database…
Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined heuristics and statistical models. Improving these heuristics requires substantial engineering…
Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems. We propose a SQL-based approach that involves (1) generating a 3NF schema from Wikipedia infoboxes, (2) generating SQL queries, and (3) query…
It is commonly accepted in the practice of on-line analytical processing of databases that the multidimensional database organization is less scalable than the relational one. It is easy to see that the size of the multidimensional…
In the past few years, the number of OLAP applications increased quickly. These applications use two significantly different DB structures: multidimensional (MD) and table-based. One can show that the traditional model of relational…
Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as "List/Count all female athletes who were born in 20th century",…
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in parsing textual data and generating code. However, their performance in tasks involving tabular data, especially those requiring symbolic reasoning,…
Database replication is an important component of reliable, disaster tolerant and highly available distributed systems. However, data replication also causes communication and processing overhead. Quantification of these overheads is…
Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR estimation field. Among most of the proposed deep neural…
Modern RDBMSs support the ability to compress data using methods such as null suppression and dictionary encoding. Data compression offers the promise of significantly reducing storage requirements and improving I/O performance for decision…