Data exploration on large amount of relational data through keyword queries

The paper describes a new approach for querying relational databases through keyword search by exploting Information Retrieval (IR) techniques. When users do not know the structures and the content, keyword search becomes the only efficient and effective solution for allowing people exploring a relational database. The approach is based on a unified view of the database relations (performed through the full disjunction operator), where its composing tuples will be considered as documents to be indexed and searched by means of an IR search engine. Moreover, as it happens in relational databases, the system can merge the data stored in different documents for providing a complete answer to the user. In particular, two documents can be joined because either their tuples in the original database share some Primary Key or, always in the original database, some tuple is connected by a Primary / Foreign Key Relation. Our preliminary proposal, the description of the tabular data structure for storing and retrieving the possible connections among the documents and a metrics for scoring the results are introduced in the paper

A data transformation model for relational and non-relational data

The information systems that support small, medium, and large organisations need data transformation solutions from multiple data sources to fulfill the requirements of new applications and decision-making to stay competitive. Relational data is the foundation for the majority of applications programme, whereas non-relational data is the foundation for the majority of newly produced applications. The relational model is the most elegant one; nonetheless, this kind of database has a drawback when it comes to managing very large volumes of data. Because they can handle massive volumes of data, non-relational databases have evolved into relational database substitutes. The key issue is that rules for data transformation processes across various data types are becoming less well-defined, leading to a steady decline in data quality. Therefore, to handle relational and non-relational data and satisfy the requirements for data quality, an empirical model in this domain knowledge is required. This study seeks to develop a data transformation model used for different data sources while satisfying data quality requirements, especially the transformation processes in relational and non-relational model, named Data Transformation with Two ETL Phases and Central-Library (DTTEPC). The different stages and methods in the developed model are used to transform the metadata information and stored data from relational to non-relational systems, and vice versa. The model is developed and validated through expert review, and the prototype based on the final version is employed in two case studies: education and healthcare. The results of the usability test demonstrate that the developed model is capable of transforming metadata data and stored data across systems. So enhancing the information systems in various organizations through data transformation solutions. The DTTEPC model improved the integrity and completeness of the data transformation processes. Moreover, supports decision-makers by utilizing information from various sources and systems in real-time demands