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The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today’s market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases’ capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from each model’s mapping strategies, as well as a new research topic - mapping multi-model data into relational tables. [ABSTRACT FROM AUTHOR]

In today's highly competitive world, the pursuit of accurate, on-time, up-todate, and complete information has become a cornerstone of the business's success. By gathering and organizing structured and relevant data, different by their nature, origin, volume, and scope, databases remain to be a valuable and trustworthy source of information in many areas. In the sphere of financial management, financial databases play a key role not only in evaluating the financial health of a business and in analyzing a business's performance but also in research and financial modeling. The paper aims to propose a generic relational database design suitable for the acquisition of relevant data about financial statements of business subjects based on therelational data model and using the methodology of designing relational databases, including the first two phases: the conceptual and the logical design. The resulting relational database schema can be easily implemented in any relational database management system, whilst the physically implemented database can provide a solid platform for organizing, storing, processing, and retrieving valuable financial information using SQL scripts. [ABSTRACT FROM AUTHOR]

Universities and accompanying faculties, competing to ensure the highest possible quality and status, face the challenge of being continuously evaluated and ranked, both internally and externally. One of the many criteria in such evaluation is the assessment of key performance indicators (KPIs) vis-à-vis published research papers. The general aim of this paper is the definition of a formal KPI evaluation framework for assessing the research publications (published papers and books), written by researchers in universities, faculties, and other research-oriented institutions. It is accomplished in two steps: (1) by proposing a conceptual and logical design of a generic relational database that can provide a solid foundation for acquisition and management of all relevant data related to research publications, based on the projected corresponding Enhanced EntityRelationship (EE-R) diagram (a conceptual design) and the resulting relational database schema (a logical design); and (2) by addressing relevant KPIs via Structured Query Language (SQL) scripts/queries using the standard SQL notation against the resultant relational design. Since the proposed framework is both generic and platform-independent, it can be easily implemented in various relational database management systems (DBMSs) to provide significant insights into the research performances of the academic staff vis-à-vis their published research publications [ABSTRACT FROM AUTHOR]

Storing XML documents in relational databases has drawn much attention in recent years because it can leverage existing investments in relational database technologies. Different algorithms have been proposed to map XML DTD/Schema to relational schema in order to store XML data in relational databases. However, most work defines mapping rules based on heuristics without considering application characteristics, hence fails to produce efficient relational schema for various applications. In this paper, we propose a workload-aware approach to generate relational schema from XML data and user specified workload. Our approach adopts the genetic algorithm to find optimal mappings. An elegant encoding method and related operations are proposed to manipulate mappings using bit strings. Various techniques for optimization can be applied to the XML to relational mapping problem based on this representation. We implemented the proposed algorithm and our experiment results showed that our algorithm was more robust and produced better mappings than existing work. [ABSTRACT FROM AUTHOR]

With XML adopted as the technology trend on the Internet, and with investment in the current relational database systems, companies must convert their relational data into XML documents for data transmission on the Internet. In the process, to preserve the users' relational data requirements of data constraints into the converted XML documents, we must define a meaningful root element for each XML document. The construction of an XML document is based on the root element and its relevant elements. The root element can be selected from a relational entity table in the existing relational database, which depends on the requirements to present the business behind. The relevant elements are mapped from the related entities, based on the navigability of the chosen entity. The derived root and relevant elements can form a Data Type Definition Graph (DTD-graph) of an XML conceptual schema diagram which can be mapped into a Data Type Definition (DTD) of an XML schema. The result is a translated XML schema with semantic constraints transferred from a relational conceptual schema of an Extended Entity Relationship (EER) model. The data conversion from relational data to the XML documents can be done after the schema translation. The relational data are loaded into XML documents according to the translated DTD. [ABSTRACT FROM AUTHOR]