Bio-Strings: A Relational Database Data-Type for Dealing with Large Biosequences

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

Relational database management systems (RDBMSs) have been a common option to manage structured data over the past decades. In recent years, with the prevalence of big data applications, vast unstructured and semi-structured data are generated, deeply challenging the relational model used in RDBMSs. For this reason, a wide spectrum of NoSQL databases are developed for managing unstructured, semi-structured or structured data. For example, graph database management systems (GDBMSs) are commonly used as an important category of NoSQL databases, to manage sophisticated graph data as well as relational data. Nonetheless, as claimed in existing literatures, both RDBMSs and GDBMSs are capable of managing graph data and relational data, the boundaries of them still remain unclear. In this paper, we propose a unified benchmark for RDBMSs and GDBMSs, to evaluate them under the same metrics, and report which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join and deep recursive operations, (3) GDBMSs and RDBMSs are comparable for two-table join and shallow recursive operations.

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

An overview of the Object Protocol Model (OPM) and the OPM data management tools

3D protein structure similarity searching is one of the most popular processes performed in the structural bioinformatics. The process is usually performed through dedicated websites or desktop software tools, which makes a secondary processing of the search results difficult. One of the alternatives is to store macromolecular data in relational databases and perform the similarity searching on the server-side of the client-server architecture. Unfortunately, relational database management systems (DBMSs) are not designed for efficient storage and processing of biological data, such as 3D protein structures. In this paper, we present the P3D-SQL extension to the Oracle PL/SQL language that allows invoking protein similarity searching in SQL queries and perform the process efficiently against a database of 3D protein structures. Availability: P3D-SQL is available from P3D-SQL project home page at: http://zti.polsl.pl/w3/dmrozek/science/p3dsql.htm

In the last few years, databases have become very important and very large because they play a strategic and important role in most organizations and because they receive a huge flow of information from multiple sources every moment in building BigData. This situation identified several limitations and weaknesses in relational database management systems (RDBMS), such as availability, real-time response, horizontal scalability, decision support, advanced data analysis, and especially the management of Bigdata which can reach zeta bytes in storage. This requires the storage and organization of this data in a new management system database not fixed by a rigid structure and resolves all problems associated with the storage of database in a relational system. In this view, organizations need a new NoSQL (not only SQL) system that overcomes the limitations of the relational system. The change of the database management system requires the migration of the databases from the relational system to another NoSQL, taking into consideration all stored data and keeping the majority of the possibilities and functionalities of the old system, with all the advantages of the new system. In this paper, we will identify the elements of relational databases that belong to nature: data, structure, and semantics, which we must migrate to a NoSQL system, such as a document-oriented system. Also in this paper, we will present the rules for transforming the structure of the relational system to another document-oriented NoSQL, according to the principal’s basics of a new approach to migration.KeywordsMigration DataBaseNoSQLDatabaseTransformation rulesBigData

The data volume for the resource description framework (RDF) is growing rapidly. To query this large amount of data, two types of query processing approaches are there: the relational approach and graph-based approach. The relational approach offers better scalability and good security, but it suffers from query joins. The graph-based approach helps to eliminate query joins. Both relational and graph-based approach have their own advantages. This paper presents the performance analysis of relational database management system (RDBMS) and graph database management system (GDBMS) in terms of query processing. The evaluation of query execution time (QET) is carried out for the same benchmark linked observation data (LOD). A detailed comparison is carried out using two popular graph-based tools, Neo4j, and DGraph with relational database PostgreSQL. Neo4j uses cipher-query language, and DGraph uses GraphQL+−, and PostgreSQL uses standard SQL language for the RDBMS. The experiments are performed for four different types of queries. It has been observed that RDBMS outperforms GDBMS for star and range queries, and graph database works well with join and projection queries. We have extended our evaluation for the comparison of available graph tools, and it has been found that DGraph outperforms Neo4j for all the types of queries and works 2.5% faster for the query execution time as well.KeywordsCypherGraph databaseGraphQL+−RDFSQL

NoSQL databases are suitable for network traffic loads experienced by today's websites. The important attributes that make them a favorable choice are inherent distributed nature, horizontal scalability and flexibility in data models. Hence, more data is now getting handled through NoSQL databases and sensible information extraction from these data stores is becoming a requirement. Information extraction may require sourcing data from multiple data sources, establishing relationship among them and querying across these data sources together. Ontology based semantic integration systems for Relational Database Management Systems (RDBMS) already exist that satisfies the above requirement. Many commercial systems are operational based on the above technique and tools and solutions for the above approach are very much mature. Hence, mapping the processing done in each stage of RDBMS based semantic integration systems to that of NoSQL systems can facilitate usage of existing tools and frameworks. The purpose of this work is to develop ontology based semantic integration system for a column-oriented NoSQL data store like HBase which is similar in architectural design to RDBMS.

Relational Database Management Systems (RDBMS) have been very successful at managing structured data with well-defined schemas. Despite this, relational systems are generally not the first choice for management of data where schemas are not pre-defined or must be flexible in the face of variations and changes. Instead, No-SQL database systems supporting JSON are often selected to provide persistence to such applications. JSON is a light-weight and flexible semi-structured data format supporting constructs common in most programming languages. In this paper, we analyze the way in which requirements differ between management of relational data and management of JSON data. We present three architectural principles that facilitate a schema-less development style within an RDBMS so that RDBMS users can store, query, and index JSON data without requiring schemas. We show how these three principles can be applied to industry-leading RDBMS platforms, such as the Oracle RDBMS Server, with relatively little effort. Consequently, an RDBMS can unify the management of both relational data and JSON data in one platform and use SQL with an embedded JSON path language as a single declarative language to query both relational data and JSON data. This SQL/JSON approach offers significant benefits to application developers as they can use one product to manage both relational data and semi-structured flexible schema data.

Interactive use of relational database management systems (DBMS) requires a user to be knowledgeable about the semantics of the application represented in the database. In many cases, however, users are not trained in the application field and are not DBMS experts. Two categories of functionality are problematic for such users: (1) updating a database without violating integrity constraints imposed by the domain and (2) using join operations to retrieve data from more than one relation. We have been conducting research to help an uninformed or casual user interact with a relational DBMS. This paper describes two capabilities to aid an interactive database user who is neither an application specialist nor a DBMS expert. We have developed deferred Referential Integrity Checking (RIC) and Intelligent Join (IJ) which extend the operations of a relational DBMS. These facilities are made possible by explicit representation of database semantics combined with a relational schema. Deferred RIC is a static validation procedure that checks uniqueness of tuples, non-null keys, uniqueness of keys, and inclusion dependencies. IJ allows a user to identify only the “target” data which is to be retrieved without the need to additionally specify “join clauses”. In this paper we present the motivation for these facilities, describe the features of each, and present examples of their use.

The architecture of Geographic Information Systems (GISs) is changing: more and more systems are based on the integrated architecture, i.e. storing geometric data in the Data Base Management System (DBMS) together with administrative data. The first step in building a Geo-DBMS is by having data types and operators for simple features (i.e. geometric primitives): point, line and polygon. This has reached a level of standardisation and is now implemented in several commercial DBMSs. The next step is to have support for the topologically structured features in the DBMS, i.e. complex features. The DBMS can then check and guarantee consistency. In addition, complex operations can be executed within the DBMS. Despite the fact that topologically structured models are well known and that it is not difficult to store the topological references, it still remains an unresolved issue as to how to effectively implement these models within a relational DBMS. In this paper, we describe the design and implementation of a topologically structured management at the DBMS level. Our focus is to translate topological structures into geometric primitives. It is then possible to define a DBMS view on a topological primitive, which makes this appear as a geometric primitive. This process supports the best of both worlds: on the one hand there are advantages of the topological structure (no redundancy) and on the other hand the ease of explicit geometric primitives in querying, analysis and presentation is available.

One of the most important qualities of a database management system running on a small system is its ability to provide data independence. A relational database management system provides data independence through its use of tables for data storage. Use of a standard operating system means that a database management system can be run on a wide variety of systems, allowing data to be transported not only between small systems but between small and large systems.Assuming that a database management system provides data independence, what other features are needed? A comparison of the features provided by two such database management systems and how easy they are to use would be helpful in answering this question. This paper presents a comparison of UNIFY and Intel Database Management System (iDB). Both are relational, multi-user systems that run under the UNIX operating system.

Call me Ishmael. We are about to learn about the White Whale: the Relational Database Management System (RDBMS). RDBMS is the most common type of data storage in business today, because it is a proven and well-supported technology. In this chapter, I will discuss what a relational database is and how you can use one. I will try to touch on most aspects of the relational database, but a relational database is too large of a whale to cover in one chapter.