OMCAT

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.

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.

On the development of the database currently has become a very important thing, each will plan something, consider any thing we will need and use existing data in a database, The development of the database currently has become a very important thing, when we want to plan something and consider everything, we will need and use existing data in a database, PostgreSQL is one Object Relational Database Management Systems (DBMS) open source with a lot of features that are not less sophisticated to commercial databases. On daily use, it sometimes occurs a case that requires a dynamic column, therefore we should make the new columns to meet those needs. However, if each row of the table column requires different column and potentially need another ones, then it is going to be useless. On the research of utilizing the technology of JavaScript Object Notation (JSON),Json_encode PHP programming language can simplify the use of the column so that a new column is no longer needed when we want to create a new column on the table, we only need a new column that stores an array that has been converted into a JSON string forms the result of json_encode that represents the column name and the content of the columns of a table.

Large image databases have been designed and used in various applications and in content-based retrieval systems. Medical Imaging modalities have been developed in the past each uniquely designed to specific imaging modality based on the image pathobiological medium. This paper presents an approach to improve the process of image storage and retrieval based on image data representation model that integrates both metadata and content description. The low level features and the labels associated with the salient objects extracted from the mages are stored in an ORDBMS (Object Relational Database Management systems) using Oracle. Our technique, which combines the image descriptors obtained by a unified approach phases of image segmentation and annotation, has a good time complexity and the experimental results on several image datasets.

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.

In IT industry research communities and data scientists have observed that Big Data has challenged the legacy of solutions. ‘Big Data’ term used for any collection of data or data sets which is so large and complex and difficult to process and manage using traditional data processing applications and existing Relational Data Base Management Systems (RDBMSs). In Big Data; the most important challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization and privacy. As the data increases in various dimensions with various features like structured, semi structured and unstructured with high velocity, high volume and high variety; the RDBMSs face another fold of challenges to be studied and analyzed. Due to the aforesaid limitations of RDBMSs, data scientists and information managers forced to rethink about alternative solutions for handling such data with 3Vs.Initially research study focused on to develop an intelligent base for decision makers so that alternative solutions for long term suitable solutions and handle the data and information with 3Vs can be designed. In this research attempts has been made to analyze the feature based capabilities of RDBMSs and then performance experimentation, observation and analysis has been done with Big Data handling tools and technologies. The features considered for scientific observation and analysis were resource consumption, execution time, on demand scalability, maximum data size, structure of the data, data visualization, and ease of deployment, cost and security. Finally the research provides a decision support metrics for decision makers in selecting the appropriate tool or technology based on the nature of data to be handled in the target organizations.

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.

Geospatial data in databases have shifted to conform to the characteristics of big-data in tandem with the development of the Internet, mobile Internet, cloud computing, and especially, spatial data acquisition technologies. Faced with tackling spatial big data, traditional spatial database management techniques based on Relational Database Management Systems have encountered problems including the unstructured characteristics of the spatial object, the high scalability of storage capacity, and the high concurrency in big data application environment. This paper focuses on the mainstream of NoSQL databases that successfully deal with unstructured big data and are widely used in Internet applications, but lack of spatial characteristics. The data operational and query modes cannot meet the requirments of GIS applications. To resolve this problem, this paper proposes a strategy that takes a NoSQL database as a warehouse for spatial big data and a traditional spatial database as the application server. The storage system architecture and the key technology and solutions are discussed. A prototype system was developed based on MongoDB, PostgreSQL and SQLite to verify the feasibility and effectiveness of the strategy.

Object-oriented models have rapidly become the model of choice for programming most new computer applications. Since most application programs need to deal with persistent data, adding persistence to objects is essential to making object-oriented applications useful in practice. There are three classes of solutions for implementing persistence in object-oriented applications: the gateway-based object persistence approach, which involves adding object-oriented programming access to persistent data stored using traditional non-object-oriented data stores, the object-relational database management system (DBMS) approach, which involves enhancing the extremely popular relational data model by adding object-oriented modeling features, and the object-oriented DBMS approach (also called the persistent programming language approach), which involves adding persistence support to objects in an object-oriented programming language. In this paper, we describe the major characteristics and requirements of object-oriented applications and how they may affect the choice of a system and method for making objects persistent in that application. We discuss the user and programming interfaces provided by various products and tools for object-oriented applications that create and manipulate persistent objects. In addition, we describe the pros and cons of choosing a particular mechanism for making objects persistent, including implementation requirements and limitations imposed by each of the three approaches to object persistence previously mentioned. Given that several object-oriented applications might need to share the same data, we describe how such applications can interoperate with each other. Finally, we describe the problems and solutions of how object-oriented applications can coexist with non-object-oriented (legacy) applications that access the same data.

In the last two decades, unstructured information has become a major challenge in information management. Such challenge is caused by the massive and increasing amount of information resulting from the conversion of almost all daily tasks into digital format. Tools and applications are necessary in organizing unstructured information, which can be found in structured data, such as in relational database management systems (RDBMS). RDBMS has robust and powerful structures for managing, organizing, and retrieving data. However, structured data still contains unstructured information. In this paper, the methods used for managing unstructured data in RDBMS are investigated. In addition, an incremental and dynamic repository for managing unstructured data in relational databases are introduced. The proposed technique organizes unstructured information through linkages among textual data based on semantics. Furthermore, it provides users with a good picture of the unstructured information. The proposed technique can rapidly and easily obtain useful data, and thus, it can be applied in numerous domains, particularly those who deal with textual data, such as news articles.KeywordsRelational databasesUnstructured dataDocument clusteringQuery efficiencyTextual data

Integrating Data-Mining Support into a Brain-Image Database Using Open-Source Components

DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic datasets. Specifically, there is a need for handling strings with variable sizes while keeping their biological meaning. Relational database management systems (RDBMS) provide several data types that could be further explored for the genomics context. Besides, they enforce integrity, consistency, and enable good abstractions for more conventional data. We propose the relational text data type to represent and manipulate biological sequences and their derivatives. We present a logical schema for representing the core biological information, which may be inferred from a given biological conceptual data schema and the corresponding function manipulations. We implement and evaluate these stored functions into an actual RDBMS for both efficacy and efficiency. We show that it is possible to enforce basic and complex requirements for the genomic domain. We claim that the well-established relational text data type in RDBMS may appropriately handle the representation and persistency of biological sequences. We base our approach on the idea of domain-specific abstract data types that can store data with semantically defined functions while hiding those details from non-technical end-users.

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

A complex network models relationships among data elements in several applications, such as social networks, urban street organization, disease monitoring and treatment co-occurrences, and communication and transport infrastructures. To store and manage large volumes of data, the Relational Database Management Systems (RDBMSs) provide efficient disk-based data management, being present in the most diverse application domains, including enterprise information, finance, medical or clinical support, and in airline reservations and scheduling. However, RDBMSs still face challenges to efficiently process queries that "navigate" over complex networks represented as graphs. Those queries, named in this work as graph queries, are often expressed by recursive queries, which execute multiple join operations between an internal, temporary table that stores intermediary results and the table that stores the graph's edges. This Ph.D. dissertation focuses on developing methods and specialized data structures based on adjacency-lists aiming at improving graph query performance in an RDBMS through recursive queries. This project's main goal targets the question: "How can we improve the performance of queries over graph data executed in a RDBMS?". To answer it, we proposed specializations over each table involved in a recursive query: the table of edges E, and the temporary result table T. The modifications in table T consists of adding extra measurement columns to enable employing it in multiple graph queries executed in a single database connection. In its turn, table E is specialized to allow storing multiple edges into a single row optimized for the execution of each join operation of the recursive query. Experiments using data from RDBMSs modeled as a graph allow concluding that: (i) enriching table T provides increased efficiency to execute multiple graph queries, such as the query types Single-Source Shortest Path (SSSP), Connected Components (CC), and PageRank (PR); (ii) grouping multiple edges from table E into a single row significantly improves the performance of individual recursive queries, reducing not only their elapsed time but also the required size to store the graph data; and (iii) employing Clustered Tables to group the edges into a single page also enables improved performance for recursive queries, with the advantage of requiring just a few changes in the SQL statements. Overall, specializing both tables T and E helps diminishing the gap of efficiently executing graph queries in a RDBMS.

For more than 40 years, the relational database management system (RDBMS) and the atomicity, consistency, isolation, durability (ACID) transaction guarantees provided through its use have been the standard for data storage. The advent of Big Data created a need for new storage approaches that led to NoSQL technologies, which rely on basic availability, soft-state, eventual consistency (BASE) transactions. Over the last decade, NewSQL RDBMS technology has emerged, providing the benefits of RDBMS ACID transaction guarantees and the performance and scalability of NoSQL databases. The reliance on virtualization in IT has continued to grow, but an investigation of current academic literature identified a void regarding the performance impact of virtualization of NewSQL databases. To help address the lack of research in this area, a quantitative experimental study was designed and carried out to answer the central research question, "What is the performance impact of Type-I virtualization on a NewSQL RDBMS?" VMware ESXi virtualization software, NuoDB RDBMS, and OLTP-Bench software were used to execute a mixed-load benchmark. Performance metrics were collected comparing bare metal and virtualized environments, and the data analyzed statistically to evaluate five hypotheses related to CPU utilization, memory utilization, disk and network input-output (I/O) rates, and database transactions per second. Findings indicated a negative performance impact on CPU and memory utilization, as well as network I/O rates. Performance improvements were noted in disk I/O rates and database transactions-per-second.