Performance Analysis Depend on OODB Instance Based on ebXML

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.

Databases are used in many facets of society by an array of people. While the obvious platform for most database applications has been a relational Data Base management Systems (DBMS), the object-oriented programming languages which are widely used in software development are not well matched to their development. In many ways it is obvious that there is an ‘object-relational’ impedance and mismatch. There are currently only a few courses about object-oriented database development in universities. In this paper, an object-oriented database named db4o is introduced as a viable alternative to traditional database development methods. It is now used in our newly developed object-oriented database course. With a visual object management viewer, db4o helps students to understand basic object-oriented database conception and to grasp programming with the object-oriented programming language.

Translating object-oriented database transactions into relational transactions

Object databases have been introduced since 1987. The first version of ODMG standard appeared in 1991, and the new release 2.0 is from 1997. During ten year period, object database technology has received considerable attention and improvements. Today products are mature and proved their efficiency in many real applications (telecommunication, energy distribution, spatial data management, Web, etc.) where gigabytes of data are manipulated. The goal of this paper is to present two key subjects: (i) why the ODMG standard is crucial, and (ii) how to implement an open architecture to connect object and relational databases.

the massive volume of data in databases, web pages, and document files usually causes information to be disorganized and unclear for the user. Therefore, information in such an environment can be classified into three forms: structured, semistructured, or unstructured. Structured information is the best form of information because it facilitates the acquisition and comprehension of knowledge. Relational Database Management System (RDBMS) has a robust structure that manages, organizes and retrieves data. There are many attempts have been made in order to deal with such data. These attempts can be categorized into three groups: within a database schema, by a developed data model within the database, or by query-based techniques in database. Nonetheless, RDBMS contain massive amount of unstructured data such as textual data.. This paper proposed Textual Virtual Schema Model (TVSM). TVSM is conducted to perform semantic textual data linking and clustering and is embedded in the relational database structure (schema). In addition, linking and converting the unstructured information to structured data. Quality improvement of textual data clusters. Achievement of high query processing efficiency in retrieving data clusters. TVSM initially developed to assist researchers, developers, and database administrators who are concerned on unstructured information management, information extraction, multi-document clustering, information retrieval, query processing efficiency, personal information management, question answering, information integration, news tracking, and news summarization

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 object-oriented anthropometric database for developing a man model

B2B 전자상거래와 같이 XML.을 이용한 정보교환이 확산되고 있으며 이에 따라 상호 교환되는 정보에 대하여 체계적이며 안정적인 저장관리가 요구되고 있다. 이를 위해 XML 응용과 데이터베이스 연계를 위한 다양한 연구가 관계형 데이터베이스를 중심으로 수행되었다. 그러나 계층구조를 갖는 XML 데이터를 2차원 테이블의 집합인 관계형 정보로 표현하는 관계형 데이터베이스로의 저장에는 본질적인 한계가 있어, 계층구조를 지원하는 객체지향 데이터베이스로의 저장이 요망된다. 이에 따라 계층구조를 갖는 XML 데이터를 객체지향 데이터 베이스로 지장하기 위한 모델링 방안이 요구된다. 본 논문에서는 객체모델을 토대로 DTD를 객체 지향 데이터베이스 스키마로 변환하기 위한 방법을 제안한다. 이를 위하여 먼저 DTD를 객체모델로 변환시키기 위한 객체변환 방안을 제시하고, 변환된 객체모델을 객체지향 데이터베이스 스키마로 변경시키기 위한 스키마 변환 방법을 제안했다. Information exchange on XML such as B2B electronic commerce is spreading. Therefore the systematic and stable management mechanism for storing the exchanged information is needed. For this goal there are many research activities for connection between XML application and relational database. But because XML data have hierarchical structures and relational database can store only flat-structured data, we need to store XML data in object-oriented database that support hierarchical structure. Accordingly the modeling methodology for storing XML data in object-oriented database is needed. In this parer, the transforming methods based on object model from XML DTD to object-oriented database schema is proposed. For this, we first introduce mapping methods that map XML DTD to object model and then we propose soem methods that transform from the object model to object-oriented database schema.

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

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.

We discuss the important database theme of Data Independence and how object oriented database applications are failing to perceive its benefits, and instead put too much reliance on encapsulation. The P/FDM object-oriented database is based on the functional data model and has a modular design based on data independence, which allows alternative kinds of object storage to be used. In this work, a relational database management system has been used to provide object storage, and we describe how the data access routines have been implemented. The principal query language used with P/FDM is Daplex, which is normally translated to Prolog, including calls to the basic data access routines. The query is optimised to minimise the expected number of calls. This gives very general method execution and pattern matching search. However, much better performance can be achieved for simpler data-intensive Daplex queries against a relational storage module by translating these to a single SQL statement, which is possible because of data independence.

Assets are critical working tools in any organisation; their technical nature makes them significant for the effective outcome of strategic designs in today’s fast-growing technological age; hence, proper management of AIS is important for an increase in organisational performance. Such conventional approaches as relational databases or data warehousing are not entirely suitable for dealing with the problem intricacy, size and volatility of today’s connected data. This paper focuses on the assessment of the traditional and novel perspectives of data relationship management in AIS. It also compares traditional approaches like Relational and Object-oriented databases with new/Emerging technologies like Graph databases which optimise data complexity and semantic web technologies for better integration of the data. Also, the integration of advanced computational technologies such as machine learning and artificial intelligence improves predictive analysis and data mining to promote decision-making and organisational performance. Advanced technologies, such as blockchain and cloud applications, advance asset management even more because of their enhanced security, clarity, and expansiveness in approaching relevant databases. Blockchain enhances the features of decentralised record-keeping and immutability, while cloud solutions are scalable storage and computational resources. This paper considers problems like integration, data quality, data scalability and security and also provides some recommendations for further studies to solve these problems. Through examining these developments, the wish for the paper is to contribute to the improvement of asset management as well as decisions in this sphere in the context of growing concern and popularity of technologies.

In this paper, we develop techniques to produce interoperable queries with object and relational databases. A user poses a local query in a local query language, against a local object or relational schema. We transparently produce appropriate queries with respect to a remote target object or relational schema, corresponding to some remote database which contains data relevant to the user's query. Mapping knowledge to resolve representational heterogeneities in local and remote schemas is expressed in a canonical representation, CRmapping, and is independent of the particular data model. A canonical representation CRquery is also used to resolve heterogeneities of query languages. A set of heterogeneous transformation algorithms define the appropriate transformations from the local queries to the remote queries. The use of canonical representations (CR) allows us to represent queries independent of the particular query language, and to resolve representational conflicts in a uniform manner, independent of models and query languages.

A query under an object-oriented data model exhibits characteristics which one finds in a relational query and those which clearly separate it from a relational query. The differences in turn have interesting impacts on the complexity of and strategy for query evaluation in object-oriented databases. The authors develop a model of a query under an object-oriented data model, and analyze the fundamental differences in the semantics of queries in object-oriented databases and relational databases. >