Converting relational databases into object relational databases

This paper proposes an approach for migrating existing Relational DataBases (RDBs) into Object-Relational DataBases (ORDBs). The approach is superior to existing proposals as it can generate not only the target schema but also the data instances. The solution takes an existing RDB as input, enriches its metadata representation with required semantics, and generates an enhanced canonical data model, which captures essential characteristics of the target ORDB, and is suitable for migration. A prototype has been developed, which migrates successfully RDBs into ORDBs (Oracle 11g) based on the canonical model. The experimental results were very encouraging, demonstrating that the proposed approach is feasible, efficient and correct

Migrating relational databases into object-based and XML databases

Rapid changes in information technology, the emergence of object-based and WWW applications, and the interest of organisations in securing benefits from new technologies have made information systems re-engineering in general and database migration in particular an active research area. In order to improve the functionality and performance of existing systems, the re-engineering process requires identifying and understanding all of the components of such systems. An underlying database is one of the most important component of information systems. A considerable body of data is stored in relational databases (RDBs), yet they have limitations to support complex structures and user-defined data types provided by relatively recent databases such as object-based and XML databases. Instead of throwing away the large amount of data stored in RDBs, it is more appropriate to enrich and convert such data to be used by new systems. Most researchers into the migration of RDBs into object-based/XML databases have concentrated on schema translation, accessing and publishing RDB data using newer technology, while few have paid attention to the conversion of data, and the preservation of data semantics, e.g., inheritance and integrity constraints. In addition, existing work does not appear to provide a solution for more than one target database. Thus, research on the migration of RDBs is not fully developed. We propose a solution that offers automatic migration of an RDB as a source into the recent database technologies as targets based on available standards such as ODMG 3.0, SQL4 and XML Schema. A canonical data model (CDM) is proposed to bridge the semantic gap between an RDB and the target databases. The CDM preserves and enhances the metadata of existing RDBs to fit in with the essential characteristics of the target databases. The adoption of standards is essential for increased portability, flexibility and constraints preservation. This thesis contributes a solution for migrating RDBs into object-based and XML databases. The solution takes an existing RDB as input, enriches its metadata representation with the required explicit semantics, and constructs an enhanced relational schema representation (RSR). Based on the RSR, a CDM is generated which is enriched with the RDB's constraints and data semantics that may not have been explicitly expressed in the RDB metadata. The CDM so obtained facilitates both schema translation and data conversion. We design sets of rules for translating the CDM into each of the three target schemas, and provide algorithms for converting RDB data into the target formats based on the CDM. A prototype of the solution has been implemented, which generates the three target databases. Experimental study has been conducted to evaluate the prototype. The experimental results show that the target schemas resulting from the prototype and those generated by existing manual mapping techniques were comparable. We have also shown that the source and target databases were equivalent, and demonstrated that the solution, conceptually and practically, is feasible, efficient and correct

Performance evaluation of an RDB and an ORDB: A comparative study using the BUCKY benchmark

This paper highlights the functionality of object-based database systems by comparing the performance of relational database (RDB) and object-relational database (ORDB) systems. The study focuses on assessing the efficiency of database systems based on query processing and object complexity. We conducted an experiment that includes running the queries on the RDB and ORDB that were used in the BUCKY benchmark and implemented on Oracle 11g. The findings of this research show that the performance of both database systems depends on various factors, such as the size and type of databases, the schema and query structures, the number of tuples scanned in tables, indexes as well as the environment, in which the experiment was carried out

Evaluating The Accuracy of Classification Algorithms for Detecting Heart Disease Risk

The healthcare industry generates enormous amounts of complex clinical data that make the prediction of disease detection a complicated process. In medical informatics, making effective and efficient decisions is very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely, J48, Random Forest, and Na\"ive Bayes to discover the accuracy of their performance. We also examine the impact of the feature selection method. A comparative and analysis study was performed to determine the best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity and specificity. The importance of using classification techniques for heart disease diagnosis has been highlighted. We also reduced the number of attributes in the dataset, which showed a significant improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart disease was Random Forest with an accuracy of 99.24%

Migrating relational databases into object-based and XML databases

Rapid changes in information technology, the emergence of object-based and WWW applications, and the interest of organisations in securing benefits from new technologies have made information systems re-engineering in general and database migration in particular an active research area. In order to improve the functionality and performance of existing systems, the re-engineering process requires identifying and understanding all of the components of such systems. An underlying database is one of the most important component of information systems. A considerable body of data is stored in relational databases (RDBs), yet they have limitations to support complex structures and user-defined data types provided by relatively recent databases such as object-based and XML databases. Instead of throwing away the large amount of data stored in RDBs, it is more appropriate to enrich and convert such data to be used by new systems. Most researchers into the migration of RDBs into object-based/XML databases have concentrated on schema translation, accessing and publishing RDB data using newer technology, while few have paid attention to the conversion of data, and the preservation of data semantics, e.g., inheritance and integrity constraints. In addition, existing work does not appear to provide a solution for more than one target database. Thus, research on the migration of RDBs is not fully developed. We propose a solution that offers automatic migration of an RDB as a source into the recent database technologies as targets based on available standards such as ODMG 3.0, SQL4 and XML Schema. A canonical data model (CDM) is proposed to bridge the semantic gap between an RDB and the target databases. The CDM preserves and enhances the metadata of existing RDBs to fit in with the essential characteristics of the target databases. The adoption of standards is essential for increased portability, flexibility and constraints preservation. This thesis contributes a solution for migrating RDBs into object-based and XML databases. The solution takes an existing RDB as input, enriches its metadata representation with the required explicit semantics, and constructs an enhanced relational schema representation (RSR). Based on the RSR, a CDM is generated which is enriched with the RDB's constraints and data semantics that may not have been explicitly expressed in the RDB metadata. The CDM so obtained facilitates both schema translation and data conversion. We design sets of rules for translating the CDM into each of the three target schemas, and provide algorithms for converting RDB data into the target formats based on the CDM. A prototype of the solution has been implemented, which generates the three target databases. Experimental study has been conducted to evaluate the prototype. The experimental results show that the target schemas resulting from the prototype and those generated by existing manual mapping techniques were comparable. We have also shown that the source and target databases were equivalent, and demonstrated that the solution, conceptually and practically, is feasible, efficient and correct.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Re-engineering relational databases

This paper surveys the recent literature about various research trends relevant to Relational DataBase (RDB) reengineering. The paper presents an analysis of approaches and techniques used in this context, including construction of object views on top of RDBs, database integration and database migration. A categorisation is presented of the selected work, concentrating on migrating an RDB as a source into object-based and XML databases as targets. Database migration from the source into each of the targets is discussed and critically evaluated, including the semantic enrichment, schema translation and data conversion. Based on a detailed analysis of the existing literature, it seems that the existing work does not provide a complete solution for more than one target database for either schema or data conversion. Besides, none of the existing proposals can be considered as a method for migrating an RDB into an object-relational database. We propose such a method based on an intermediate canonical data model, which enriches the semantics of the source RDB and captures characteristics of the target databases

An Integrated Approach to Relational Database Migration

Relational DataBases (RDBs) are dominant in the market place yet they have limitations in the support of complex structure and user-defined data types provided by relatively recent database technologies (i.e., object-based and XML databases). Such a mismatch inspires work on migrating an RDB into these technologies. The problem is how to effectively migrate existing RDBs, as a source, into the recent database technologies, as targets, and what is the best way to enrich RDBs' semantics and constraints in order to meet the characteristics of these targets? Existing work does not appear to provide a solution for more than one target database. We tackle this question by proposing a solution for migrating an RDB into these targets based on available standards. The solution takes an existing RDB as input, enriches its metadata representation with as much semantics as possible, and constructs an enhanced Relational Schema Representation (RSR). Based on the RSR, a canonical data model is generated, which captures essential characteristics of the target data models that are suitable for migration. A prototype has been implemented, which successfully migrates RDBs into object-oriented, object-relational and XML databases using the canonical data model

Relational Database Migration: A Perspective

This paper presents an investigation into approaches and techniques used for database conversion. Constructing object views on top of a Relational DataBase (RDB), simple database integration and database migration are among these approaches. We present a categorisation of selected works proposed in the literature and translation techniques used for the problem of database conversion, concentrating on migrating an RDB as source into object-based and XML databases as targets. Database migration from the source into each of the targets is discussed in detail including semantic enrichment, schema translation and data conversion. Based on a detailed analysis of the existing literature, we conclude that an existing RDB can be migrated into object-based/XML databases according to available database standards. We propose an integrated method for migrating an RDB into object-based/XML databases using an intermediate Canonical Data Model (CDM), which enriches the source database's semantics and captures characteristics of the target databases. A prototype has been implemented which successfully translates CDM into object-oriented (ODMG 3.0 ODL), object-relational (Oracle 10g) and XML schemas

An algorithm for constructing XML Schema documents from relational databases

The aim of this paper is to present a solution to automatically generate an XML schema from an existing relational database (RDB). The important goal of this translation is to enrich the source schema using semantics that might have not been clearly expressed in it, by acquiring as much information as possible about objects and relationships that exist among them. The next step is to produce an enhanced metadata model, which captures essential characteristics of target XML schema, and is suitable for translation. In details, we present translations of all constructs of an RDB into an XML Schema and integrate these into an algorithm. This process is to simplify exchange of data between different databases, practically the import of data of RDBs into XML documents. A prototype has been developed to realize the algorithm and generate target schema. To validate our proposal, we present experimental results using both schemas. The results show that the proposed algorithm is correct