Using Actors to Build a Parallel DBMS

In this paper, we present the design and the architecture of a parallel main memory database management system. We focus on concurrency control scheme and recovery. Our prototype is based on the concept of “database actors”, an object-oriented data model well suited for parallelmanipulations. The storage sub system is built upon distributed Ram-files using SDDS (Scalable Distributed Data Structures) techniques. A nested transaction model is proposed and used to handle concurrency access and recovery. We have also proposed novel approach, based on wait-die, to implement a distributed deadlock prevention technique for our model of nested transactions

Associative access in persistent object stores : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Sciences in Information Systems at Massey University

Page 276 missing from original copy.The overall aim of the thesis is to study associative access in a Persistent Object Store (POS) providing necessary object storage and retrieval capabilities to an Object Oriented Database System (OODBS) (Delis, Kanitkar & Kollios, 1998 cited in Kirchberg & Tretiakov, 2002). Associative access in an OODBS often includes navigational access to referenced or referencing objects of the object being accessed (Kim. Kim. & Dale. 1989). The thesis reviews several existing approaches proposed to support associative and navigational access in an OODBS. It was found that the existing approaches proposed for associative access could not perform well when queries involve multiple paths or inheritance hierarchies. The thesis studies how associative access can be supported in a POS regardless of paths or inheritance hierarchies involved with a query. The thesis proposes extensions to a model of a POS such that approaches that are proposed for navigational access can be used to support associative access in the extended POS. The extensions include (1) approaches to cluster storage objects in a POS on their storage classes or values of attributes, and (2) approaches to distinguish references between storage objects in a POS based on criteria such as reference types - inheritance and association, storage classes of referenced storage objects or referencing storage objects, and reference names. The thesis implements Matrix-Index Coding (MIC) approach with the extended POS by several coding techniques. The implementation demonstrates that (1) a model of a POS extended by proposed extensions is capable of supporting associative access in an OODBS and (2) the MIC implemented with the extended POS can support a query that requires associative access in an OODBS and involves multiple paths or inheritance hierarchies. The implementation also provides proof of the concepts suggested by Kirchberg & Tretiakov (2002) that (1) the MIC can be made independent from a coding technique, and (2) data compression techniques should be considered as appropriate alternatives to implement the MIC because they could reduce the storage size required

Performance Comparison of Hibernate and EclipseLink Technologies for Mapping an Object-Oriented Model to a Relational Database

Different tiers of modern applications are built using object-oriented programming for implementing business logic and the relational database model for data storage. To solve the impendence mismatch issue that arises between the object model and relational schema, various Object-Relational Mapping (ORM) tools have been designed. In this research, the performance of two open source ORM technologies, namely Hibernate and EclipseLink, is investigated. Hibernate is a well established middleware solution while EclipseLink, which stems from Oracle’s TopLink, is a new product on the persistence landscape. For the purpose of this research, a web-based application was developed and used as a test system. The performance test facilities were integrated into design of the application. The abstract layer introduced into application’s architecture with the Spring Data Access Object (DAO), made the system highly modular allowing easily switching between persistence technologies with no alterations in the rest of the application code

Performance Analysis of Java Persistence API Providers

Nowadays, fast and accurate access to data is very important. Usually data is managed and processed through software applications. In recent years, the most preferred programming model by most application developers is Object Oriented Programming (OOP) where data is represented through objects. These data must be persistent and therefore needs to be stored, and storage can be done on a variety of databases. The most common databases are Relational Database Management Systems (RDBMS). While persistence of objects in RDBMS is limited by object-relational mismatch which is the inconsistency of the direct interaction between two components based on different approaches, OOP object on one side and RDBMS table data on the other, Object-relational mapping (ORM) can be used as a solution. ORM maps the data stored in database tables into the application objects. In other words, ORM persists data from application environment to that of the database. In this paper, we use the Java Persistence API (JPA) specification which provides the characteristics of the ORM technique for developing Java applications. A comparison of three JPA providers was performed by implementing three JPA applications in order to conclude which JPA provider has a better performance

An improved method for database design.

Chan, Chi Wai Alan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 121-126).Abstracts in English and Chinese.Abstract --- p.vAcknowledgements --- p.viiiList of Figures --- p.ixList of Tables --- p.xiChapter 1. --- Introduction --- p.12Chapter 1.1. --- Object-oriented databases --- p.12Chapter 1.2. --- Object-oriented Data Model --- p.14Chapter 1.3. --- Class and Object Instances --- p.15Chapter 1.4. --- Inheritance --- p.16Chapter 1.5. --- Constraint --- p.18Chapter 1.6. --- Physical Design for OODB Storage --- p.19Chapter 1.7. --- Problem Description --- p.20Chapter 1.8. --- Genetic Algorithm --- p.22Chapter 1.8.1. --- Constraint Handling Methods in GA --- p.25Chapter 1.9. --- Contributions of this work --- p.27Chapter 1.10. --- Outline of this work --- p.30Chapter 2. --- Literature Review --- p.32Chapter 2.1. --- Object-oriented database --- p.32Chapter 2.2. --- Object-Oriented Data model --- p.33Chapter 2.3. --- Physical Storage Model for OODBs --- p.35Chapter 2.3.1. --- Home Class (HC) Model --- p.36Chapter 2.3.2. --- Repeated Class (RC) Model --- p.38Chapter 2.3.3. --- Split Instance (SI) Model --- p.39Chapter 2.4. --- Solving physical storage design for OODBs --- p.40Chapter 2.5. --- Transaction-Based Approach --- p.41Chapter 2.6. --- Minimize database operational cost --- p.42Chapter 2.7. --- Combinational Optimization Method --- p.43Chapter 2.8. --- Research in Genetic Algorithm --- p.46Chapter 2.9. --- Implementation in GA --- p.47Chapter 2.10. --- Fitness function --- p.49Chapter 2.11. --- Crossover operation --- p.50Chapter 2.12. --- Encoding and Representation --- p.51Chapter 2.13. --- Parent Selection in Crossover Operation --- p.52Chapter 2.14. --- Reproductive selection --- p.53Chapter 2.14.1. --- Selection of Crossover Operator --- p.54Chapter 2.14.2. --- Replacement --- p.54Chapter 2.15. --- The Use of Constraint Handling Method --- p.55Chapter 2.15.1. --- Penalty function --- p.56Chapter 2.15.2. --- Decoder gives instruction to build feasible solution --- p.57Chapter 2.15.3. --- Adjustment method --- p.58Chapter 3. --- Solving Physical Storage Problem for OODB using GA --- p.60Chapter 3.1. --- Physical storage models for OODB --- p.61Chapter 3.2. --- Database operation for transactions --- p.62Chapter 3.3. --- Properly designed physical storage structure --- p.68Chapter 3.4. --- Fitness Evaluation --- p.69Chapter 3.5. --- Initial population --- p.72Chapter 3.6. --- Cross-breeding --- p.72Chapter 3.7. --- GA Operators --- p.74Chapter 3.8. --- Physical Design Problem Formulation for GA --- p.75Chapter 3.9. --- Representation and Encoding --- p.75Chapter 3.10. --- Solving Physical Storage Problem for OODB in GA --- p.76Chapter 3.10.1. --- Representation of design solution --- p.76Chapter 3.10.2. --- Encoding --- p.78Chapter 3.10.3. --- Initial population --- p.80Chapter 3.10.4. --- Parent Selection for breeding --- p.80Chapter 3.11. --- Traditional Constraint handling method --- p.83Chapter 3.11.1. --- Improve the Performance of Inheritance Constraint Handling methods --- p.85Chapter 3.12. --- Weakness in Gorla's GA approach --- p.87Chapter 4. --- Proposed Methodology --- p.88Chapter 4.1 --- Enhanced Crossover Operator --- p.90Chapter 4.2. --- Infeasible Solutions and Enhanced Adjustment Method --- p.93Chapter 4.3. --- Propagation Adjustment Method --- p.97Chapter 5. --- Computational Experiments --- p.99Chapter 5.1. --- Introduction --- p.99Chapter 5.2. --- Experiment Objective --- p.101Chapter 5.3. --- Tools and Setup --- p.102Chapter 5.4. --- Crossover Operator --- p.105Chapter 5.5. --- Mutation Operator --- p.105Chapter 5.6. --- Termination condition --- p.106Chapter 5.7. --- Computational Experiments --- p.107Chapter 5.7.1. --- An Illustrative Example ´ؤ UNIVERSITY database --- p.107Chapter 5.7.2. --- Simulation ´ؤ 9 classes and 25 classes --- p.115Chapter 5.7.3. --- Result --- p.116Chapter 6. --- Conclusions --- p.118Chapter 6.1. --- Summary of Achievements --- p.118Chapter 7. --- Bibliography --- p.121Chapter 8. --- Appendix --- p.12

Implementasi Client-Server Pada Sistem Informasi Pengolahan Nilai Siswa Menggunakan Object-Oriented Programming

ABSTRACTThe use of information technology in educational institutions be necessary for work to be efficient and flexible. The process of reporting student data and assessment results at SMA Negeri 1 Dharmasraya is still manual using paper (paper-based), and there is no specific storage for the database. To make reports on student learning outcomes and share resources easier then an information system was built on a client-server network using object-oriented programming with a centralized database so that the stored data can be organized. The build systems using the System Development Life Cycle waterfall model, which starts from the analysis, design, coding, testing, and maintenance. The results of this study are the establishment of a multiuser system that implements the use of client-server computer networks to process student grades.Key Words: Client-Server, Information Systems, OO

JAS-mine: A new platform for microsimulation and agent-based modelling

We introduce JAS-mine, a new Java-based computational platform that features tools to support the development of large-scale, data-driven, discrete-event simulations. JAS-mine is specifically designed for both agent-based and microsimulation modelling, anticipating a convergence between the two approaches. An embedded relational database management system provides tools for sophisticated input-output communications and data storage, allowing the power of relational databases to be used within an object-oriented framework. The JAS-mine philosophy encourages the separation of distinct concepts, objects and functionalities of the simulation model, and advocates and supports transparency, flexibility and modularity in model design. For instance, JAS-mine allows to store the list of regressors and their estimated coefficients externally to code, making it easy to change the specification of regression models used in the simulation and achieving a complete parallelisation between the tasks of the econometricians and those of the programmers. Moreover, tools for uncertainty analysis and search over the parameter space are also built in

Storing RDF as a Graph

RDF is the first W3C standard for enriching information resources of the Web with detailed meta data. The semantics of RDF data is defined using a RDF schema. The most expressive language for querying RDF is RQL, which enables querying of semantics. In order to support RQL, a RDF storage system has to map the RDF graph model onto its storage structure. Several storage systems for RDF data have been developed, which store the RDF data as triples in a relational database. To evaluate an RQL query on those triple structures, the graph model has to be rebuilt from the triples. In this paper, we presented a new approach to store RDF data as a graph in a object-oriented database. Our approach avoids the costly rebuilding of the graph and efficiently queries the storage structure directly. The advantages of our approach have been shown by performance test on our prototype implementation OO-Store

Innovative Evaluation System – IESM: An Architecture for the Database Management System for Mobile Application

As the mobile applications are constantly facing a rapid development in the recent years especially in the academic environment such as student response system [1-8] used in universities and other educational institutions; there has not been reported an effective and scalable Database Management System to support fast and reliable data storage and retrieval. This paper presents Database Management Architecture for an Innovative Evaluation System based on Mobile Learning Applications. The need for a relatively stable, independent and extensible data model for faster data storage and retrieval is analyzed and investigated. It concludes by emphasizing further investigation for high throughput so as to support multimedia data such as video clips, images and documents

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them