A relational dataflow database

A model of a relational database system based on the principles of functional, data-driven computation is proposed. Relations (sets of data tuples) are represented as streams of values carried by independent tokens among operators of an unraveling dataflow network.Values may be “updated” by circulating the database through an update operator. To perform a query on the database, streams involved in that query are replicated and submitted as inputs to dataflow programs (graphs) obtained by translating relational algebra expressions.

User Defined Types and Nested Tables in Object Relational Databases

Bernadette Byrne, Mary Garvey, ‘User Defined Types and Nested Tables in Object Relational Databases’, paper presented at the United Kingdom Academy for Information Systems 2006: Putting Theory into Practice, Cheltenham, UK, 5-7 June, 2006.There has been much research and work into incorporating objects into databases with a number of object databases being developed in the 1980s and 1990s. During the 1990s the concept of object relational databases became popular, with object extensions to the relational model. As a result, several relational databases have added such extensions. There has been little in the way of formal evaluation of object relational extensions to commercial database systems. In this work an airline flight logging system, a real-world database application, was taken and a database developed using a regular relational database and again using object relational extensions, allowing the evaluation of the relational extensions.Peer reviewe

A comparative analysis of data redundancy and execution time between relational and object oriented schema table

The design of database is one of the important parts in building software, because database is the data storage inside the system. There are some techniques that allow the programmer to improve design of the database. One of the most popular techniques being used for database is the relational technique, which content entity relationship diagram and normalization. The relational technique is easy to use and useful for reducing data redundancy because the normalization technique solves the data redundancy by applying normalization normal forms on the schema tables. The second technique is the object oriented technique, which content class diagram and generate schema table. An advantage of object oriented technique is its closeness to programming languages like C++ or C#. This project is starting with applying relational technique and object oriented technique to define which technique uses less data redundancy during design database. Based on experimental results for total data redundancy in HMS case study was 336 for relational technique and 364 for object oriented technique as well as, course database case study was 186 for relational technique and 204 for object oriented technique. Also, this project is focus on query execution time between relational databases and object oriented database by using user friendly window. The experimental result for query execution time in HMS case study was 107.25 milliseconds for RDBMS and 80.5 milliseconds for OODBMS. In course database case study was 46.75 milliseconds for RDBMS and 31.75 milliseconds for OODBMS. However, the comparative analysis in this project is explaining the result of comparison between relational and object oriented techniques specifically with data redundancy and query execution time

Probabilistic Relational Model Benchmark Generation

The validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential. In this paper, we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes. We are particularly interested in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs) to a relational data mining context and enable effective and robust reasoning over relational data. Even though a panoply of works have focused, separately , on the generation of random Bayesian networks and relational databases, no work has been identified for PRMs on that track. This paper provides an algorithmic approach for generating random PRMs from scratch to fill this gap. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest not only for machine learning researchers to evaluate their proposals in a common framework, but also for databases designers to evaluate the effectiveness of the components of a database management system