Object Relational Mapper Research Articles

Symptomatic Assistance

The project aims to help the users get the idea if he/she may be suffering from heart disease or not. Web development is the work involved in developing a Website for the web (world wide web) or an intranet (a private network).Web development can range from Developing an easy single static page of plain text to complex web applications, electronic business and social networking services. The main goal of this website (SYMPTOMATIC ASSISTANCE) is to predict the possibility of having Heart disease. For this , the user needs to provide some information regarding their health. Such as blood pressure, glucose, cigarettes per day etc. According to which the website will respond. This will make people aware and help them improve their health. Machine learning is a method of data analysis that automates analytical model building. It is a branch of AI supported the thought that systems can learn from data, identify patterns and make decisions with minimal human intervention. For this we chose the best dataset from kaggle and used it in the best possible way to predict the output with high accuracy. For being able to predict the correct output, we applied a few machine learning models and chose the best fitted algorithm according to accuracy. For connecting machine Learning models with the webpages we used Flask. Flask is a micro framework written in Python. It is classified as a microframework because it doesn't require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions which will add application features as if they were implemented in Flask itself. Extensions exist for object-relational mappers, form validation, upload handling, various open authentication technologies and several common framework related tools.At the end will deploy our project using Heroku. Heroku is a cloud Platform as a service (PaaS) supporting several programming languages. One of the first cloud platforms, This project will make it easy for the user to know their health closely.

Performance Comparison of CRUD Methods using NET Object Relational Mappers: A Case Study

Most applications available nowadays are using an Object Relational Mapper (ORM) to access and save data. The additional layer that is being wrapped over the database induces a performance impact in detrimental of raw SQL queries; on the other side, the advantages of using ORMs by focusing on domain level through application development represent a premise for easier development and simpler code maintenance. In this context, this paper makes a performance comparison between three of the most used ORM technologies from the .NET family: Entity Framework Core 2.2, nHibernate 5.2.3 and Dapper 1.50.5. The main objective of the paper is to make a comparative analysis of the impact that a specific ORM has on application performance when realizing database requests. In order to perform the analysis, a specific testing architecture was designed to ensure the consistency of tests. Performance evaluation for time responses and memory usage for each technology was done using the same CRUD (Create Read Update Delete) operations on the database. The results obtained proved that the decision to use one of another is dependent of the most used type of operation. A comprehensive discussion based on results analysis is done in order to support a decision for choosing a specific ORM by the software engineers in the process of software design and development.

A Model-Driven Approach to Generate Schemas for Object-Document Mappers

Many actual NoSQL systems are schemaless, that is, the structure of the data is not defined beforehand in any schema, but it is implicit in the data itself. This characteristic is very convenient when the data structure suffers frequent changes. However, the agility and flexibility achieved is at the cost of losing some important benefits, such as 1) assuring that the data stored and retrieved fits the database schema; 2) some database utilities require to know the schema, and; 3) schema visualization helps developers to write better code. In previous work, we proposed a model-based reverse engineering approach to infer schema models from NoSQL data. Model-driven engineering (MDE) techniques can be used to take advantage of extracted models with different purposes, such as schema visualization or automatic code generation. Here, in this paper, we present an MDE solution to automate the usage of Object-NoSQL mappers when the database already exists. We will focus on mappers that are available for document systems (Object-Document mappers, ODMs), but the proposed approach is mapper-independent. These mappers are emerging to provide similar functionality to Object-Relational mappers: they are in charge of the mapping of objects into NoSQL data (documents in the case of ODMs) for object-oriented applications. We show how schemas and other artifacts (e.g. validators and indexes) for ODMs can be automatically generated from inferred schemas. The solution consists of a two-step model transformation chain, where an intermediate model is generated to ease the code generation. We have applied our approach for two popular ODMs: Mongoose and Morphia and validated it with the StackOverflow dataset.

English

Object relational mapping from relational databases has become topic to be dealt with in the recent decade. Object Relational Mapping is a layer which is responsible to bridge the gap between object-oriented programs and relational database. Lot of new Object Relational Mappers have been developed which includes forward engineering as well as reverse engineering approach for developing the object code. In this paper we are proposing the method of reverse engineering for developing object code file.