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

О топологии путей нормализации в реляционном каркасе

Исследованы пути нормализации в универсальном каркасе реляционных баз данных (БД) и топологии этих путей. Доказана теорема замкнутости путей нормализации в реляционном каркасе. Теорема позволяет применять реляционный каркас как уникальный носитель схем БД, нормализованных до высоких форм, а также анализировать существующие и внедренные БД на предмет их аномалий и влияния на приложения в процессе эксплуатации.Досліджено шляхи нормалізації в універсальному каркасі реляційних баз даних (БД) і топологію цих шляхів. Доведено теорему замкненості шляхів нормалізації в реляційному каркасі. Теорема дозволяє використовувати реляційний каркас в якості універсального носія схем БД, нормалізованих до високих форм, а також аналізувати існуючі та впроваджені БД на предмет їх аномалій та впливу на програмне застосування в процесі експлуатації.In the normalization ways in the universal frame of the relational databases and the topology of these ways are investigated. Theorem about closure of normalization ways in a relational frame has been proved. The theorem allows using a relational frame as a unique database schemes carrier, normalized to the higher forms. It also allows analyzing the existing and embedded databases for their anomalies and the impact on the software usage during the operation

Analysis of the Impact of Data Normalization on Cyber Event Correlation Query Performance

A critical capability required in the operation of cyberspace is the ability to maintain situational awareness of the status of the infrastructure elements that constitute cyberspace. Event logs from cyber devices can yield significant information, and when properly utilized they can provide timely situational awareness about the state of the cyber infrastructure. In addition, proper Information Assurance requires the validation and verification of the integrity of results generated by a commercial log analysis tool. Event log analysis can be performed using relational databases. To enhance database query performance, previous literatures affirm denormalization of databases. Yet database normalization can also increase query performance. Database normalization improved the majority of the queries performed using very large data sets of router events. In addition, queries performed faster on normalized tables when all the necessary data were contained in the normalized tables. Database normalization improves table organization and maintains better data consistency than a lack of normalization. Nonetheless, there are some tradeoffs when normalizing a database, such as additional preprocessing time and extra storage requirements. But overall, normalization improved query performance and must be considered an option when analyzing event logs using relational databases. There are three primary research questions addressed in this thesis: (1) What standards exist for the generation, transport, storage, and analysis of event log data for security analysis?; (2) How does database normalization impact query performance when using very large data sets (over 30 million) of router events?; and (3) What are the tradeoffs between using a normalized versus non-normalized database in terms of preprocessing time, query performance, storage requirements, and database consistency

Optimizing Federated Queries Based on the Physical Design of a Data Lake

The optimization of query execution plans is known to be crucial for reducing the query execution time. In particular, query optimization has been studied thoroughly for relational databases over the past decades. Recently, the Resource Description Framework (RDF) became popular for publishing data on the Web. As a consequence, federations composed of different data models like RDF and relational databases evolved. One type of these federations are Semantic Data Lakes where every data source is kept in its original data model and semantically annotated with ontologies or controlled vocabularies. However, state-of-the-art query engines for federated query processing over Semantic Data Lakes often rely on optimization techniques tailored for RDF. In this paper, we present query optimization techniques guided by heuristics that take the physical design of a Data Lake into account. The heuristics are implemented on top of Ontario, a SPARQL query engine for Semantic Data Lakes. Using sourcespecific heuristics, the query engine is able to generate more efficient query execution plans by exploiting the knowledge about indexes and normalization in relational databases. We show that heuristics which take the physical design of the Data Lake into account are able to speed up query processing

Using a Semi-Realistic Database to Support a Database Course

A common problem for university relational database courses is to construct effective databases for instructions and assignments. Highly simplified ‘toy’ databases are easily available for teaching, learning, and practicing. However, they do not reflect the complexity and practical considerations that students encounter in real-world projects after their graduation. On the other hand, production databases may contain too much domain nuances and complexity to be effectively used as a learning tool. Sakila is a semi-realistic, high quality, open source, and highly available database provided by MySQL. This paper describes the use of Sakila as a unified platform to support instructions and multiple assignments of a graduate database course for five semesters. Based on seven surveys with 186 responses, the paper discusses our experience using Sakila. We find this approach promising, and students in general find it more useful and interesting than the highly simplified databases developed by the instructor, or obtained from textbooks. We constructed a collection of 124 problems with suggested solutions on the topics of database modeling and normalization, SQL query, view, stored function, stored procedure, trigger, database Web-driven application development with PHP/MySQL, Relational Algebra using an interpreter, Relational Calculus, XML generation, XPath, and XQuery. This collection is available to Information Systems (IS) educators for adoption or adaptation as assignments, examples, and examination questions to support different database courses

Fast and Simple Relational Processing of Uncertain Data

This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relation representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.Comment: 12 pages, 14 figure

Kolmogorov Complexity in perspective. Part II: Classification, Information Processing and Duality

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts published in a same volume. Part II is dedicated to the relation between logic and information system, within the scope of Kolmogorov algorithmic information theory. We present a recent application of Kolmogorov complexity: classification using compression, an idea with provocative implementation by authors such as Bennett, Vitanyi and Cilibrasi. This stresses how Kolmogorov complexity, besides being a foundation to randomness, is also related to classification. Another approach to classification is also considered: the so-called "Google classification". It uses another original and attractive idea which is connected to the classification using compression and to Kolmogorov complexity from a conceptual point of view. We present and unify these different approaches to classification in terms of Bottom-Up versus Top-Down operational modes, of which we point the fundamental principles and the underlying duality. We look at the way these two dual modes are used in different approaches to information system, particularly the relational model for database introduced by Codd in the 70's. This allows to point out diverse forms of a fundamental duality. These operational modes are also reinterpreted in the context of the comprehension schema of axiomatic set theory ZF. This leads us to develop how Kolmogorov's complexity is linked to intensionality, abstraction, classification and information system.Comment: 43 page