Securing Relational Databases against Security Vulnerabilities: A Case of Microsoft SQL Server and PostgreSQL

2018 ◽

Vol 8 ◽

pp. 263-269

Author(s):

Grzegorz Dziewit ◽

Jakub Korczyński ◽

Maria Skublewska-Paszkowska

Keyword(s):

Binary Data ◽

Relational Databases ◽

Database Systems ◽

Database System ◽

Sql Server ◽

External Application ◽

Oracle Database ◽

Sql Server Database ◽

Relational Database Systems ◽

Mean Time

Comparison of efficiency is not a trivial phenomenon because of disparities between different database systems. This paper presents a methodology of comparing relational database systems in respect of mean time of execution individual DML queries containing subqueries and conjunction of tables. The presented methodology can be additionally accommodated to studies of efficiency in a range of database system itself (study of queries executed directly in database engine). The described methodology allows to receive statement telling which database system is better in comparison to another in dependency of functionalities fulfilled by external application. In the article the analysis of mean time of execution individual DML queries was performed.Two research hypotheses have been put forward: "Microsoft SQL Server database system needs less time to execute INSERT and UPDATE queries than Oracle database" and "Oracle database system needs less time to execute DML queries with binary data than SQL Server"

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State of the Art Technology

Advances in Data Mining and Database Management - Design, Performance, and Analysis of Innovative Information Retrieval ◽

10.4018/978-1-4666-1975-3.ch015 ◽

2013 ◽

pp. 201-218

Author(s):

Ibrahim Dweib ◽

Joan Lu

Keyword(s):

Database Management ◽

Relational Databases ◽

State Of The Art ◽

Database Management Systems ◽

The State ◽

Sql Server ◽

Management Systems ◽

Advantages And Disadvantages ◽

Xml Documents

This chapter presents the state of the art approaches for storing and retrieving the XML documents from relational databases. Approaches are classified into schema-based mapping and schemaless-based mapping. It also discusses the solutions which are included in Database Management Systems such as SQL Server, Oracle, and DB2. The discussion addresses the issues of: rebuilding XML from RDBMS approaches, comparison of mapping approaches, and their advantages and disadvantages. The chapter concludes with the issues addressed.

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Decision making based on data analyses using data warehouses

International Journal of Business & Technology ◽

10.33107/ijbte.2018.6.3.04 ◽

2018 ◽

Vol 6 (3) ◽

pp. 1-6

Author(s):

Valdrin Haxhiu

Keyword(s):

Decision Making ◽

Data Warehouse ◽

Relational Databases ◽

Daily Basis ◽

Transformation Process ◽

Sql Server ◽

Data Warehouses ◽

Data Analyses ◽

Weak Points ◽

Using Data

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making.

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Performance comparison of relational databases SQL Server, MySQL and PostgreSQL using a web application and the Laravel framework

Journal of Computer Sciences Institute ◽

10.35784/jcsi.2279 ◽

2020 ◽

Vol 17 ◽

pp. 358-364

Author(s):

Rafał Wodyk ◽

Maria Skublewska-Paszkowska

Keyword(s):

Web Application ◽

Relational Databases ◽

Performance Comparison ◽

Sql Server ◽

Technical Parameters

Many database implementations are supported by application frameworks that can affect their performance. The paper presents a comparison of the performance of SQL Server, MySQL and PostgreSQL relational databases based on an application written in PHP using the Laravel framework. The time of performance for various types of queries, both simple and using column and table concatenation was evaluated. The obtained results for the same database structures differed depending on the operations performed on the databases. Looking at the entirety of the research conducted, it can be concluded that in the case of databases in which the number of records is not too large (up to 1000 records) and the technical parameters of the device on which the database is running are of low or medium class, MySQL performs very well.

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Set Valued Attributes

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch104 ◽

2005 ◽

pp. 632-637 ◽

Cited By ~ 3

Author(s):

Karthikeyan Ramasamy ◽

Prasad M. Deshpande

Keyword(s):

Relational Database ◽

Data Model ◽

Relational Databases ◽

Database Systems ◽

Sql Server ◽

Relational Model ◽

Database Model ◽

Competitive Edge ◽

The Past ◽

Relational Database Systems

About three decades ago, when Codd (1970) invented the relational database model, it took the database world by storm. The enterprises that adapted it early won a large competitive edge. The past two decades have witnessed tremendous growth of relational database systems, and today the relational model is by far the dominant data model and is the foundation for leading DBMS products, including IBM DB2, Informix, Oracle, Sybase, and Microsoft SQL server. Relational databases have become a multibillion-dollar industry.

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How tax audit and tax advisory can benefit from big data analytics tools data analysis and processing in relational databases using SQL Server and Power Pivot & Power View in Excel

2018 13th Iberian Conference on Information Systems and Technologies (CISTI) ◽

10.23919/cisti.2018.8399472 ◽

2018 ◽

Author(s):

Marcia R. C. Santos ◽

Raul M. S. Laureano ◽

Carlos E. R. Albino

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Relational Databases ◽

Big Data Analytics ◽

Sql Server ◽

Tax Audit

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COMPARE

Proceedings of the VLDB Endowment ◽

10.14778/3476249.3476291 ◽

2021 ◽

Vol 14 (11) ◽

pp. 2419-2431

Author(s):

Tarique Siddiqui ◽

Surajit Chaudhuri ◽

Vivek Narasayya

Keyword(s):

Relational Databases ◽

Poor Performance ◽

Database Systems ◽

Optimization Techniques ◽

Sql Server ◽

High Dimensional ◽

Extensive Evaluation ◽

Real World Datasets ◽

High Dimensional Datasets ◽

Database Engine

Data analysis often involves comparing subsets of data across many dimensions for finding unusual trends and patterns. While the comparison between subsets of data can be expressed using SQL, they tend to be complex to write, and suffer from poor performance over large and high-dimensional datasets. In this paper, we propose a new logical operator COMPARE for relational databases that concisely captures the enumeration and comparison between subsets of data and greatly simplifies the expressing of a large class of comparative queries. We extend the database engine with optimization techniques that exploit the semantics of COMPARE to significantly improve the performance of such queries. We have implemented these extensions inside Microsoft SQL Server, a commercial DBMS engine. Our extensive evaluation on synthetic and real-world datasets shows that COMPARE results in a significant speedup over existing approaches, including physical plans generated by today's database systems, user-defined functions (UDFs), as well as middleware solutions that compare subsets outside the databases.

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