Set Valued Attributes

Background/Objectives: Since most of the enterprises are still using relational database as the back-end database, XML Enabled Database (XED) plays an important role to ensure the seamless integration of XML technology via the relational database. Methods/Statistical analysis: In this paper, we will compare the features supported in XED product, namely Microsoft SQL Server, Oracle and IBM DB2. In order to do so, we propose and develop XR-Gine to measure the performance of these storages. Findings: Some of the performance comparisons include speed of storing, retrieving, and updating functions in the database approaches. Based on the results obtained, the best performance of storing evaluation is Oracle. On the other hand, in terms of query retrieval, Microsoft SQL Server outperforms especially in supporting huge datasets, which is a necessity with the overwhelming data produced across the web nowadays. Application/Improvements: The results generated by the engine are beneficial to aid the community to choose the appropriate XED database system.

14 - Database Systems Performance Analysis

Modern large-scale information systems often use multiple database management systems, not all of which are necessarily relational. In recent years, NoSQL databases have gained acceptance in certain domains while relational databases remain de facto standard in many others. Many "legacy" information systems also use relational databases. Unlike relational database systems, NoSQL databases do not have a common data model or query language, making it difficult for users to access data in a uniform manner when using a combination of relational and NoSQL databases or simply several different NoSQL database systems. Therefore, the need for uniform data access from such a variety of data sources becomes one of the central problems for data integration. In this paper we provide an overview of the main problems, methods, and solutions for data integration between relational and NoSQL databases, as well as between different NoSQL databases. We focus mainly on the problems of structural, syntactic, and semantic heterogeneity and on proposed solutions for uniform data access, emphasizing some of the more recent proposals.

Aim/Purpose: This paper investigates the changing paradigms for technical skills that are needed by Data Engineers in 2018. Background: A decade ago, data engineers needed technical skills for Relational Database Management Systems (RDBMS), such as Oracle and Microsoft SQL Server. With the advent of Hadoop and NoSQL Databases in recent years, Data Engineers require new skills to support the large distributed datastores (Big Data) that currently exist. Job demand for Data Scientists and Data Engineers has increased over the last five years. Methodology: This research methodology leveraged the Pig programming language that used MapReduce software located on the Amazon Web Services (AWS) Cloud. Data was collected from 100 Indeed.com job advertisements during July of 2017 and then was uploaded to the AWS Cloud. Using MapReduce, phrases/words were counted and then sorted. The sorted phrase / word counts were then leveraged to create the list of the 20 top skills needed by a Data Engineer based on the job advertisements. This list was compared to the 20 top skills for a Data Engineer presented by Stitch that surveyed 6,500 Data Engineers in 2016. Contribution: This paper presents a list of the 20 top technical skills required by a Data Engineer.

In the last decades, relational databases were the most obvious choice for business applications. Times have changed and we should learn to use new models. Nowadays, we are living in the connected world, with tons of data related to each other. Moreover, in the emerging trend of using containers and enormous popularity of the cloud systems we can finally increase the performance of our solutions while decreasing costs. In this paper, authors have presented a new approach to improve overall performance and usability of databases. Results of the analysis are based on repeatable and verifiable observation of REST-based interactions with four products: Neo4j, MongoDB, Azure Cosmos DB and Microsoft SQL Server.

Conventional query languages such as SQL and MDX have limited formatting and visualization capabilities. Thus, although powerful queries can be composed, another layer of software is needed to report or present the results in a useful form to the analyst. VizQL™ is designed to fill that gap. VizQL evolved from the Polaris system at Stanford, which combined query, analysis and visualization into a single framework [1].VizQL is a formal language for describing tables, charts, graphs, maps, time series and tables of visualizations. These different types of visual representations are unified into one framework, making it easy to switch from one visual representation to another (e.g. from a list view to a cross-tab to a chart). Unlike current charting packages and like query languages, VizQL permits an unlimited number of picture expressions. Visualizations can thus be easily customized and controlled. VizQL is a declarative language. The desired picture is described; the low-level operations needed to retrieve the results, to perform analytical calculations, to map the results to a visual representation, and to render the image are generated automatically by the query analyzer. The query analyzer compiles VizQL expressions to SQL and MDX and thus VizQL can be used with relational databases and datacubes. The current implementation supports Hyperion Essbase, Microsoft SQL Server, Microsoft Analysis Services, MySQL, Oracle, as well as desktop data sources such as CSV and Excel files. This analysis phase includes many optimizations that allow large databases to be browsed interactively. VizQL enables a new generation of visual analysis tools that closely couple query, analysis and visualization.

This article focuses on database monitoring and performance tuning using supported diagnostic tools in Oracle and Microsoft SQL Server. The topic's relevance stems from the need to reduce the frequency of critical incidents and service recovery times while adhering to industry standards for IT service management. The novelty of the work lies in the analytical integration of DBMS telemetry (AWR/ASH/ADDM, Query Store/Intelligent Query Processing) with incident management processes and diagnostic platforms that use generative AI to accelerate the prototyping of fixes. The paper describes Oracle's diagnostic artifacts and the IQP mechanisms in SQL Server, examines their operational effects on MTTR and plan stability, and their connection with ITIL/ISO 20000 practices. Special attention is given to comparing the "observability" and "manageability" of incidents, as well as the role of LLM prompts in accelerating the "detection -> resolution" cycle. The work aims to develop practical recommendations for reducing incidents and speeding up recovery. To achieve this, comparative analysis, analytical generalization, and systematization of practices were applied. The conclusion describes the obtained effects and limitations of applicability. The article will be useful for architects, DBAs, and operations managers in industries with strict SLAs.

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

The article focuses on comparing relational and non-relational databases using applications written in C#. The aim of the study is to understand in which cases relational databases are preferred and when it is worth considering the use of non-relational databases. The research examines the speed of data retrieval, updating, and deletion, in the context of five different databases, including relational ones like PostgreSQL, MySql, Oracle, and non-relational ones such as Neo4j and MongoDB. The data consists of 1,578,098 records. In the case of relational databases, a unified database model was applied, while in NoSQL databases, the data model was appropriately adjusted to the specific type of non-relational database. Differences in the execution time of database queries are analyzed based on indexing strategies and query complexity. Special attention is given to query performance efficiency in the context of using C# and libraries that facilitate the connection between the application and databases. The conducted research indicates that the PostgreSQL database achieves the lowest average query response times.

The two stages of database design are logical and physical design. Logical database design, also referred to as , is done first. It is the process of studying the workings of a business organization, constructing a set of tables to store the business data, and understanding the constraints on the data, the dependencies between the tables, and the business rules concerning the data. The logical database design process is conducted without reference to any specific database technology such as Oracle or Microsoft SQL Server. Tools such as SQL Developer Data Modeler and Toad Data Modeler can be used for the purpose. Physical database design follows logical database design. First, the logical model is mapped to the proprietary features of the chosen database technology. Security requirements, integrity requirements, and business rules are also implemented. Finally, you consider performance: the ability of the database engine to handle work requests efficiently. This typically involves the creation of indexes on the data, and Oracle Database provides a wealth of indexing mechanisms to choose from.

The importance of big data, one of the most popular and researched topics of today, has been a subject of constant debate. Big data appear in almost every aspect of our lives, such as healthcare, education, shopping, social media, and industry; however, its storage and processing is not very efficient when using traditional methods. Therefore, the aim of this study is to obtain big data using a novel social shopping application that collects data from its users. The application is designed to collect information about people, products, and friendships among people, as well as product relationships, and post creations, and also enables setting up various options to mark products, such as like, buy, have, or help. The data gathered by the system is analyzed using both relational and non-relational databases, and the performance of these databases is then compared using specific queries, to reveal the model which performs better and more efficiently. Three different database models were designed and implemented for the system; a relational database, a document database, and a graph database. As a result of the experiments, there is no single model that is superior to the others as all three databases have their advantages and disadvantages. Therefore, the database selection should be decided based on the application domain.

Many types of maritime incident databases have been established that allow people to learn from past incidents and develop corresponding mitigation measures. However, our investigation of multiple international and national databases shows that most existing databases only record basic information regarding incidents in a single table. Lots of useful information is not included in the database (i.e., limited extension of the database). Meanwhile, some basic information is recorded tautologically (i.e., data redundancy). In this paper, two widely used databases are taken as examples, the Global Integrated Shipping Information System and the Lloyd's List Intelligence, to explain these common problems of existing databases. To overcome these limitations and improve the efficiency of data maintenance, this paper develops a relational maritime safety management database. The entity-relationship model is first used to depict the inter-related semantic information surrounding maritime incidents, and a relational database model is subsequently formed. Microsoft Access is employed to implement the proposed database, and a database application is also designed to demonstrate the utility of the database. Our preliminary study shows that the proposed database is implementable and has potential usage for both industry and academic research.

The subject of the study is the methods of intellectual analysis, namely the construction of a decision tree, associative analysis, the identification of patterns between related events based on data presented by a relational model. The purpose of the study is to analyze the features of information units and data structures, using the example of relational systems that affect the technology of knowledge extraction. Tasks: the article solves the following tasks: to consider the relational data model as the most popular and effective data structure used in intelligent information systems for data processing and storage; to analyze the operations of relational algebra, the operational component of the relational data model regarding the application of aggregate functions; to develop a general formal statement of the problem of knowledge extraction from a relational database; to consider the concept of functional associative rules; the ID3 decision tree generation algorithm focused on data processing in relational systems is analyzed. The following methods are implemented: modern view and trends in the field of data mining; features of building information systems based on relational databases, relational algebra, theory of normalization of relations; analysis of literature on the topic of research; comparative analysis. Results achieved: the relational data model is considered as the most effective data structure used in intelligent information systems for data processing and storage. A group of aggregate functions of relational databases is identified and analyzed with respect to key attributes of the relation, which makes it possible to build logical dependencies between information units of the subject area being analyzed. The task of extracting knowledge from the database is formally formulated. The concept of functional associative rules is introduced. The ID3 decision tree generation algorithm focused on data processing in relational systems is carefully analyzed. The semantic network (SN), built on the basis of the proposed approach, allows to increase the efficiency of decision support systems. Conclusions: the universal approach proposed in the article to build a relational data model of an information system for searching for associative patterns in data allows to solve a whole class of typical tasks in which objects are connected by a "many-to-many" relationship or M →N. The relational database model is proposed as a universal information structure for solving associative analysis tasks and presenting knowledge in the form of a semantic network. The examples given in the article confirm the effectiveness of the developed and considered approaches to solving the problem of data mining in the environment of relational systems. Solving the problem of identifying knowledge in data will allow to improve the quality of management decisions made.