Bridging Relational and NoSQL Worlds

2018 ◽  
pp. 177-238
Keyword(s):  
Relational Databases ◽  
Nosql Databases ◽  
Data Manipulation ◽  
Further Development ◽  
Analyze Data

The chapter discusses the fact that the development and use of NoSQL databases showed that neither everything was good in NoSQL nor everything was so bad in relational databases. Namely, when operating with data, NoSQL databases have identical requirements for entering, updating, deleting or searching data, or for the data manipulation that SQL already resolved long ago. Therefore, it is not surprising that further development of many NoSQL databases shifted towards supporting SQL, which is one of the topics of this chapter. Namely, database users are generally not concerned with details about how data is stored. Rather, they want to have the possibility to view and analyze data together, regardless of whether the data is stored in relational or NoSQL databases. Therefore, vendors of relational databases were forced to look for solutions that would allow them to work with data stored in NoSQL databases as well.

scholarly journals Performance Analysis and Optimization Techniques for Oracle Relational Databases

2019 ◽  
Vol 19 (2) ◽  
pp. 117-132
Author(s):  
Fernando Almeida ◽  
Pedro Silva ◽  
Fernando Araújo
Keyword(s):  
Execution Time ◽  
Relational Databases ◽  
Database System ◽  
Optimization Techniques ◽  
Management Systems ◽  
System Response ◽  
Database Access ◽  
Data Manipulation ◽  
System Response Time ◽  
Analyze Data

Abstract Databases provide an efficient way to store, retrieve and analyze data. Oracle relational database is one of the most popular database management systems that is widely used in a different variety of industries and businesses. Therefore, it is important to guarantee that the database access and data manipulation is optimized for reducing database system response time. This paper intends to analyze the performance and the main optimization techniques (Forall, Returning, and Bulk Collect) that can be adopted for Oracle Relational Databases. The results have shown that the adoption of Forall and Bulk Collect approaches bring significant benefits in terms of execution time. Furthermore, the growth rate of the average execution time is lower for Bulk Collect than Forall. However, adoption of Returning approach doesn’t bring significant statistical benefits.

Formalizing the Mapping of UML Conceptual Schemas to Column-Oriented Databases

2018 ◽  
Vol 14 (3) ◽  
pp. 44-68 ◽  
Author(s):  
Fatma Abdelhedi ◽  
Amal Ait Brahim ◽  
Gilles Zurfluh
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Relational Databases ◽  
Traditional Approach ◽  
Physical Models ◽  
Decision Making Process ◽  
Nosql Databases ◽  
Care Field ◽  
Sufficient Degree

Nowadays, most organizations need to improve their decision-making process using Big Data. To achieve this, they have to store Big Data, perform an analysis, and transform the results into useful and valuable information. To perform this, it's necessary to deal with new challenges in designing and creating data warehouse. Traditionally, creating a data warehouse followed well-governed process based on relational databases. The influence of Big Data challenged this traditional approach primarily due to the changing nature of data. As a result, using NoSQL databases has become a necessity to handle Big Data challenges. In this article, the authors show how to create a data warehouse on NoSQL systems. They propose the Object2NoSQL process that generates column-oriented physical models starting from a UML conceptual model. To ensure efficient automatic transformation, they propose a logical model that exhibits a sufficient degree of independence so as to enable its mapping to one or more column-oriented platforms. The authors provide experiments of their approach using a case study in the health care field.

Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

2016 ◽  
pp. 133-162 ◽  
Author(s):  
Berkay Aydin ◽  
Vijay Akkineni ◽  
Rafal A Angryk
Keyword(s):  
Data Storage ◽  
Relational Databases ◽  
Large Scale ◽  
Database Systems ◽  
Distributed Database ◽  
Data Models ◽  
Trajectory Data ◽  
Nosql Databases ◽  
Distributed Database Systems ◽  
Partitioned Data

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.

scholarly journals When Relational-Based Applications Go to NoSQL Databases: A Survey

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 241
Author(s):  
Geomar A. Schreiner ◽  
Denio Duarte ◽  
Ronaldo dos S. Melo
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Relational Databases ◽  
Research Area ◽  
Relational Data ◽  
Data Sets ◽  
System Architectures ◽  
Nosql Databases ◽  
State Of Art ◽  
Open Issues

Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution.

Migration from a Relational Database to NoSQL

2018 ◽  
Vol 8 (3) ◽  
pp. 63-80
Author(s):  
Samah Bouamama
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Relational Databases ◽  
New Technologies ◽  
Management Tools ◽  
Nosql Databases

This article describes how due to the monstrous evolution of the technology and the enormous increase in data, it becomes difficult to work with traditional database management tools; relational databases quickly reach their limits and adding servers does not increase performance. As a result of this problem, new technologies have emerged, such as NoSQL databases, which radically change the architecture of the databases that the authors are used to seeing, thus increasing the performance and availability of services. As these technologies are relatively new, standard or formal migration processes do not yet exist, the authors thought it useful to propose a migration approach from a relational database to a database-oriented columns type HBase and Cassandra.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549 ◽  
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

scholarly journals Security Analysis of MongoDB

2020 ◽  
Author(s):  
Sahib Singh
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Relational Databases ◽  
Security Analysis ◽  
Nosql Databases ◽  
Computing Platforms

NoSQL Databases are a form of non-relational databases whose primary purpose is to store and retrieve data. Due to recent advancements in cloud computing platforms and the emergence of Big Data, NoSQL Databases are more becoming popular than ever. In this paper we are going to understand and analyze the fundamental security features and the vulnerabilities of MongoDB and how it performs compared to relational databases on these fronts.

scholarly journals IMPLEMENTASI WEBSITE PENCARIAN KOS DENGAN NoSQL

2017 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Danny Kriestanto ◽  
Alif Benden Arnado
Keyword(s):  
Data Storage ◽  
Structure Data ◽  
Relational Databases ◽  
New Technology ◽  
Unstructured Data ◽  
Nosql Databases ◽  
Storage Technology ◽  
Nosql Database ◽  
Boarding House ◽  
New Type

The new technology of database has moved forward the relational databases. Now, the massive and unstructured data encourage experts to create a new type of database without using query. One of this technology is called NoSQL (Not Only SQL). One of the developing RDBMS that using this technique is MongoDB, which already supporting data storage technology that is no longer need for structured tables and rigid-typed of data. The schema was made flexible to handle the changes of data. The MongoDB data collecting characteristics in the form of arrays is considered suitable for the implementation of boarding house searching where each of the boarding houses have their own scenario structures. MongoDB also supports several programming language, including PHP with Bootstrap material as interface. The results of the research showed that there are alot of difference in implementing a NoSQL database with the regular relational one. NoSQL databases considered alot more complicated in structure, data type, even the CRUD system. The results also showed that in order to view an array inside another array will need two processes.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

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