scholarly journals Benchmarking Big Data OLAP NoSQL Databases

2018 ◽  
pp. 82-94 ◽  
Author(s):  
Mohammed El Malki ◽  
Arlind Kopliku ◽  
Essaid Sabir ◽  
Olivier Teste
Keyword(s):  
Big Data ◽  
Nosql Databases

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.

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

Big Data Analytics

2019 ◽  
pp. 67-81
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Big Data ◽  
Open Problem ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Capture ◽  
Data Sets ◽  
Full Potential ◽  
Processing Application ◽  
Nosql Databases ◽  
The World

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

Secure search for encrypted personal health records from big data NoSQL databases in cloud

Computing ◽  
2019 ◽  
Vol 102 (6) ◽  
pp. 1521-1545 ◽  
Author(s):  
Lanxiang Chen ◽  
Nan Zhang ◽  
Hung-Min Sun ◽  
Chin-Chen Chang ◽  
Shui Yu ◽  
...  
Keyword(s):  
Big Data ◽  
Personal Health Records ◽  
Personal Health ◽  
Health Records ◽  
Nosql Databases ◽  
Secure Search

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.

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.

Framework for Visualization of GeoSpatial Query Processing by Integrating Redis With Spark

2019 ◽  
Vol 8 (3) ◽  
pp. 1-25 ◽  
Author(s):  
S. Vasavi ◽  
V.N. Priyanka G ◽  
Anu A. Gokhale
Keyword(s):  
Big Data ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Real Time Processing ◽  
Time Processing ◽  
Storage And Retrieval ◽  
Tourism Marketing ◽  
Memory Store ◽  
Benchmark Datasets

Nowadays we are moving towards digitization and making all our devices produce a variety of data, this has paved the way to the emergence of NoSQL databases like Cassandra, MongoDB, and Redis. Big data such as geospatial data allows for geospatial analytics in applications such as tourism, marketing, and rural development. Spark frameworks provide operators storage and processing of distributed data. This article proposes “GeoRediSpark” to integrate Redis with Spark. Redis is a key-value store that uses an in-memory store, hence integrating Redis with Spark can extend the real-time processing of geospatial data. The article investigates storage and retrieval of the Redis built-in geospatial queries and has added two new geospatial operators, GeoWithin and GeoIntersect, to enhance the capabilities of Redis. Hashed indexing is used to improve the processing performance. A comparison on Redis metrics with three benchmark datasets is made. Hashset is used to display geographic data. The output of geospatial queries is visualized to the type of place and the nature of the query using Tableau.

Security of NoSQL Database Against Intruders

2019 ◽  
Vol 13 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Khaleel Ahmad ◽  
Mohammad Shoaib Alam ◽  
Nur Izura Udzir
Keyword(s):  
Big Data ◽  
Relational Database ◽  
Large Scale ◽  
Distributed Databases ◽  
Structural Data ◽  
Database Management System ◽  
Base System ◽  
Security Measures ◽  
Nosql Databases ◽  
Nosql Database

Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

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