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Proceedings ArticleDOI

Big Data: The NoSQL and RDBMS review

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TLDR
This paper provides a review and the comparison between NoSQL and Relational Database Management System (RDBMS) and defines the application that matches the system and subsequently able to accurately correlates to a specific NoSQL system.
Abstract
The quantity of data transmitted in the network intensified rapidly with the increased dependency on social media applications, sensors for data acquisitions and smartphones utilizations. Typically, such data is unstructured and originates from multiple sources in different format. Consequently, the abstraction of data for rendering is difficult, that lead to the development of a computing system that is able to store data in unstructured format and support distributed parallel computing. To data, there exist approaches to handle big data using NoSQL. This paper provides a review and the comparison between NoSQL and Relational Database Management System (RDBMS). By reviewing each approach, the mechanics of NoSQL systems can be clearly distinguished from the RDBMS. Basically, such systems rely on multiple factors, that include the query language, architecture, data model and consumer API. This paper also defines the application that matches the system and subsequently able to accurately correlates to a specific NoSQL system.

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Citations
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NoSQL Injection Attack Detection in Web Applications Using RESTful Service

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Büyük Veri: Uygulama Alanları, Analitiği ve Güvenlik Boyutu

TL;DR: Buyuk Veri as discussed by the authors is another example of a buy-it-then-buy-it approach to the problem of data augmentation in Map-Reduce systems, which has been successfully applied in the context of Hadoop and MPP.
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SQL Database with physical database tuning technique and NoSQL graph database comparisons

TL;DR: The physical database tuning of the Oracle Relational database is done and NoSQL graph database is compared with NoSQL Graph database to increase the performance of relational databases.
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A literature review on NoSQL database for big data processing

TL;DR: The NoSQL databases for Big Data processing including its transactional and structural issues including the structural issues and the real-time data mining techniques to extract the estimated valuable information are presented.
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Security assurance of MongoDB in singularity LXCs: an elastic and convenient testbed using Linux containers to explore vulnerabilities

TL;DR: A virtual, portable and easily deployable vulnerability assessment general testbed within the Linux container network is introduced and how to use generated results to improve the security level of the given system is discussed.
References
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Proceedings ArticleDOI

Dynamo: amazon's highly available key-value store

TL;DR: D Dynamo is presented, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience and makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
Journal ArticleDOI

Scalable SQL and NoSQL data stores

Rick Cattell
TL;DR: This paper examines a number of SQL and socalled "NoSQL" data stores designed to scale simple OLTP-style application loads over many servers, and contrasts the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions.
Proceedings ArticleDOI

History repeats itself: sensible and NonsenSQL aspects of the NoSQL hoopla

TL;DR: Based on more than three decades of database systems work in the research and product arenas, what are many of the pitfalls to avoid since there is currently a mad rush to develop and adopt a plethora of NoSQL systems in a segment of the IT population, including the research community?
Proceedings ArticleDOI

MeT: workload aware elasticity for NoSQL

TL;DR: MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment and is able to autonomously achieve the performance of a manual configured cluster and quickly reconfigure the cluster according to unpredicted workload changes.
Journal ArticleDOI

Large-Scale Deep Belief Nets With MapReduce

TL;DR: This paper presents a distributed learning paradigm for the RBMs and the backpropagation algorithm using MapReduce, a popular parallel programming model, and demonstrates that the distributedRBMs and DBNs are amenable to large-scale data with a good performance in terms of accuracy and efficiency.
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