scholarly journals Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

2020 ◽  
Vol 11 (04) ◽  
pp. 4321-4329
Author(s):  
Amira Hassan Abed
Keyword(s):  
Big Data ◽  
Database Systems ◽  
Nosql Database

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

Adapting Big Data Ecosystem for Landscape of Real World Applications

2019 ◽  
pp. 1-14
Author(s):  
Jyotsna Talreja Wassan
Keyword(s):  
Big Data ◽  
Online Education ◽  
Social Networking ◽  
Health Management ◽  
Database Systems ◽  
Internet Age ◽  
Heterogeneous Datasets ◽  
The World ◽  
Relational Database Systems ◽  
Data Ecosystem

Big data is revolutionizing the world in the internet age. The wide variety of areas like online businesses, electronic health management, social networking, demographics, geographic information systems, online education, etc. are gaining insight from big data principles. Big data is comprised of heterogeneous datasets which are too large to be handled by traditional relational database systems. An important reason for explosion of interest in big data is that it has become cheap to store volumes of data and there is a major rise in computation capacity. This chapter gives an overview of big data ecosystems comprising various big data platforms useful in today's competitive world.

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

Building OLAP Cubes From Columnar NoSQL Data Warehouses

2019 ◽  
pp. 129-157
Author(s):  
Khaled Dehdouh
Keyword(s):  
Big Data ◽  
Database System ◽  
Massive Data ◽  
Data Warehouses ◽  
Online Analysis ◽  
Storage Model ◽  
Data Cubes ◽  
Nosql Database ◽  
Oriented Approach

In the big data warehouses context, a column-oriented NoSQL database system is considered as the storage model which is highly adapted to data warehouses and online analysis. Indeed, the use of NoSQL models allows data scalability easily and the columnar store is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). To build OLAP cubes corresponding to the analysis contexts, the most common way is to integrate other software such as HIVE or Kylin which has a CUBE operator to build data cubes. By using that, the cube is built according to the row-oriented approach and does not allow to fully obtain the benefits of a column-oriented approach. In this chapter, the main contribution is to define a cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes according to the columnar approach by taking into account the non-relational and distributed aspects when data warehouses are stored.

Architecture for Big Data Storage in Different Cloud Deployment Models

2021 ◽  
pp. 178-208
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Data Services ◽  
Big Data Applications ◽  
Nosql Database ◽  
Amazon Web Services ◽  
Product Domains ◽  
Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Fragment Re-Allocation Strategy Based on Hypergraph for NoSQL Database Systems

2016 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Zhikun Chen ◽  
Shuqiang Yang ◽  
Yunfei Shang ◽  
Yong Liu ◽  
Feng Wang ◽  
...  
Keyword(s):  
Database Systems ◽  
Communication Cost ◽  
Allocation Strategy ◽  
Hypergraph Partitioning ◽  
Computing Model ◽  
Operation Pattern ◽  
Nosql Database ◽  
High Scalability ◽  
Weighted Hypergraph ◽  
Allocation Strategies

NoSQL database is famed for the characteristics of high scalability, high availability, and high fault-tolerance. It is used to manage data for a lot of applications. The computing model has been transferred to “computing close to data”. Therefore, the location of fragment directly affects system's performance. Every site's load dynamical changes because of the increasing data and the ever-changing operation pattern. So system has to re-allocate fragment to improve system's performance. The general fragment re-allocation strategies of NoSQL database scatter the related fragments as possible to improve the operations' parallel degree. But those fragments may interact with each other in some application's operations. So the high parallel degree of operation may increase system's communication cost such as data are transferred by network. In this paper, the authors propose a fragment re-allocation strategy based on hypergraph. This strategy uses a weighted hypergraph to represent the fragments' access pattern of operations. A hypergraph partitioning algorithm is used to cluster fragments in the strategy. This strategy can improve system's performance according to reducing the communication cost while guaranteeing the parallel degree of operations. Experimental results confirm that the strategy will effectively contribute in solving fragment re-allocation problem in specific application environment of NoSQL database system, and it can improve system's performance.

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