NOSQL Database Engines for Big Data Management

International Journal of Trend in
International Open Access Journal
ISSN No: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
NOSQL Database Engines
Assistant Professor, Department of Computer Science and Engineering
SSM College of Engineering and Technology
ABSTRACT
We are living in the digital world and last two decades
have seen significant expansion in the information on
internet technology. In present digital world the IOT
is most popular term means computers, mobile phones
and physical devices like sensors are connected to
internet. With the rapid outreach of internet it is very
important to focus on technological advancements for
managing huge amount of data with easy access.
Keywords: Sensor, IOT, NOSQL
I. INTRODUCTION
A database is a collection of data items that provides
an organizational structure for information storage.
Conceptually, database is a component of database
system. Besides database, database system consists of
database users, database applications and Database
Management Systems (DBMS). Database users need
not to be always human. It is possible, for example,
for other software programs to be users of the
database. Users interact with database application and
application further depends on the DBMS to extract
and store data in the database. The DBMS acts as a
gatekeeper. All the information owing in or out of
database must pass through the DBMS. It is a critical
mechanism for maintaining quality of data and
database. Users and database applications are not
allowed directly to interact with database. A Database
Management System is an intermediary between
database applications and database. The DBMS
Database Application
International Journal of Trend in Scientific Research and Development (IJTSRD)
International Open Access Journal | www.ijtsrd.com
ISSN No: 2456 - 6470 | Volume - 2 | Issue – 6 | Sep
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
NOSQL Database Engines for Big Data Management
Mrs. Yasmeen
Assistant Professor, Department of Computer Science and Engineering
SSM College of Engineering and Technology, Baramulla, Jammu and Kashmir
We are living in the digital world and last two decades
have seen significant expansion in the information on
internet technology. In present digital world the IOT
is most popular term means computers, mobile phones
and physical devices like sensors are connected to
internet. With the rapid outreach of internet it is very
important to focus on technological advancements for
managing huge amount of data with easy access.
A database is a collection of data items that provides
an organizational structure for information storage.
Database also provides a mechanism for querying,
creating, modifying and deleting data. A list can also
be used to store data but in a list, redun
major issue. A database can store relationships and
data that are more complicated than a simple list with
lesser or no redundancy. A relational database stores
data in tables. Normally a table is based on one
information theme. For example, an
be divided into manager table, intern table, and junior
staff table. A table is a two dimensional grid of data
that contains columns and rows. The convention in
relational database world is that columns represent
different attributes of an entity and each row
represents the instance of the entity.
Figure: A Database System
Conceptually, database is a component of database
system. Besides database, database system consists of
database users, database applications and Database
Management Systems (DBMS). Database users need
not to be always human. It is possible, for example,
or other software programs to be users of the
database. Users interact with database application and
application further depends on the DBMS to extract
and store data in the database. The DBMS acts as a
gatekeeper. All the information owing in or out of
tabase must pass through the DBMS. It is a critical
mechanism for maintaining quality of data and
database. Users and database applications are not
allowed directly to interact with database. A Database
Management System is an intermediary between
applications and database. The DBMS
creates and manages the database. DBMS can be
categorized based on its data model. Relational
Database Management Systems (RDBMS) [50] use
relational data model given by Dr. E.F. Codd.
RDBMS maintain data in tables and
which are created among data and tables. Database is
divided into tables and they are connected through a
"key field". RDBMS is the most famous and used
database model.
Over last four decades, RDBMS remain a key
technology to store structured data. But with growing
size of data, companies do need modern technologies
to maintain and process data. RDBMS are not that
good for large data volumes with varying datatypes.
They also have scalability problem and often result
Database Application Database Management
System
Research and Development (IJTSRD)
www.ijtsrd.com
6 | Sep – Oct 2018
Oct 2018 Page: 617
or Big Data Management
Assistant Professor, Department of Computer Science and Engineering,
Baramulla, Jammu and Kashmir, India
Database also provides a mechanism for querying,
creating, modifying and deleting data. A list can also
be used to store data but in a list, redundancy is a
major issue. A database can store relationships and
data that are more complicated than a simple list with
lesser or no redundancy. A relational database stores
data in tables. Normally a table is based on one
information theme. For example, an employee list can
be divided into manager table, intern table, and junior
staff table. A table is a two dimensional grid of data
that contains columns and rows. The convention in
relational database world is that columns represent
n entity and each row
represents the instance of the entity.
creates and manages the database. DBMS can be
categorized based on its data model. Relational
Database Management Systems (RDBMS) [50] use
relational data model given by Dr. E.F. Codd.
RDBMS maintain data in tables and relationships
which are created among data and tables. Database is
divided into tables and they are connected through a
"key field". RDBMS is the most famous and used
Over last four decades, RDBMS remain a key
d data. But with growing
size of data, companies do need modern technologies
to maintain and process data. RDBMS are not that
good for large data volumes with varying datatypes.
They also have scalability problem and often result
Database

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
into failure while performing distributed sharding.
Oracle Real Application Clusters (RAC) is a
relational database cluster that provides high
availability, reliability and performance. Also,
MySQL cluster is another example where relational
databases scale on large cluster. RDBMS
ACID (Atomicity, Consistency, Isolation and
Durability) properties defined by Jim Gray in the late
1970s. Consistency is bottleneck for scalability of
relational databases. RDBMS follow strict data model
and can not violate ACID properties. That is
NoSQL data stores were developed to address the
challenges of traditional databases.
II. NOSQL DATABASES
In a computing system, huge amount of data comes
out every day from the web. A large section of these
data is handled by Relational database
systems (RDBMS). The idea of relational model came
with E. F. Codd’s 1970 paper named "A relational
model of data for large shared data banks" which
made data modelling and application programming
much easier. Beyond the benefits, the relational
is also well-suited for client-server programming and
today it is a predominant technology for storing
structured data in web and business applications
.Applications also grow with time and pose
challenging demands for the data management. As
stated by Jim Gray, the most challenging part is to
understand the data and find patterns, trends,
anomalies and extract the relevant information. With
the advent of Web 2.0 applications, the data stores
needed to scale to OLTP/OLAP-style application
loads where millions of users read and update the
information, in contrast to the traditional data stores.
These data stores provide good horizontal scalability
for the simple read/write operations distributed over
many servers. The relational database systems have
little capability to horizontally scale to these levels.
So, this paved the way to seek alternative solutions for
scenarios where relational database systems proved to
be not the right choice. NOSQL database are growing
fast and are best choice for handling the big world
problem popularly known as Big Data and supporting
Business Intelligence in organisations. Today we need
rich mobile apps highly available, very responsive and
not affected by network availability. To develop such
modern mobile apps NOSQL (mobile databases) are
the best solution for modern mobile app development.
NOSQL use wide variety of different DB
technologies that came into existence in response to
the demands present in building modern applications.
ing distributed sharding.
Oracle Real Application Clusters (RAC) is a
relational database cluster that provides high
availability, reliability and performance. Also,
MySQL cluster is another example where relational
databases scale on large cluster. RDBMS satisfy
ACID (Atomicity, Consistency, Isolation and
Durability) properties defined by Jim Gray in the late
1970s. Consistency is bottleneck for scalability of
relational databases. RDBMS follow strict data model
and can not violate ACID properties. That is why
were developed to address the
In a computing system, huge amount of data comes
out every day from the web. A large section of these
data is handled by Relational database management
systems (RDBMS). The idea of relational model came
Codd’s 1970 paper named "A relational
model of data for large shared data banks" which
made data modelling and application programming
much easier. Beyond the benefits, the relational model
server programming and
today it is a predominant technology for storing
structured data in web and business applications
Applications also grow with time and pose
challenging demands for the data management. As
by Jim Gray, the most challenging part is to
understand the data and find patterns, trends,
anomalies and extract the relevant information. With
the advent of Web 2.0 applications, the data stores
style application
millions of users read and update the
information, in contrast to the traditional data stores.
These data stores provide good horizontal scalability
for the simple read/write operations distributed over
many servers. The relational database systems have
little capability to horizontally scale to these levels.
So, this paved the way to seek alternative solutions for
scenarios where relational database systems proved to
be not the right choice. NOSQL database are growing
g the big world
problem popularly known as Big Data and supporting
Business Intelligence in organisations. Today we need
rich mobile apps highly available, very responsive and
not affected by network availability. To develop such
mobile databases) are
the best solution for modern mobile app development.
NOSQL use wide variety of different DB
technologies that came into existence in response to
the demands present in building modern applications.
Mobile world is one of the most dyna
Information Technology today. Smart
tablets have created a huge market for mobile
applications. Consequently there is an increasing
demand for mobile application developers. Almost all
of the mobile applications require a persistent
layer, including options for queries. So the interest of
database professionals, academics and researchers for
mobile technologies is increasing. NOSQL approach
is a strong competitor to the relational model because
it supports high scalability. The
describes that not any database system supports all the
three attributes but only two of three is possible.
Relational databases support only consistency and
partition tolerance properties and the NOSQL
databases support the last two mea
partition tolerance for high availability and
partitioning of data.
Types:
There are three main types of NoSQL data
Key-Value Data stores, Extensible Record Data
and Document Data stores.
In the Key-Value data stores
indexed with keys and its data model follows a
famous memcached distributed in
Examples include Project Voldemort, Riak and
Redis.
Document data stores retrieve, manage and store
semi structured data. They provide support for
multiple forms of documents (object). The values
are stored in documents as lists or nested
documents. Few examples are MongoDB,
SimpleDB, and CouchDB.
Extensible Record data stores are motivated from
Google's Big Table. It has flexible data model
with rows and columns. Rows and columns can
split over multiple nodes. HBase, Hyper
and PNUTS are its few examples.
III. DOCUMENT DATA STORES
Document oriented data stores
retrieve and manage semi structured data. They
support multiple types of documents (objects) per
stores. "Documents" save values as nested documents
or lists. These documents are of any type ranging
from PDF, Word document, XML, HTML, etc.
SimpleDB, CouchDB, and MongoDB are few
examples of Document Oriented datastore.
MongoDB: MongoDB is an open source, document
oriented datastore that is written in C++. It is
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Oct 2018 Page: 618
Mobile world is one of the most dynamic areas of
Information Technology today. Smart phones and
tablets have created a huge market for mobile
applications. Consequently there is an increasing
demand for mobile application developers. Almost all
of the mobile applications require a persistent data
layer, including options for queries. So the interest of
database professionals, academics and researchers for
mobile technologies is increasing. NOSQL approach
is a strong competitor to the relational model because
it supports high scalability. The famous CAP theorem
describes that not any database system supports all the
three attributes but only two of three is possible.
Relational databases support only consistency and
partition tolerance properties and the NOSQL
databases support the last two means availability and
partition tolerance for high availability and
There are three main types of NoSQL data stores:
stores, Extensible Record Data stores
data stores, the values are
indexed with keys and its data model follows a
famous memcached distributed in-memory cache.
Examples include Project Voldemort, Riak and
Document data stores retrieve, manage and store
semi structured data. They provide support for
of documents (object). The values
are stored in documents as lists or nested
documents. Few examples are MongoDB,
SimpleDB, and CouchDB.
Extensible Record data stores are motivated from
Table. It has flexible data model
and columns. Rows and columns can
split over multiple nodes. HBase, Hyper Table,
and PNUTS are its few examples.
DATA STORES
data stores are design to store,
retrieve and manage semi structured data. They
of documents (objects) per data
. "Documents" save values as nested documents
or lists. These documents are of any type ranging
Word document, XML, HTML, etc.
SimpleDB, CouchDB, and MongoDB are few
examples of Document Oriented datastore.
MongoDB is an open source, document
oriented datastore that is written in C++. It is

developed by 10gen (Now MongoDB Inc.) for a wide
variety of real time applications. It also provides full
index support for collection of documents. MongoDB
has a well structured document query mechanism.
Next few subsections discuss different aspects of
MongoDB design.
NoSQL data stores are quite handy to deal with much
large velocity and volume of data. MongoDB is a
scalable and high performance NoSQL datastor
an agile datastore that allows schemas to change
quickly as applications evolve. It is provided with the
rich querying capabilities. MongoDB is a real time
datastore usually used for online data but also find
applicability in wide variety of indus
MongoDB package has different tools. Depending on
operating system, the MongoDB package has
different package components. Mongod, mongo and
mongos are the core processes of MongoDB package.
Mongod is responsible for database whereas mongos
is for sharded cluster. Mongo is the interactive shell or
the client. For the Windows environment, there are
specific services like mongod.exe and mongos.exe.
Figure: MongoDB Packag
IV. EXTENSIBLE RECORD DATA STORES
Google's Big Table is the motivation for extensible
record database engines. It has a flexible data model
with rows and columns which can be extended any
time. Apache HBase, Apache Accumulo, and
developed by 10gen (Now MongoDB Inc.) for a wide
variety of real time applications. It also provides full
index support for collection of documents. MongoDB
a well structured document query mechanism.
Next few subsections discuss different aspects of
are quite handy to deal with much
large velocity and volume of data. MongoDB is a
scalable and high performance NoSQL datastore. It is
an agile datastore that allows schemas to change
quickly as applications evolve. It is provided with the
rich querying capabilities. MongoDB is a real time
datastore usually used for online data but also find
applicability in wide variety of industries. The
MongoDB package has different tools. Depending on
operating system, the MongoDB package has
different package components. Mongod, mongo and
mongos are the core processes of MongoDB package.
Mongod is responsible for database whereas mongos
is the interactive shell or
the client. For the Windows environment, there are
specific services like mongod.exe and mongos.exe.
Different tools for data and binary import/export
functionalities are the part of MongoDB package.
Different MongoDB tools are depicted in the
following figure. Mongod is the primary daemon
process for the MongoDB system. It takes care of data
requests, manages data format and executes
background management operations. Datastore is a
physical container for collections. Each datastore gets
its own set of files on the file system. A single
MongoDB server typically has multiple
Unlike Extensible Record store datastore like HBase,
MongoDB does not require a file system to run.
Collection is a group of MongoDB documenet and is
equivalent to a RDBMS table. Collections do not
enforce any type of schema. Documents within a
collection can have different fields. Normally, all
documents in a collection are of similar or related
purpose. Inside one collection, user can have "n"
number of documents. Document has a JavaScript
Object Notation (JSON) structure that stores a set of
key/value pairs. Normally, all documents in a
collection are of similar purpose.
Figure: MongoDB Package Components
DATA STORES
Google's Big Table is the motivation for extensible
record database engines. It has a flexible data model
with rows and columns which can be extended any
time. Apache HBase, Apache Accumulo, and
HyperTable are few of the famous Extensible Record
stores. Extensible record stores are scalable and both
rows and columns can split over multiple nodes.
Extensible Record stores are often term as Column
Oriented data stores.
Oct 2018 Page: 619
Different tools for data and binary import/export
functionalities are the part of MongoDB package.
ferent MongoDB tools are depicted in the
following figure. Mongod is the primary daemon
process for the MongoDB system. It takes care of data
requests, manages data format and executes
background management operations. Datastore is a
collections. Each datastore gets
its own set of files on the file system. A single
MongoDB server typically has multiple data stores.
Unlike Extensible Record store datastore like HBase,
MongoDB does not require a file system to run.
p of MongoDB documenet and is
equivalent to a RDBMS table. Collections do not
enforce any type of schema. Documents within a
collection can have different fields. Normally, all
documents in a collection are of similar or related
ion, user can have "n"
number of documents. Document has a JavaScript
Object Notation (JSON) structure that stores a set of
key/value pairs. Normally, all documents in a
collection are of similar purpose.
HyperTable are few of the famous Extensible Record
tensible record stores are scalable and both
rows and columns can split over multiple nodes.
Extensible Record stores are often term as Column

HBase: HBase is a Column Oriented data store that
runs on top of HDFS. HBase is an open sou
Apache project which can be summarized as
distributed, fault tolerant scalable data store. It is good
in managing sparse data sets. Unlike a relational
database management system (RDBMS), it does not
support structured query language like SQL. In fact,
HBase is not at all a relational database. HBase is
written in Java much like a typical Hadoop
application but it does not use MapReduce. HBase
applications can also be written using AVRO, REST
and THRIFT API. A HBase system is made up of set
of tables. These tables are stored in HDFS. Each table
contains rows and columns much like a traditional
V. KEY-VALUE DATA STORES
Key-value: Key-value data stores are primarily a big
hash tables with unique primary key and a pointer to a
particular data item. Its data model has identical
design to the memcached in memory cache. The keys
can be primitive types or objects and values are
accessed only by keys. These data stores provide sup
port for much functionality like replication, partition,
locking, versioning, transactions and/or other features.
They are extremely useful in building specialized
application with super fast query capabilities.
Cassandra, Redis, Riak, Scalaris, and Project
Voldemort are few examples of key-value
Cassandra: Cassandra is a distributed, highly scalable
and fault tolerant NoSQL datastore. It is a structured
store with decentralized architecture. It was developed
by Facebook Inc. and its first release came out in
2008. The main aim to develop Cassandra is to meet
storage requirements of the Index Search Problem.
For this purpose, Facebook needed a datastore with
HBase is a Column Oriented data store that
runs on top of HDFS. HBase is an open source
Apache project which can be summarized as
distributed, fault tolerant scalable data store. It is good
in managing sparse data sets. Unlike a relational
database management system (RDBMS), it does not
support structured query language like SQL. In fact,
HBase is not at all a relational database. HBase is
written in Java much like a typical Hadoop
application but it does not use MapReduce. HBase
applications can also be written using AVRO, REST
and THRIFT API. A HBase system is made up of set
hese tables are stored in HDFS. Each table
contains rows and columns much like a traditional
database. Each table has a column defines as it
primary key and all calls to access the table must use
the primary key. HBase architecture has three layers
namely: the client layer, the server layer and the
storage layer. The client layer provides an interface to
the user. It has client library which is used to
communicate with the HBase installation. The storage
layer has a coordination system and a file system.
HDFS is the most commonly used file system for
HBase. Apache ZooKeeper is used as the coordination
service for HBase. A master server and the region
servers are two component of server layer. The
following figure describes the architecture overview
of HBase.
Figure: HBase Architectur
DATA STORES
are primarily a big
hash tables with unique primary key and a pointer to a
particular data item. Its data model has identical
design to the memcached in memory cache. The keys
can be primitive types or objects and values are
ata stores provide sup-
like replication, partition,
locking, versioning, transactions and/or other features.
They are extremely useful in building specialized
application with super fast query capabilities.
k, Scalaris, and Project
value data stores.
Cassandra: Cassandra is a distributed, highly scalable
and fault tolerant NoSQL datastore. It is a structured
store with decentralized architecture. It was developed
Inc. and its first release came out in
2008. The main aim to develop Cassandra is to meet
storage requirements of the Index Search Problem.
For this purpose, Facebook needed a datastore with
very high write throughput. Apache Cassandra is an
open source project under the Apache license 2.0.
In traditional databases that can be deployed over
multiple nodes and even in
Google's Bigtable etc, master slave relationship exist
between the nodes. The master is authority for
distributing and managing data. Slaves on other hand
synchronize their data to the master. All writes pass
via master and it is the single point of failure. The
architectures that have master/slave setup sometime
have adverse effect if master node fails.
By contrast, Cassandra was designed with the
understanding that failures can and do occur. It has a
peer-to-peer distribution model. The data is divided
among all nodes in the cluster. All nodes are
structurally identical. Therefore, there is no master
node. Equality among nodes due to peer
network improves general datastore ability. It also
makes scaling up and scaling down much easier
because a new node will not be treated differently.
The following figure describes the Cassandra Read
Repair.
Oct 2018 Page: 620
database. Each table has a column defines as it
primary key and all calls to access the table must use
the primary key. HBase architecture has three layers
y: the client layer, the server layer and the
storage layer. The client layer provides an interface to
the user. It has client library which is used to
communicate with the HBase installation. The storage
layer has a coordination system and a file system.
HDFS is the most commonly used file system for
HBase. Apache ZooKeeper is used as the coordination
service for HBase. A master server and the region
servers are two component of server layer. The
following figure describes the architecture overview
very high write throughput. Apache Cassandra is an
roject under the Apache license 2.0.
In traditional databases that can be deployed over
multiple nodes and even in data stores like HBase,
Google's Bigtable etc, master slave relationship exist
between the nodes. The master is authority for
nd managing data. Slaves on other hand
synchronize their data to the master. All writes pass
via master and it is the single point of failure. The
architectures that have master/slave setup sometime
have adverse effect if master node fails.
ssandra was designed with the
understanding that failures can and do occur. It has a
peer distribution model. The data is divided
among all nodes in the cluster. All nodes are
structurally identical. Therefore, there is no master
ng nodes due to peer-to-peer
network improves general datastore ability. It also
makes scaling up and scaling down much easier
because a new node will not be treated differently.
The following figure describes the Cassandra Read

VI. CONCLUSION
Big Data is a very popular term today represented by
3V i.e volume, variety and velocity of data. The
research paper focus on new breed of databases
Figure: Read Repair of Cassandra
REFRENCES:
1. A B Moniruzzaman and Syed AkhtarHossain,
“NOSQL Database: New Era of Databases for
Big Data Analytics- Classification, Comparison
and Characteristics”, International Journal of
Database Theory and application.
2. Aaron Schram and Kenneth M. Anderson,
“MySQL to NOSQL: Data Modelling
challenges In Supporting Scalability
3. AmeyaNayak, Anil Poriya, and DikshayPoojary,
“Types of NOSQL Databases and its
comparison with the Relational Databases”,
International Journal of Applied Information
Systems, Volume 5 No. 4, March 2013,
www.ijais.org.
4. AnkitaBhatewara and KalyaniWaghmare,
“Improving Network Scalability using NoSql
Database”, International Journal of Advanced
Computer Research, Volume-2, Number
6, December-2012.
5. Chad DeLoatch and Scott Blindt, “
Databases: Scalable Cloud and Enterprise
Solutions”, August 2, 2012.
6. Chieh Ming Wu, Yin Fu Huang, John Lee,
“Comparisons between MongoDB and MS
Big Data is a very popular term today represented by
3V i.e volume, variety and velocity of data. The
research paper focus on new breed of databases
commonly known as NOSQL Databases and how
they are beneficial for managing huge volumes of
data.
Figure: Read Repair of Cassandra
A B Moniruzzaman and Syed AkhtarHossain,
NOSQL Database: New Era of Databases for
Classification, Comparison
International Journal of
Aaron Schram and Kenneth M. Anderson,
MySQL to NOSQL: Data Modelling
challenges In Supporting Scalability”.
AmeyaNayak, Anil Poriya, and DikshayPoojary,
Types of NOSQL Databases and its
with the Relational Databases”,
International Journal of Applied Information
Systems, Volume 5 No. 4, March 2013,
AnkitaBhatewara and KalyaniWaghmare,
Improving Network Scalability using NoSql
International Journal of Advanced
2, Number-4, Issue-
Chad DeLoatch and Scott Blindt, “NOSQL
Databases: Scalable Cloud and Enterprise
Chieh Ming Wu, Yin Fu Huang, John Lee,
Comparisons between MongoDB and MS-
SQL Databases on the TWC Website
American Journal of Software Engineering and
Applications, Volume 4, No 3, April 2015.
7. Clarence J M Tauro, Aravindh S and Shreeharsha
A.B, “Comparative Study of the New
Generation, Agile, Scalable, High Performance
NOSQL Databases”, International Journal of
Computer Applications, volume 48
2012.
8. David Taniar, “High Performance Database
Processing”, 26th
IEEE International Conference
on Advanced Information
Applications, 2012.
9. DrK.Chitra and B.Jeevarani, “
Available, Scalable, and Eventually Consistent
NOSQL Databases”, Volume3, Issue7, July
2013, www.ijarcsse.com.
10. Felix Gessert, Wolfram Wingerath, Steffen
Friedrich, Norbert Ritter, “
systems: a survey and decision guidance
Springer, November 2016.
11. GuoYubin, Zhang Liankuan, Lin Fengren, Li
Ximing, “A Solution for Privacy
Data Manipulation and
Oct 2018 Page: 621
commonly known as NOSQL Databases and how
they are beneficial for managing huge volumes of
ses on the TWC Website”,
American Journal of Software Engineering and
Applications, Volume 4, No 3, April 2015.
Clarence J M Tauro, Aravindh S and Shreeharsha
Comparative Study of the New
Generation, Agile, Scalable, High Performance
International Journal of
Computer Applications, volume 48-No. 20, June
High Performance Database
IEEE International Conference
Advanced Information Networking and
DrK.Chitra and B.Jeevarani, “Study on Basically
Available, Scalable, and Eventually Consistent
Volume3, Issue7, July
Felix Gessert, Wolfram Wingerath, Steffen
Friedrich, Norbert Ritter, “NoSQL database
systems: a survey and decision guidance”,
Springer, November 2016.
GuoYubin, Zhang Liankuan, Lin Fengren, Li
A Solution for Privacy-Preserving
and Query on NOSQL

database”, Journal of Computers, Vol 8, No. 6,
June 2013.
12. Hanen Abbes, FaiezGargouri,
Integration: a MongoDB Database and
Modular Ontologies based Approach”,
September 2016.
13. InduArora and andDrAnu Gupta, “
Databases: A Paradigm Shift in Databases”,
International Journal of Computer Science Issues,
Vol 9, Issue 4, No. 3, July 2012, www.IJCSI.com
14. IoannisKonstantinou, Evangelos Angelou,
Christina Boumpouka, DimitriosTsoumakos,
NectariosKoziris, “On the Elasticity of NOSQL
Databases over Cloud Management Platforms
(extended version)”, Computing Systems
Laboratory, School of Electrical and Computer
Engineering National Technical University of
Athens.
15. Joao Ricardo Lourenco, Bruno Cabral, Paulo
Carreiro, Marco Vieira, Jorge Bernardino,
“Choosing the right NoSQL database for
thejob: a quality attribute evaluation”
of Big Data, Springer, 2015.
16. Katarina Grolinger, Wilson A Higashino1,
AbhinavTiwari,Miriam AM Capretz, “
management in cloud environments:
NoSQLand NewSQL data stores
Cloud Computing, Springer, 2013.
17. Leonardo Rocha, Fernando Vale, E
Darlinton Barbosa, FernandoMourao, “
Journal of Computers, Vol 8, No. 6,
Hanen Abbes, FaiezGargouri, “Big Data
a MongoDB Database and
Modular Ontologies based Approach”, Elsevier,
InduArora and andDrAnu Gupta, “Cloud
Databases: A Paradigm Shift in Databases”,
International Journal of Computer Science Issues,
www.IJCSI.com
IoannisKonstantinou, Evangelos Angelou,
Christina Boumpouka, DimitriosTsoumakos,
On the Elasticity of NOSQL
Databases over Cloud Management Platforms
”, Computing Systems
tory, School of Electrical and Computer
Engineering National Technical University of
Joao Ricardo Lourenco, Bruno Cabral, Paulo
Carreiro, Marco Vieira, Jorge Bernardino,
Choosing the right NoSQL database for
thejob: a quality attribute evaluation” Journal
Katarina Grolinger, Wilson A Higashino1,
AbhinavTiwari,Miriam AM Capretz, “Data
management in cloud environments:
NoSQLand NewSQL data stores”Journal of
Leonardo Rocha, Fernando Vale, Elder Cirilo,
Darlinton Barbosa, FernandoMourao, “A
Framework for Migrating Relational Datasets
to NoSQL”, Elsevier, Volume 51, 2015.
18. Liana Stanescu, Marius Brezovan, and Dumitru
Dan Burdescu, “An algorithm for mapping the
relational databases to
study”,International Journal of Computer Science
and Applications, January
2017,https://www.researchgate.net/publication/31
8599517.
19. Lior Okman, Nurit Gal-Oz, YaronGonen, Jenny
Abramov, “Security Issues in NoSQL
Databases”, International Joint Confere
IEEE TrustCom, 2011.
20. Marin FOTACHE and Dragos COGEAN,
“NOSQL and SQL Databases for the Mobile
Applications. Case study:
MongoDBVsPostgreSQL”,
2/2013.
21. Nadeem Qaisar Mehmood, Rosario Culmone,
Leonardo Mostarda, “Modeling temporal aspects
of sensordata for MongoDBNoSQL database
Journal of Big Data, Springer, 2017.
22. Naseer Ganiee, “New Database Constraints and
Modern Applications”, IJLTEMAS, Volume III,
Issue II, February 2014.
23. Naseer Ganiee, “NOSQL: The Big Data
Solution”, International J
in Engineering Technology, Management and
Applied Sciences, Volume 1, Issue 2, July 2014.
Oct 2018 Page: 622
Framework for Migrating Relational Datasets
”, Elsevier, Volume 51, 2015.
Liana Stanescu, Marius Brezovan, and Dumitru
An algorithm for mapping the
relational databases toMongodb – a case
International Journal of Computer Science
and Applications, January
https://www.researchgate.net/publication/31
Oz, YaronGonen, Jenny
Security Issues in NoSQL
”, International Joint Conference of
Marin FOTACHE and Dragos COGEAN,
NOSQL and SQL Databases for the Mobile
Applications. Case study:
MongoDBVsPostgreSQL”, Volume 17, No
Nadeem Qaisar Mehmood, Rosario Culmone,
Modeling temporal aspects
of sensordata for MongoDBNoSQL database”,
Journal of Big Data, Springer, 2017.
New Database Constraints and
IJLTEMAS, Volume III,
NOSQL: The Big Data
International Journal of Advancement
in Engineering Technology, Management and
Applied Sciences, Volume 1, Issue 2, July 2014.

NOSQL Database Engines for Big Data Management

More Related Content

What's hot (18)

Similar to NOSQL Database Engines for Big Data Management (20)

More from ijtsrd (20)

Recently uploaded (20)

NOSQL Database Engines for Big Data Management