This document proposes a hybrid database system that integrates a NoSQL database (MongoDB) and a relational database (MySQL) to address the limitations of each individual system for big data storage and management. It discusses the properties of big data, reviews the approaches of relational and NoSQL databases, highlights their strengths and weaknesses, and then describes the proposed hybrid system that categorizes data as structured or unstructured and stores it in the appropriate database to leverage the benefits of both models. The system is designed to enhance big data storage and management by bridging the gaps between relational and NoSQL approaches.
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
Representing Non-Relational Databases with Darwinian NetworksIJERA Editor
The Darwinian networks (DNs) are first introduced by Dr Butz [1] to simplify and clarify how to work with Bayesian networks (BNs). DNs can unify modeling and reasoning tasks into a single platform using the graphical manipulation of the probability tables that takes on a biological feel. From this view of the DNs, we propose a graphical library to represent and depict non-relational databases using DNs. Because of the growing of this kind of databases, we need even more tools to help in the management work, and the DNs can help with these tasks.
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
Representing Non-Relational Databases with Darwinian NetworksIJERA Editor
The Darwinian networks (DNs) are first introduced by Dr Butz [1] to simplify and clarify how to work with Bayesian networks (BNs). DNs can unify modeling and reasoning tasks into a single platform using the graphical manipulation of the probability tables that takes on a biological feel. From this view of the DNs, we propose a graphical library to represent and depict non-relational databases using DNs. Because of the growing of this kind of databases, we need even more tools to help in the management work, and the DNs can help with these tasks.
What is NoSQL? NoSQL describes a family of approaches to managing data at an enterprise level that have key similarities, but - at the same time - are very different from classic SQL based relational databases.
NoSQL has emerged as a 'movement' over the last 5 years and many specific noSQL datastores - Mongo, Redis, HBase, Cassandra, Neo4J - are being used for mission critical systems by many organizations including Facebook, LinkedIn, Dropbox, American Express, NSA, & the CIA. Does NoSQL spell the end of SQL based relational datastores like Oracle, MySQL, SQLServer, & Sybase? Definitely not, but the world is moving in the direction of "Polyglot Persistence" and away from the "Relational Persistence" hegemony. In my presentation I will explain why this shift is occurring and will speculate about what the future will hold.
Database concepts and Archeticture Ch2 with in class ActivitiesZainab Almugbel
This is the slides of chapter 2 of the book Ramez Elmasri and Shamkant Navathe, "Fundamentals of Database Systems" 6th Edition, 2010
I did not include the activities in the slides. I printed them out in separate papers. Then, I asked students: who liked to participate in activity 1 (the interview) in the class. I selected 2 students for the first activity (one was the interviewer and another was the guest). I did the same for the other activities.
TOP NEWSQL DATABASES AND FEATURES CLASSIFICATIONijdms
Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce cost commodity
nodes. Out work emphasize on how big data is addressed through top NewSQL databases considering their
features. This NewSQL databases paper conveys some of the top NewSQL databases [54] features collection
considering high demand and usage. First part, around 11 NewSQL databases have been investigated for
eliciting, comparing and examining their features so that they might assist to observe high hierarchy of
NewSQL databases and to reveal their similarities and their differences. Our taxonomy involves four types
categories in terms of how NewSQL databases handle, and process big data considering technologies are
offered or supported. Advantages and disadvantages are conveyed in this survey for each of NewSQL
databases. At second part, we register our findings based on several categories and aspects: first, by our
first taxonomy which sees features characteristics are either functional or non-functional. A second
taxonomy moved into another aspect regarding data integrity and data manipulation; we found data
features classified based on supervised, semi-supervised, or unsupervised. Third taxonomy was about how
diverse each single NewSQL database can deal with different types of databases. Surprisingly, Not only do
NewSQL databases process regular (raw) data, but also they are stringent enough to afford diverse type of
data such as historical and vertical distributed system, real-time, streaming, and timestamp databases.
Thereby we release NewSQL databases are significant enough to survive and associate with other
technologies to support other database types such as NoSQL, traditional, distributed system, and semirelationship
to be as our fourth taxonomy-based. We strive to visualize our results for the former categories
and the latter using chart graph. Eventually, NewSQL databases motivate us to analyze its big data
throughput and we could classify them into good data or bad data. We conclude this paper with couple
suggestions in how to manage big data using Predictable Analytics and other techniques.
Course Title: Database Programming with SQL
Course Code: DEE 431
TOPICS COVER:
Database Terminologies
Drawbacks of Traditional System
Data processing Modes
Application of DBMS
Types of Database
Histroy of Database
Characteristics of Database
Advantages and Disadvantages of Database
Types of database architecture: 1 Tier, 2 Tier, 3 Tier
What is a Database?
Database creation steps
Benefits of using Database
Types of Table Relationships
What is a Database model
Database Management System
Users of Database
MS Access
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
SURVEY ON IMPLEMANTATION OF COLUMN ORIENTED NOSQL DATA STORES ( BIGTABLE & CA...IJCERT JOURNAL
NOSQL is a database provides a mechanism for storage and retrieval of data that is modeled for huge amount of data which is used in big data and Cloud Computing . NOSQL systems are also called "Not only SQL" to emphasize that they may support SQL-like query languages. A basic classification of NOSQL is based on data model; they are like column, Document, Key-Value etc. The objective of this paper is to study and compare the implantation of various column oriented data stores like Bigtable, Cassandra.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
Many institutions and companies with technological development have been producing large size of structured and unstructured data. Therefore, we need special databases to deal with these data and thus emerged NoSQL databases. They are widely used in the cloud databases and the distributed systems. In the era of big data, those databases provide a scalable high availability solution. So we need new architectures to try to meet the need to store more and more different kinds of different data. In order to arrive at a good structure of large and diverse data, this structure must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we experiment the Riak key-value database to measure their performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Throughput and latency of the NoSQL database over different types of experiments and different sizes of data are compared and then results were discussed.
What is NoSQL? NoSQL describes a family of approaches to managing data at an enterprise level that have key similarities, but - at the same time - are very different from classic SQL based relational databases.
NoSQL has emerged as a 'movement' over the last 5 years and many specific noSQL datastores - Mongo, Redis, HBase, Cassandra, Neo4J - are being used for mission critical systems by many organizations including Facebook, LinkedIn, Dropbox, American Express, NSA, & the CIA. Does NoSQL spell the end of SQL based relational datastores like Oracle, MySQL, SQLServer, & Sybase? Definitely not, but the world is moving in the direction of "Polyglot Persistence" and away from the "Relational Persistence" hegemony. In my presentation I will explain why this shift is occurring and will speculate about what the future will hold.
Database concepts and Archeticture Ch2 with in class ActivitiesZainab Almugbel
This is the slides of chapter 2 of the book Ramez Elmasri and Shamkant Navathe, "Fundamentals of Database Systems" 6th Edition, 2010
I did not include the activities in the slides. I printed them out in separate papers. Then, I asked students: who liked to participate in activity 1 (the interview) in the class. I selected 2 students for the first activity (one was the interviewer and another was the guest). I did the same for the other activities.
TOP NEWSQL DATABASES AND FEATURES CLASSIFICATIONijdms
Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce cost commodity
nodes. Out work emphasize on how big data is addressed through top NewSQL databases considering their
features. This NewSQL databases paper conveys some of the top NewSQL databases [54] features collection
considering high demand and usage. First part, around 11 NewSQL databases have been investigated for
eliciting, comparing and examining their features so that they might assist to observe high hierarchy of
NewSQL databases and to reveal their similarities and their differences. Our taxonomy involves four types
categories in terms of how NewSQL databases handle, and process big data considering technologies are
offered or supported. Advantages and disadvantages are conveyed in this survey for each of NewSQL
databases. At second part, we register our findings based on several categories and aspects: first, by our
first taxonomy which sees features characteristics are either functional or non-functional. A second
taxonomy moved into another aspect regarding data integrity and data manipulation; we found data
features classified based on supervised, semi-supervised, or unsupervised. Third taxonomy was about how
diverse each single NewSQL database can deal with different types of databases. Surprisingly, Not only do
NewSQL databases process regular (raw) data, but also they are stringent enough to afford diverse type of
data such as historical and vertical distributed system, real-time, streaming, and timestamp databases.
Thereby we release NewSQL databases are significant enough to survive and associate with other
technologies to support other database types such as NoSQL, traditional, distributed system, and semirelationship
to be as our fourth taxonomy-based. We strive to visualize our results for the former categories
and the latter using chart graph. Eventually, NewSQL databases motivate us to analyze its big data
throughput and we could classify them into good data or bad data. We conclude this paper with couple
suggestions in how to manage big data using Predictable Analytics and other techniques.
Course Title: Database Programming with SQL
Course Code: DEE 431
TOPICS COVER:
Database Terminologies
Drawbacks of Traditional System
Data processing Modes
Application of DBMS
Types of Database
Histroy of Database
Characteristics of Database
Advantages and Disadvantages of Database
Types of database architecture: 1 Tier, 2 Tier, 3 Tier
What is a Database?
Database creation steps
Benefits of using Database
Types of Table Relationships
What is a Database model
Database Management System
Users of Database
MS Access
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
SURVEY ON IMPLEMANTATION OF COLUMN ORIENTED NOSQL DATA STORES ( BIGTABLE & CA...IJCERT JOURNAL
NOSQL is a database provides a mechanism for storage and retrieval of data that is modeled for huge amount of data which is used in big data and Cloud Computing . NOSQL systems are also called "Not only SQL" to emphasize that they may support SQL-like query languages. A basic classification of NOSQL is based on data model; they are like column, Document, Key-Value etc. The objective of this paper is to study and compare the implantation of various column oriented data stores like Bigtable, Cassandra.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
Many institutions and companies with technological development have been producing large size of structured and unstructured data. Therefore, we need special databases to deal with these data and thus emerged NoSQL databases. They are widely used in the cloud databases and the distributed systems. In the era of big data, those databases provide a scalable high availability solution. So we need new architectures to try to meet the need to store more and more different kinds of different data. In order to arrive at a good structure of large and diverse data, this structure must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we experiment the Riak key-value database to measure their performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Throughput and latency of the NoSQL database over different types of experiments and different sizes of data are compared and then results were discussed.
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
Many institutions and companies with technological development have been producing large size of structured and unstructured data. Therefore, we need special databases to deal with these data and thus emerged NoSQL databases. They are widely used in the cloud databases and the distributed systems. In the era of big data, those databases provide a scalable high availability solution. So we need new architectures to try to meet the need to store more and more different kinds of different data. In order to arrive at a good structure of large and diverse data, this structure must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we experiment the Riak key-value database to measure their performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Throughput and latency of the NoSQL database over different types of experiments and different sizes of data are compared and then results were discussed.
EVALUATION CRITERIA FOR SELECTING NOSQL DATABASES IN A SINGLE-BOX ENVIRONMENTijdms
In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to evaluate the usefulness of NoSQL systems in small-scale single-box environments. Specifically, key value, document, column family, and graph database are discussed with respect to the ability of each to provide CRUD transactions in a single-box environment
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENT
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 3/4, August 2017
DOI: 10.5121/ijcsea.2017.7402 15
HYBRID DATABASE SYSTEM FOR BIG DATA
STORAGE AND MANAGEMENT
Blessing E. James and P.O.Asagba
Department of Computer Science, University of Port Harcourt, Choba, Rivers State,
Nigeria
ABSTRACT
Relational database systems have been the standard storage system over the last forty years. Recently,
advancements in technologies have led to an exponential increase in data volume, velocity and variety
beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational
database for data storage and management. Some core features of database system such as ACID have
been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and
management of extremely voluminous data of diverse components known as big data, such that the two
models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having
these two databases in one system can enhance storage and management of big data bridging the gap
between relational and NoSQL storage approach.
KEYWORDS
ACID, BASE, Big data, NoSQL, SQL, MongoDB
1. INTRODUCTION
Rapid industrialization, greater affordability of devices, sudden escalation in the usage of
portable devices and recent advancement in technological knowledge such as the connection of
every object to the internet known as Internet of Things (IoT), mesh or cloud computing, big user
etc. have led to an inconceivably massive realm of data known as big data[18][19]. Generally,
big data is regarded as enormous volume (terabyte and pentabyte) of data consisting of various
data types (structured, semi-structured and unstructured) and of real time availability (velocity) so
that it becomes impossible for such data to be stored or managed using means like traditional
relational database systems. In simple words, big data is data whose volume and nature (semi-
structured and unstructured data type) is greater than what conventional database systems could
handle. This enormous increase in data quantity has opened up great opportunities for significant
scientific achievements, improved business strategies, health care methods etc. [14]
Relational database systems have served as the actual storage systems for several years. However,
within the last four years, there have been great resolutions in the computing world that have
lessened the prevalent of relational databases which has led to an up thrust in the consideration of
new storage model called NoSQL [17]. This is because relational databases were never designed
to store or manage such volume of data, with high velocity and variety, unstructured data or rapid
growth [15]. Enterprises are therefore turning to a new emerging storage approach called NoSQL
for solutions to inherent challenges in big data. Most NoSL scale horizontally with increase in
data volume and are also sufficiently flexile in order to hold partially structured and dispersed
data collection [11]. With NoSQL, data in the form of voice, video, email, and documents can be
properly stored and managed. However, as appealing as this approach is, it is not without
limitations. The innovators of NoSQL will-fully or perhaps unintentionally left out some
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desirable database ingredient such as transactions, and security in order to achieve what relational
databases could not offer .
In this work, we developed a hybrid database system using MongoDB and MySQL databases
which are popular variants of NoSQL database and relational database systems respectively. Prior
to data storage, data is categorized into structured data and unstructured data depending on the
nature of data. Unstructured data are stored and managed in the MongoDB database while storage
and management of strictly structured data is carried out using MySQL database. Our hybrid
system is such that, the databases making up the system can function separately for instance, our
system could be used as a separate and complete MongoDB database. Instead of giving up the
functions of relational database systems for NoSQL database we have developed an approach that
offers the benefits of the both systems in a single database system.
2. RELATIONAL DATABASE STORAGE APPROACH
The paradigm governing relational databases are built on the principles of relations in
mathematics. Many of the prevalent and free databases are in this class. Being able to store data
in tables of rows and columns while retaining and imposing the relationship between the sored
data is one of the basic traits of relational databases [1]. The basic rules of relational databases
[1] are:
• Data and information must be stored in tables of rows and columns.
• To access the content of a column, the name of the table, column and the primary key
must be specified.
• Cases of absent and inappropriate entry must be handled systematically different from
expected entries and not dependent on the type of data being entered.
• The Database Management System should support an active online catalogue.
• There must be at least one language supported by the Database Management System
which could be used either separately or within programs.
• The Database Management System must be able to update views
• Basic operations such as insertion, updating and deletion must be supported by the
Database Management System.
• Modifications performed on the logical structure such as adding or removing columns
from tables must not affect the user views.
• Changes at the physical level such as storage must not affect the entire application.
• Restrictions pertaining to Integrity should be isolated from the application.
• In distributed environment, the impact of database distribution must be perceived by user.
In relational model, data is represented in tables or relations. A table as shown in table 1 is
primarily a collection of related data entries and it is made up of several columns and rows
referred to as fields and records respectively. Almost all databases built on relational model
guarantee Atomicity Consistency Isolation and Durability transactions.
Table 1: Tabular data representation in relational database
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3. NOSQL DATABASE STORAGE APPROACH
NoSQL databases are Object Oriented databases designed to address processing issues created by
expanding data volumes and diversity, particularly in big data applications. NoSQL databases are
considered to be very necessary in situation where the volume of data is far beyond what could be
handled by relational database and also the information constituent is such that must not be stored
in a relational database. NoSQL databases are built on distributed model to guarantee Basically
Available, Soft-state, Eventual consistency (BASE) properties [21]. Figure 1 shows a model of
document base NoSQL database. In this model instead of storing data in rows and columns in a
table, data which may fill several columns in a table could be stored in documents which are
grouped into collections. There are four basic categories of NoSQL database [7].
• Key-Value Store: Data is stored using two connected but distinct items- a distinctive
identifier called key and a corresponding value which could be a data or a pointer to the
location of the data. It is very suitable for key based systems. E.g. Dynamo, Riak
• Column family store: Data is organized in rows and columns as in relational database
systems. E.g. Cassandra
• Graph family store: for processes which could be represented in the form of relationship
with interconnected elements such as social network. E.g. Neo4j
• Document Store: Data is stored in documents .Appropriate for storage of documents in
diverse format. E.g. MongoDB
3.1 STRENGTH OF NOSQL DATABASE AND LIMITATIONS OF RELATIONAL DATABASE
NoSQL databases were developed to eliminate the limitations or drawbacks encountered in the
use of relational databases especially in big data storage enronments. As such, most of the
drawbacks in the relational storage system form the strength or advantages of NoSQL database
systems. The weaknesses of relational databases and the strength of NoSQL databases depend
mostly on the features discussed in the following sections.
3.1.1 SCALABILITY
In relational storage systems, expansion is achieved by replacing the existing storage or server
with a bigger (more expensive) server which implies increasing the horse power of the existing
hardware . This is known as the vertical scaling or scaling up . It is obvious that as the volumn of
data increases, it may get to a stage that the biggest affordable server may not be able to meet the
storage requirement as shown in figure 2 , this may in turn reduce the system’s performance. And
also the system is plagued with a single point of failure. NoSQL databases are built on
distributed architecture such that partitioning (sharding) of a database across several servers is
possible. As such expansion is achieved through the addition of inexpensive servers connected to
the database cluster shown in figure 3. This is known as horizontal scaling or scaling out.
Horizontal scaling increases system’s performance at minimal cost by promoting rapid data
expansion and eliminating the single point of failure existing in relational databases.
Figure 1: Document base data representation (Source: [23])
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Figure 2.Vertical scaling in relational databases. (Source [23])
3.1.2 FLEXIBILITY
Relational databases are schema-agnostic; data cannot be stored without defining the schema of
such data. As such in big data environment where there is need for storage of unstructured data, it
is impossible to know the schema or structure of data beforehand. NoSQL on the other hand have
dynamic schema such that the schema must not be pre-defined. As such, NoSQL database could
be for storage of both structured and unstructured data.
WEAKNESSES OF NOSQL DATABASE
Solving some of the problems in relational databases introduced certain weaknesses in NoSQL
database. Some of the weaknesses of NoSQL database are:
• Complex transactions: MongoDB does not support multi-document transactions. With the
availability of the NoSQL databases, support for ACID transactions across documents
was typically given up. Exclusion of ACID transaction is a trade-off used by NoSQL to
provide solution to issues pertaining to scalability.
• Stability: Some NoSQL databases are still in their pre-production phases and are
therefore not stable or matured enough for some sensitive task.
• Global Support: Enterprises demand global support and services from database vendors
when a core component of the system fails. NoSQL lacks such services to enterprise
customers.
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Figure 3. Horizontal scaling in NoSQL database. (Source [24])
4. BIG DATA
Big data refers primarily to group of data which have become extremely voluminous (petabytes
and terabyte) consisting of various data types (structured, semi-structured and unstructured) and
of real time availability (velocity) such that it is not efficient to be stored or processed with
traditional tools or means such as conventional database systems. These traditional tools have
been used over the years to hold, process and analyse voluminous set of information in
companies, industries etc. Big data does not only refer to collection of voluminous data, but
extremely large volume of structured and unstructured data subject to very high rate of change,
derived from divers avenues which may include email, social media, phone calls etc. Big data is
used to describe a collection of sophisticated data whose volume increases continuously and
rapidly at such rate that the use of conventional management tools for storage and analysis
becomes inefficient and inappropriate. The complexity in this enormous volume of dispersed
data is attributable to the fact that data collected from different sources may also have different
format and it may be necessary to integrate them into a unit for analysis.
4.1 PROPERTIES OF BIG DATA
Although the word big indicates the size of a thing, in big data, big is not limited to data volume
but also incorporates other attributes such as Velocity, and Variety. These three attributes
describe the primary properties of big data known as the 3 V’s of big data [22];
• Volume: This is sometimes taken to be the ultimate attribute of big data. It depicts very
large and ever growing amount of data ranging from terabyte (1012 byte) to yotta-byte
which is trillions of gigabyte.
• Velocity: Velocity refers to real time availability of data for processing. Big data is also
characterized by instantaneous arrival of enormous data for processing. It entails the rate
at which data is circulated within the system e.g. the velocity upon which data is derived
out of internal and external operations and sources such as interactions with machines,
humans, social media etc
• Variety: This represents the diverse format of data in a data set. Big data is made up of
data derived from various sources such as emails, machines, social networks, business
transactions, mobile devices etc. Data from different sources assume different forms such
as spread sheets, photos, videos etc. Variety as a property of big data describes divers
forms of data derived from diverse sources.
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• Veracity: Refers to the truthfulness of the data. It deals with the relevance of the data
(processed or analysed) to the task at hand. It reveals the need to avoid accumulation of
dirty data.
• Volatility: Deals with the reasonable life span of stored data in the world of real time data
processing. It investigates the validity of stored data to the current analysis.
• Validity: Decisions are as valid as the data used in the analyses
4.2 BIG DATA CHALLENGES
Big data challenges could also be referred to as the steps involved in the processing of this
extremely large volume of data of divers types and high velocity for use. To leverage the
numerous benefits of big data, the data must go through the following processes [5], in other
words, the following challenges must be overcome;
• Ingestion: The procedure through which data is obtained and imported for use or storage.
Data can be ingested once it is supplied by the source or grouped into batches and
ingested within specified interval. The process usually starts with ranking of data sources,
authenticating each file before channelling data to accurate destination.
• Storage: Storage is very complex; it includes search and retrieval of data and may also
include complex security and privacy issues. Storage framework for big data should be
able to hold large volumes of structured, semi-structured and unstructured data
efficiently.
• Analytic: Big data is almost useless without efficient analytic tools and procedures
through which useful information is extracted from what seems to be junk of data.
Analytics in the field of big data also include computations on big data. It covers
frameworks and tools such as Map Reduce and Hadoop that are used to derive meaning
from big data. The result of data analytics can be used to improved services rendered to
customers, marketing strategies, and general decision making.
• Visualization: This covers data presentation for easy identification of patterns or grasping
of new concepts. Big data visualization tools and techniques allow data representation in
the form of graphs, charts, maps and even videos making it easy for information
communication.
5. RELATED WORKS
[13] [2] presented a report on the classification, properties and comparison of NoSQL databases.
They explored the strength and weaknesses of the different types of NoSQL databases. [3]
Investigated the elasticity of NoSQL databases based on the scalability and speed of read and
update operations. It was shown that the speed at which read operation is performed in Hbase is
high whereas insert operation is fast in Cassandra and Riak is slow at both read and write
operations. [17] Carried out a detailed comparison between MongoDB and Microsoft SQL
databases. Microsoft SQL is a relational database system as such the storage capacity of the
system can only be increased by introducing a server with bigger capacity and this usually incurs
extra cost. NoSQL database on the other hand is non-relational system whose capacity could
easily scale horizontally to accommodate more data. The system was implemented in Java
programming language using Eclipse Integrated Development Environment. It was observed that
although MongDB and Microsoft SQL perform write operations faster than read operations, read
and write operations in MongoDB is almost ten times faster than read and write in Microsoft SQL
database. Although MongoDB has a higher read/write ability, there exists situations were speed
is not the ultimate or the only requirement for databases. MongoDB is not appropriate for heavy
transactional tasks. An evaluation of the performances of MongoDB, PostgreSQL and Cassandra
by [4] revealed that Cassandra is more appropriate for large distributed sensor systems. [6]
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Opined that more engineering task is demanded from programmers who depend on relational
databases for data storage and management.
6. PROPOSED SYSTEM
The aim of the proposed system is to design a Hybrid Database System for the storage and
management of big data. Our hybrid system is made up of MySQL database and MongoDB
which are the most popular relational and NoSQL (non-relational) database servers. Data is
grouped into structured and unstructured data category, structured data is channelled into the
MongoDB database, while the choice of database for the unstructured data depends on the mode
in which the application runs in; this could be MongoDB for hybrid mode and MySQL for SQL
mode. The databases integrated in the proposed system can also function in isolation.
[8] Presented an overview of the existing NoSQL databases using data model, query model,
replication and consistency model.
6.1 DESIGN OF PROPOSED SYSTEM
System design shows the components that make up a system. The proposed system consists of the
following basic components; MySQL database, MongoDB database. These components are
further discussed in detail and the architectural design of the proposed system given in figure 3
shows the connections amongst these components.
• SQL Component: contains the storage engine which handles data storage in MySQL
database. The storage engine is made up of a transaction log file and data file groups
which could be hierarchically broken down into data files table, indexes, extent and page
which is the smallest unit of storage in relational databases. The transaction log file
component of the storage engine is used to achieve and maintain data integrity and
recovery in the database. It records the start and end of each operation and also every
modification performed on data in the database.
• MongoDB Component: MongoDB uses replication to ensure redundancy and
consistency. Influx of data from different destinations and in different format are broken
down and equally dispersed to a collection of non-static extensible terminals called shard.
Data describing other data within the cluster are saved in configuration servers. Every of
these servers contain replica of all metadata for the purpose of redundancy. When client
request is made, it forms one of the routing processes which are used to check the
configuration servers to know the position of the request.
6.2 ALGORITHM
Internally, our hybrid system uses both the B-tree algorithm and the proportional fill algorithm.
The relational database (SQL) runs majorly on a proportional filled algorithm. The proportional
fill algorithm is used by the SQL storage engine to write data to the database files depending on
the size of free space in each data file rather than writing in each file until it is full then moving to
the second one sequentially. As such, the SQL server storage engine will write more frequently to
the files with more free space. MongoDB on the other hand is built on a B-tree algorithm.
However, a step-by-step procedure for data processing in our hybrid system is given as;
Step 1: Load data
Step 2: Define class of data
Step 3: Initialize DB hybrid Interface
Step 4: Test Data: if
Data is structured then
Store in SQL database
Data is unstructured then
Store in MongoDB database
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Step 5: Update database hybrid interface
Step 6: View, Delete, Update, Exit
7. EXPERIMENTAL SETUP AND RESULTS
The proposed system is implemented in C# in visual studio Integrated Development
Environment. MongoDB and MySQL were used for data storage. Some of the classes used in the
program are; Person, Symbol, PatientInfo, FileInfo, Panel, Person.
For our application to function properly, the MongoDB server would first be started from
command prompt by navigating to the installation directory and executing the command
mongod.exe as shown in figure 4. By default, MongoDB server will start at port 27017. To start
the client shell, the command mongo.exe is executed on a separate command prompt window.
Figure 5 shows the client shell connecting to Test which is the default system database.
To execute our hybrid application, the input data which is made up of structured and unstructured
data is saved in the system’s local disk. We invoke our application by double clicking on the
application icon. An overview of our application shown in figure.6 shows the modes that our
proposed system can function in.
When big data is loaded in our hybrid system, the database used for storage is determined by the
mode in which the application runs in. Unstructured data is channelled to the MongoDB database
(except when the application is in MongoDB mode in which case MongoDB stores both
structured and unstructured data) while MySQL database is used to store and manage structured
data.
Figure 3. Architectural design of the proposed system
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Figure 4. Starting MongoDB
Figure 5. MongoDB connecting to default system database
Figure 6. Overview of DB_Hybrid Interface
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Figure 7. View data operation in SQL mode.
Fig. 8: View data operation in hybrid mode.
8. DISCUSSION OF RESULT
We loaded big data in our hybrid database system in SQL mode, MongoDB mode and also hybrid
mode. The databases used for data storage and management vary depending on the mode that the
system runs in. The output of the implemented hybrid database system for big data storage and
management is discussed here;
In SQL mode, data is stored and managed in MySQL database. The system discards data in
unstructured form since it cannot be stored in SQL database. A screen shot of the load data
operation performed by our hybrid system in SQL mode is given in figure 7, a view of the
database content shown in figure 8 shows that unstructured data is discarded by our hybrid
application in SQL mode. In MongoDB mode, storage and management of both structured and
unstructured data is performed using MongoDB database. This stores both structured and
unstructured data in MongoDB. Also in hybrid mode, data in structured form is stored and
managed using SQL database while MongoDB is used to store and manage unstructured data, this
is shown in figure.9
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From the output in figure 7, figure 8, and figure 9, it can be seen that our hybrid system integrates
the functions or flavours of MySQL which is a popular relational database and MongoDB which
is a non-relational database in one database system allowing the databases in the system to
function in isolation and also in integration.
The proposed system supports managerial operations such as Update and Delete which can be
used to manage data in the system. Figure 10 shows update operation in our hybrid database
system.
Figure 9. Update operation
Figure 10. Load data operation in SQL mode.
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9. CONCLUSION AND RECOMMENDATION
In conclusion, we proposed a method which combines SQL database which belongs to the
relational group of database systems and MongoDB being a NoSQL database to store and manage
big data. With the result obtained it is understandable that our system can be used for storage and
management of big eliminating the weaknesses in both databases.
10. CONTRIBUTION TO KNOWLEDGE
This work presents the following contributions to knowledge;
1. Development of a hybrid database system for big data storage and management.
2. This approach improves on the use of MongoDB database for big data storage.
3. The study establishes the possibility of having the flexibility and scalability of NoSQL
database and also the stability and transactional ingredients of a relational database in one
database management system.
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AUTHORS
Blessing E.James is a Graduate Assistant in the Department of Computer Science ,Akwa
Ibom State University, She obtained a Bachelor of Technology degree in Mathematics and
Computer Science, 2010 from the Federal University of Technology Owerri, Imo State,
Nigeria and a Master of Science degree (2007) from the University of Port Harcourt,
Nigeria. Her interest is on Big data, Database Management Systems, Database Knowledge
Management, Data Mining and Machine Learning.
Prince Oghenekaro ASAGBA had his B.Sc degree in Computer Science at the University of
Nigeria, Nsukka, in 1991 with a Second Class (Hons) Upper Division.He had his M.Sc
degree in Computer Science at the University of Benin in April, 1998, and a Ph.D degree in
Computer Science at the University of Port Harcourt in March, 2009. Asagba is an avid
researcher with over fifty researched published articles in reputable journals, both locally
and internationally. Asagba possesses over 20 years of reasonable wealth of research /
teaching experience at the University level. He is a Visiting Professor / Scholar to some
Universities in Nigeria. His research interest include: Computing and Information Security, Network
Analysis, Software Engineering, Database Management Systems, Modelling and Programming. He is a
member of Nigeria Computer Society (NCS) and Computer Professional Registration Council of Nigeria
(CPN).