Big Data Security on Hadoop Open Source Frame for Healthcare Data Management using One-Time-Pad Encryption Algorithm (Published)
The study elicited knowledge about the factors associated with one-time pad encryption/decryption with big data in healthcare; formulate an assembled algorithms model for one-time pad encryption; design and implement the system and evaluating the system performance with the view implementing big data security on Hadoop open-source framework for healthcare data. Literature was sourced to investigate the factors associated with healthcare security attacks and various consequences of breach of data. An assembled algorithm model was formulated using mathematical theory of one-time pad encryption and a model was designed using Universal Modelling Language (UML) and implemented using python programming language, Distributed File System of Hadoop, Yet Another Resource Negotiator called YARN; encryption time and decryption time was adopted for the performance metrics deployed for the evaluation of the developed system. The result showed that as the size of the files increased, the encryption/decryption time keeps increasing as well. While carryout the algorithm evaluation, two different values (file sizes) were used for testing on the Hadoop framework.Securing the healthcare (Ebola) big-data, it was observed that OTP encryption/decryption performed better compared to AES encryption/decryption in term of computational processing time of the healthcare big-data considered. Considering before/after downloading, it was observed that there was need for authentication for another level of security towards securing healthcare records on HDFS. The study concluded that, big data analytics on Hadoop is ideal for today’s big healthcare data and also that One Time Pad encryption algorithm is sufficient to provide needed big healthcare data security.
Keywords: Algorithm, Big Data, Encryption, Hadoop, data security, data vulnerabilities
The Role of Big Data in Improving Artificial Intelligence Algorithms (Published)
In the dynamic landscape of artificial intelligence (AI), the integration of big data has emerged as a pivotal force in driving advancements and enhancing the capabilities of AI algorithms. This research paper delves into the fundamental role that big data plays in improving AI algorithms. By leveraging vast datasets, AI algorithms can be refined and optimized, resulting in more accurate predictions, deeper insights, and heightened overall performance. The paper begins by exploring the symbiotic relationship between big data and AI, emphasizing how the availability of massive datasets has become a catalyst for innovation in machine learning and deep learning. Through comprehensive data analysis, AI models can learn and adapt to complex patterns, leading to more intelligent decision-making. This research paper explores “The Role of Big Data in Improving Artificial Intelligence Algorithms” by delving into the pivotal influence that large datasets have on the development and enhancement of AI algorithms.
Keywords: Artificial Intelligence Algorithms, Autonomous Systems, Big Data, Data-Driven Technologies
Query Processing in the Cloud for Big Data Applications Benefits and Risks (Published)
The advent of cloud computing has transformed the landscape of big data processing, offering numerous benefits and presenting certain risks. This paper explores the domain of query processing in the cloud for big data applications, elucidating the advantages and challenges associated with this paradigm shift. Benefits: Scalability: Cloud platforms provide elastic resources, allowing big data applications to scale up or down based on demand. This scalability enables organizations to process vast amounts of data without significant upfront investments in hardware. Accessibility: Cloud-based query processing offers accessibility from anywhere, promoting remote work and collaboration, and facilitating data sharing and analysis among global teams. Risks: Data Security and Privacy: Storing and processing sensitive data in the cloud can pose security and privacy risks if not properly managed. Data breaches and unauthorized access are potential concerns. Data Transfer Costs: Transferring large volumes of data to and from the cloud can result in significant costs, particularly when dealing with extensive datasets. Vendor Lock-In: Adopting cloud services can lead to vendor lock-in, making it challenging to migrate to another provider or back to on-premises infrastructure. This paper delves into these benefits and risks in detail, providing insights into strategies for mitigating the associated challenges and making informed decisions when considering query processing in the cloud for big data applications. The balance between reaping the benefits of cloud scalability and managing the associated risks is crucial in the ever-evolving landscape of big data processing.
Keywords: Accessibility, Big Data, Cloud Computing, Cost Efficiency, Managed Services, Query processing, scalability
Bridging Bytes and Business: A Research Inquiry into Big Data’s Strategic Significance (Published)
In the era of information-driven economies, “Bridging Bytes and Business: A Research Inquiry into Big Data’s Strategic Significance” serves as a scholarly exploration into the transformative nexus of data analytics and strategic business endeavors. This research delves into the strategic role played by big data in contemporary business landscapes, examining its multifaceted influence on operational efficiencies, decision-making processes, and the overall competitive advantage of organizations. Employing a meticulous research methodology that encompasses literature reviews, case studies, and real-world applications, this paper seeks to bridge the gap between the intricacies of data analytics and the strategic imperatives of modern businesses. By synthesizing insights from diverse industries, ranging from technology and finance to healthcare and beyond, our analysis aims to provide a comprehensive understanding of how big data acts as a bridge, connecting the analytical prowess of bytes with the strategic imperatives of business success.
Keywords: Big Data, Data Driven Decision Making, Data Insights, Data Revolution, Data Streams, Digital Innovation, Industry Disruption, Industry Transformation, Technological Advancements, data analytics
Optimizing Query Processing for Big Data: A Comprehensive Review (Published)
The proliferation of big data has reshaped the landscape of data management, necessitating innovative approaches to query processing. This research paper presents a comprehensive review of query processing techniques tailored to the unique challenges posed by big data environments. The paper incorporates real-world case studies that showcase successful implementations of advanced query processing techniques within organizations across various industries. These case studies underscore the practical impact of optimized query processing on data-driven decision-making and analytics.
Keywords: Big Data, Database management, Machine learning algorithm, Query processing, offload queries
An Improved Land Mapping and Geographical Information Management System Using Geodatabase (Published)
With data constantly increasing at a tremendous speed, it is crucial to have better knowledge of how information is manipulated and stored for subsequent retrieval and use. The data storage geodatabase strategy is introduced as dependable alternative based on acknowledged relational database concepts, which form foundation of selected database handling system. Simple but well-defined tables composed of distinctive features selected to store and handle spatial data and rule-base for every topographical dataset. In this paper, we developed an improved land mapping and Geographical Information System (GIS) using geodatabase. The study provides an enhanced approach to storing and managing data using a geodatabase, contributing to further research into alternative way of handling big data. Development of a web application that interacts with geodatabase for big data storage without the need of running multiple servers or enterprise class software. Thus this research is useful for those who have need for efficient data storage and management in today’s world of data size and complexities. By storing data within a geodatabase, one can draw from the benefits that come with its data management capabilities to leverage spatial information.
Keywords: Big Data, Geodatabase, NoSQL, ORDBMS, PostgreSQL, RDBMS, SQL
Opinion Mining In Big Data: Trend of Thinking for Big Data Era (Published)
This ear with the rapidly growing of internet and network using there are a huge data that have been introduced, Big Data are now on the double expanding rabidly in all domains, including opinion and sentiment analysis, for there are many social media and other websites that offer chances to provide the visitors and customers to post their opinion which usually contains valuable information that could be helpfully for several issues. And there are different methods and techniques that proposed to face this huge data and the big social data to make it more beneficial for several fields. This Paper introduces the big data and the most common it is usage and challenge, and it also investigate the sentiment analysis and it is common techniques and thinking about it is futures. This paper also thinking about the future of big data and opinion mining is clearly discussed and thinking about the future of big data and opinion mining. And the paper will discuss the challenges that facing the big data and opinion mining.
Keywords: Big Data, Data mining, Social media, opinion mining