scholarly journals Intelligent Secure Storage Mechanism for Big Data

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 246-261
Author(s):  
K.R. Remesh Babu ◽  
K.P. Madhu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Big Data Analytics ◽  
Business Organizations ◽  
Storage Mechanism ◽  
Data Intensive ◽  
Secure Storage ◽  
Huge Data ◽  
Efficient Data ◽  
Data Intensive Applications

The management of big data became more important due to the wide spread adoption of internet of things in various fields. The developments in technology, science, human habits, etc., generates massive amount of data, so it is increasingly important to store and protect these data from attacks. Big data analytics is now a hot topic. The data storage facility provided by the cloud computing enabled business organizations to overcome the burden of huge data storage and maintenance. Also, several distributed cloud applications supports them to analyze this data for taking appropriate decisions. The dynamic growth of data and data intensive applications demands an efficient intelligent storage mechanism for big data. The proposed system analyzes IP packets for vulnerabilities and classifies data nodes as reliable and unreliable nodes for the efficient data storage. The proposed Apriori algorithm based method automatically classifies the nodes for intelligent secure storage mechanism for the distributed big data storage.

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

Decoding Big Data Analytics for Emerging Business Through Data-Intensive Applications and Business Intelligence

2020 ◽  
pp. 66-80
Author(s):  
Vinay Kellengere Shankarnarayan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Business Models ◽  
Big Data Analytics ◽  
Research Area ◽  
Future Perspective ◽  
Massive Data ◽  
Data Intensive ◽  
Tools And Techniques ◽  
Data Intensive Applications

In recent years, big data have gained massive popularity among researchers, decision analysts, and data architects in any enterprise. Big data had been just another way of saying analytics. In today's world, the company's capital lies with big data. Think of worlds huge companies. The value they offer comes from their data, which they analyze for their proactive benefits. This chapter showcases the insight of big data and its tools and techniques the companies have adopted to deal with data problems. The authors also focus on framework and methodologies to handle the massive data in order to make more accurate and precise decisions. The chapter begins with the current organizational scenario and what is meant by big data. Next, it draws out various challenges faced by organizations. The authors also observe big data business models and different frameworks available and how it has been categorized and finally the conclusion discusses the challenges and what is the future perspective of this research area.

Bioinformatics-Driven Big Data Analytics in Microbial Research

2015 ◽  
pp. 265-283 ◽  
Author(s):  
Ratna Prabha ◽  
Anil Rai ◽  
D. P. Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Genome Sequencing ◽  
Big Data Analytics ◽  
Biological Data ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Huge Data ◽  
Computational Resources

With the advent of sophisticated and high-end molecular biological technologies, microbial research has observed tremendous boom. It has now become one of the most prominent sources for the generation of “big data.” This is made possible due to huge data coming from the experimental platforms like whole genome sequencing projects, microarray technologies, mapping of Single Nucleotide Polymorphisms (SNP), proteomics, metabolomics, and phenomics programs. For analysis, interpretation, comparison, storage, archival, and utilization of this wealth of information, bioinformatics has emerged as a massive platform to solve the problems of data management in microbial research. In present chapter, the authors present an account of “big data” resources spread across the microbial domain of research, the efforts that are being made to generate “big data,” computational resources facilitating analysis and interpretation, and future needs for huge biological data storage, interpretation, and management.

NoSQL Databases

2015 ◽  
pp. 109-152 ◽  
Author(s):  
Mainak Adhikari ◽  
Sukhendu Kar
Keyword(s):  
Cloud Computing ◽  
Social Networking ◽  
Data Storage ◽  
Social Networking Sites ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Nosql Database ◽  
Real Time Applications ◽  
Data Intensive Applications

NoSQL database provides a mechanism for storage and access of data across multiple storage clusters. NoSQL dabases are finding significant and growing industry to meet the huge data storage requirements of Big data, real time applications, and Cloud Computing. NoSQL databases have lots of advantages over the conventional RDBMS features. NoSQL systems are also referred to as “Not only SQL” to emphasize that they may in fact allow Structured language like SQL, and additionally, they allow Semi Structured as well as Unstructured language. A variety of NoSQL databases having different features to deal with exponentially growing data intensive applications are available with open source and proprietary option mostly prompted and used by social networking sites. This chapter discusses some features and challenges of NoSQL databases and some of the popular NoSQL databases with their features on the light of CAP theorem.

ScaDS Dresden/Leipzig – A competence center for collaborative big data research

2018 ◽  
Vol 60 (5-6) ◽  
pp. 327-333 ◽  
Author(s):  
René Jäkel ◽  
Eric Peukert ◽  
Wolfgang E. Nagel ◽  
Erhard Rahm
Keyword(s):  
Big Data ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Data Intensive ◽  
Innovative Methods ◽  
Huge Data ◽  
Wide Range ◽  
Resource Requirements ◽  
Visualization Of Data ◽  
Data Intensive Applications

Abstract The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibilities of state-of-the-art methods for the acquisition, integration, analysis and visualization of data and are summarized under the term big data. ScaDS Dresden/Leipzig, as one Germany-wide competence center for collaborative big data research, bundles efforts to realize data-intensive applications for a wide range of applications in science and industry. In this article, we present the basic concept of the competence center and give insights in some of its research topics.

scholarly journals Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

2021 ◽  
Vol 22 (4) ◽  
pp. 401-412
Author(s):  
Hrachya Astsatryan ◽  
Arthur Lalayan ◽  
Aram Kocharyan ◽  
Daniel Hagimont
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Data Storage ◽  
File Systems ◽  
Large Datasets ◽  
Data Sets ◽  
Mapreduce Framework ◽  
Data Intensive ◽  
Parallel Data ◽  
Data Intensive Applications

The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.

scholarly journals Content an Insight to Security Paradigm for BigData on Cloud: Current Trend and Research

2017 ◽  
Vol 7 (5) ◽  
pp. 2873 ◽  
Author(s):  
Chhaya S Dule ◽  
Girijamma H. A.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Current Trend ◽  
Security And Privacy ◽  
Future Research ◽  
Survey Analysis ◽  
Cloud Infrastructure ◽  
Privacy And Security ◽  
Storage Mechanism ◽  
Efficient Data

The sucesssive growth of collabrative applications prodcuing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented

scholarly journals Task Selection for Scheduling using Hadoop Scheduler

2019 ◽  
Vol 9 (2S) ◽  
pp. 708-710
Keyword(s):  
Big Data ◽  
Parallel Programming ◽  
Data Storage ◽  
Selection Method ◽  
Experimental Result ◽  
Task Selection ◽  
Data Intensive ◽  
Selection For ◽  
Data Intensive Applications ◽  
Big Data Storage

MapReduce is a prevalent model for data intensive applications. This covers the difficulties of parallel programming and provides an abstract environment. Hadoop is a benchmark for Big Data storage by being able to provide load balancing, scalable and fault tolerance operation. Hadoop output is mainly dependent on scheduler. Various algorithms for scheduling [6-10]have been suggested for various types of environments, applications and workload. In this work new task selection method is developed to facilitate the scheduler, if a node has several local tasks. Experimental result shows an improvement of 20% in respect of locality and fairness.

scholarly journals The Construction and Thinking of Cloud Computing and Big Data Technology in Smart Campus

2019 ◽  
Vol 3 (2) ◽  
pp. 152
Author(s):  
Xianglan Wu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Information Technologies ◽  
Rapid Development ◽  
Smart Campus ◽  
Huge Data ◽  
New Information ◽  
Big Data Technology

<p>In today's society, the rise of the Internet and rapid development make every day produce a huge amount of data. Therefore, the traditional data processing mode and data storage can not be fully analyzed and mined these data. More and more new information technologies (such as cloud computing, virtualization and big data, etc.) have emerged and been applied, the network has turned from informationization to intelligence, and campus construction has ushered in the stage of smart campus construction.The construction of intelligent campus refers to big data and cloud computing technology, which improves the informatization service quality of colleges and universities by integrating, storing and mining huge data.</p>

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