Security Issues and Challenges Related to Big Data

2021 ◽  
pp. 1604-1620
Author(s):  
Jaimin N. Undavia ◽  
Atul Patel ◽  
Sheenal Patel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Database Systems ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Huge Amount ◽  
Current Time ◽  
Security Issues ◽  
Future Prediction ◽  
Data Term

Availability of huge amount of data has opened up a new area and challenge to analyze these data. Analysis of these data become essential for each organization and these analyses may yield some useful information for their future prospectus. To store, manage and analyze such huge amount of data traditional database systems are not adequate and not capable also, so new data term is introduced – “Big Data”. This term refers to huge amount of data which are used for analytical purpose and future prediction or forecasting. Big Data may consist of combination of structured, semi structured or unstructured data and managing such data is a big challenge in current time. Such heterogeneous data is required to maintained in very secured and specific way. In this chapter, we have tried to identify such challenges and issues and also tried to resolve it with specific tools.

Security Issues and Challenges Related to Big Data

2018 ◽  
pp. 86-101
Author(s):  
Jaimin N. Undavia ◽  
Atul Patel ◽  
Sheenal Patel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Database Systems ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Huge Amount ◽  
Current Time ◽  
Security Issues ◽  
Future Prediction ◽  
Data Term

Availability of huge amount of data has opened up a new area and challenge to analyze these data. Analysis of these data become essential for each organization and these analyses may yield some useful information for their future prospectus. To store, manage and analyze such huge amount of data traditional database systems are not adequate and not capable also, so new data term is introduced – “Big Data”. This term refers to huge amount of data which are used for analytical purpose and future prediction or forecasting. Big Data may consist of combination of structured, semi structured or unstructured data and managing such data is a big challenge in current time. Such heterogeneous data is required to maintained in very secured and specific way. In this chapter, we have tried to identify such challenges and issues and also tried to resolve it with specific tools.

scholarly journals Text And Sentimental Analysis On Big Data

2021 ◽  
pp. 324-334
Author(s):  
Saifuzzafar Jaweed Ahmed
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Virtual Machines ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Large Field ◽  
Huge Amount ◽  
Data Set ◽  
Big Data Processing

Big Data has become a very important part of all industries and organizations sectors nowadays. All sectors like energy, banking, retail, hardware, networking, etc all generate a huge amount of unstructured data which is processed and analyzed accurately in a structured form. Then the structured data can reveal very useful information for their business growth. Big Data helps in getting useful data from unstructured or heterogeneous data by analyzing them. Big data initially defined by the volume of a data set. Big data sets are generally huge, measuring tens of terabytes and sometimes crossing the sting of petabytes. Today, big data falls under three categories structured, unstructured, and semi-structured. The size of big data is improving in a fast phase from Terabytes to Exabytes Of data. Also, Big data requires techniques that help to integrate a huge amount of heterogeneous data and to process them. Data Analysis which is a big data process has its applications in various areas such as business processing, disease prevention, cybersecurity, and so on. Big data has three major issues such as data storage, data management, and information retrieval. Big data processing requires a particular setup of hardware and virtual machines to derive results. The processing is completed simultaneously to realize results as quickly as possible. These days big data processing techniques include Text mining and sentimental analysis. Text analytics is a very large field under which there are several techniques, models, methods for automatic and quantitative analysis of textual data. The purpose of this paper is to show how the text analysis and sentimental analysis process the unstructured data and how these techniques extract meaningful information and, thus make information available to the various data mining statistical and machine learning) algorithms.

scholarly journals Applications of Big Data in the Digital India: Opportunities and Challenges

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
Vinay Kumar ◽  
Arpana Chaturvedi
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Exponential Growth ◽  
Social Networking Sites ◽  
Unstructured Data ◽  
Threat Perception ◽  
Data Repository ◽  
Huge Amount ◽  
Real Challenge ◽  
Area Of Application

<div><p><em>With the advent of Social Networking Sites (SNS), volumes of data are generated daily. Most of these data are multimedia type and unstructured with exponential growth. This exponential growth of variety, volume and complexity of structured and unstructured data leads to the concept of big data. Managing big data and harnessing its benefits is a real challenge. With increase in access to big data repository for various applications, security and access control is another aspect that needs to be considered while managing big data. We have discussed area of application of big data, opportunities it provides and challenges that we face in the managing such huge amount of data for various applications. Issues related to security against different threat perception of big data are also discussed. </em></p></div>

The Impact of Big Data on Security

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

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

Survelliance of Type I and II Diabetic Subjects on Physical Characteristics

2021 ◽  
pp. 308-344
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Parul Singhal
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Electronic Health Records ◽  
Analysis Data ◽  
Healthcare Systems ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Diagnostic Information ◽  
Unstructured Data ◽  
Type I

The Delhi and NCR healthcare systems are rapidly registering electronic health records and diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing, and large quantities of information are examined and new insights are part of the analysis of this technology experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. The authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increasing with age in case of males and not following the pattern of diabetes variation with age, while in the case of females, TTH pattern variation is the same as diabetes (i.e., increasing trend up to age of 60 then decreasing).

scholarly journals IoT-Fog-Cloud system architecture for big data analysis and cybersecurity systems: a review of fog computing, the introduction to the Internet of Things audit

2020 ◽  
Author(s):  
D. R. Kolisnyk ◽  
◽  
K. S. Misevych ◽  
S. V. Kovalenko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
System Architecture ◽  
Fog Computing ◽  
Big Data Analysis ◽  
Future Research ◽  
The Internet ◽  
Security Issues ◽  
The Internet Of Things

The article considers the issues of system architecture IoT-Fog-Cloud, considers the interaction between the three levels of IoT, Fog and Cloud for the effective implementation of programs for big data analysis and cybersecurity. The article also discusses security issues, solutions and directions for future research in the field of the Internet of Things and nebulous computing.

scholarly journals Big Data and MapReduce Challenges, Opportunities and Trends

2016 ◽  
Vol 6 (6) ◽  
pp. 2911
Author(s):  
Sachin Arun Thanekar ◽  
K. Subrahmanyam ◽  
A. B. Bagwan
Keyword(s):  
Big Data ◽  
High Velocity ◽  
Structured Data ◽  
Unstructured Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Commodity Hardware ◽  
Survey Paper ◽  
Recent Trends ◽  
Large Clusters

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data.  The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.</p>

Sign in / Sign up

Export Citation Format

Share Document