scholarly journals Drought Prediction using Geo-Spatial Big Data

2019 ◽  
Vol 8 (2S5) ◽  
pp. 132-136
Keyword(s):  
Big Data ◽  
Query Language ◽  
Digital Processing ◽  
Video Data ◽  
Drought Analysis ◽  
Traditional System ◽  
Security Issues ◽  
Digital World ◽  
Drought Prediction ◽  
Huge Data

The digital world with digital processing, requires large storage space. The continuous explosion of the data such as text, image, audio, video, data centers and backup data lead to several problem in both storage and retrieval process. In this paper drought analysis and prediction is done using big data processing tools such as Hadoop and hive which can increase high. Previously to analyze and predict drought, traditional techniques such as AVISO model is used which is complex to process, requires more processing time, cannot process huge data and also has more security issues like malware in the database, abuse of privileges, etc. The system proposed in this paper can process huge data and has more processing speed. Here, drought analysis and prediction is carried out. To analyze drought dataset with more than ten lakhs are processed and drought type is found using map-reduce algorithm which maps and reduces the data using numerical summarization. Drought types such as D0, D1, D2,D3 D4 are analyzed to obtain reduced output. The obtained drought type are clustered using hive. To predict drought, random forest algorithm acts as an predictor which creates multiple decision trees and finds the best split among them. Finally, the predicted output is visualized using the time series model. The tools used in this paper include Hadoop and hive which can process huge data and it is the solution of Big Data. Hadoop is an open-source software framework for storing data and processing them efficiently, even if the data size is very huge. Hadoop uses Hadoop Distributed File System(HDFS) for storage and MapReduce for processing the data. Hive is a query processing tool which is built on top of Hadoop. It is a Structured query language(SQL)-like language called HiveQL (HQL). In this paper hive is used to cluster the data obtained from MapReduce. Thus using Big Data improves performance more than 50% compared to traditional system

scholarly journals Reliable Distributed Fuzzy Discretizer for Associative Classification of Big data

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Associative Classification ◽  
Data Set ◽  
Digital World ◽  
Huge Data ◽  
Fuzzy Partitioning ◽  
Discrete Values ◽  
Associative Classifiers

Data Mining is an essential task because the digital world creates huge data daily. Associative classification is one of the data mining task which is used to carry out classification of data, based on the demand of knowledge users. Most of the associative classification algorithms are not able to analyze the big data which are mostly continuous in nature. This leads to the interest of analyzing the existing discretization algorithms which converts continuous data into discrete values and the development of novel discretizer Reliable Distributed Fuzzy Discretizer for big data set. Many discretizers suffer the problem of over splitting the partitions. Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria. Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.

An Overview of Big Data Security with Hadoop Framework

2016 ◽  
pp. 165-181
Author(s):  
Jaya Singh ◽  
Ashish Maruti Gimekar ◽  
S. Venkatesan
Keyword(s):  
Big Data ◽  
Data Security ◽  
High Volume ◽  
Digital Data ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Traditional System ◽  
Security Issues ◽  
Security Challenges ◽  
Almost All

Big Data is a very huge volume of data which is beyond the storage capacity and processing capability of traditional system. The volume of data is increasing at exponential rate. Therefore, there is the need of such mechanism to store and process such high volume of data. The impressiveness of the Big data lies with its major applicability to almost all industries. Therefore, it represents both, the tremendous opportunities and complex challenges. Such omnipotent eminence leads to the privacy and security related challenges to the big data. Nowadays, security of big data is mainly focused by every organization because it contains a lot of sensitive data and useful information for taking decisions. The hostile nature of digital data itself has certain inherited security challenges. The aim of Big data security is to identify security issues and to find the better solution for handling security challenges. The observation and analysis of different security mechanism related to the issues of big data and their solutions are focused in this chapter.

scholarly journals A Review on Latest Technologies in Big Data Analysis

2018 ◽  
Vol 7 (3.1) ◽  
pp. 93
Author(s):  
Castro S ◽  
Pushpalakshmi R
Keyword(s):  
Big Data ◽  
Digital Technologies ◽  
Large Data ◽  
Big Data Analysis ◽  
Digital World ◽  
Huge Data ◽  
Big Data Applications ◽  
Research Problems ◽  
Modern Information ◽  
Multiple Stages

In this digital world, the modern information systems have produced a large amount of data which needs huge depositary in terms of terabytes for storage. Some of the digital technologies such as cloud computing and Internet of Things (IoT) are considered as the major sources of such large data. It is necessary to extract knowledge by analyzing these huge data which needs several attempts at multiple stages for decision making. Thus, the recent researches have focused on the analysis of big data. The main aim of this paper is to investigate the challenges of big data, applications, opportunities, implantation tools and its research problems. Thus, this study presents a platform to investigate big data at various levels. Moreover, it initiates a novel perspective for researchers to provide the solutions according to the challenges and research problems. 

An Overview of Big Data Security With Hadoop Framework

Web Services ◽  
2019 ◽  
pp. 13-24
Author(s):  
Jaya Singh ◽  
Ashish Maruti Gimekar ◽  
S. Venkatesan
Keyword(s):  
Big Data ◽  
Data Security ◽  
High Volume ◽  
Digital Data ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Traditional System ◽  
Security Issues ◽  
Security Challenges ◽  
Almost All

Big Data is a very huge volume of data which is beyond the storage capacity and processing capability of traditional system. The volume of data is increasing at exponential rate. Therefore, there is the need of such mechanism to store and process such high volume of data. The impressiveness of the Big data lies with its major applicability to almost all industries. Therefore, it represents both, the tremendous opportunities and complex challenges. Such omnipotent eminence leads to the privacy and security related challenges to the big data. Nowadays, security of big data is mainly focused by every organization because it contains a lot of sensitive data and useful information for taking decisions. The hostile nature of digital data itself has certain inherited security challenges. The aim of Big data security is to identify security issues and to find the better solution for handling security challenges. The observation and analysis of different security mechanism related to the issues of big data and their solutions are focused in this chapter.

The beginning of phygital world

2020 ◽  
Vol 10 (2) ◽  
pp. 1-4
Author(s):  
Evgeny Soloviov ◽  
Alexander Danilov
Keyword(s):  
Big Data ◽  
Learning Styles ◽  
21St Century ◽  
Digital Technology ◽  
Aviation Education ◽  
Digital World ◽  
The World ◽  
Unique Experience ◽  
Smart Data

The Phygital word itself is the combination pf physical and digital technology application.This paper will highlight the detail of phygital world and its importance, also we will discuss why its matter in the world of technology along with advantages and disadvantages.It is the concept and technology is the bridge between physical and digital world which bring unique experience to the users by providing purpose of phygital world. It is the technology used in 21st century to bring smart data as opposed to big data and mix into the broader address of array of learning styles. It can bring new experience to every sector almost like, retail, medical, aviation, education etc. to maintain some reality in today’s world which is developing technology day to day. It is a general reboot which can keep economy moving and guarantee the wellbeing of future in terms of both online and offline.

Research on Video Encryption and Privacy Protection in Cloud Monitoring System Based on H.264

2019 ◽  
Vol 12 ◽  
Author(s):  
Qingtao Wu ◽  
Zaihui Cao
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Video Data ◽  
Encryption Algorithm ◽  
Management Tool ◽  
Video Encryption ◽  
Wireless Multimedia ◽  
Monitoring Technology ◽  
Security Issues ◽  
Cloud Monitoring

: Cloud monitoring technology is an important maintenance and management tool for cloud platforms.Cloud monitoring system is a kind of network monitoring service, monitoring technology and monitoring platform based on Internet. At present, the monitoring system is changed from the local monitoring to cloud monitoring, with the flexibility and convenience improved, but also exposed more security issues. Cloud video may be intercepted or changed in the transmission process. Most of the existing encryption algorithms have defects in real-time and security. Aiming at the current security problems of cloud video surveillance, this paper proposes a new video encryption algorithm based on H.264 standard. By using the advanced FMO mechanism, the related macro blocks can be driven into different Slice. The encryption algorithm proposed in this paper can encrypt the whole video content by encrypting the FMO sub images. The method has high real-time performance, and the encryption process can be executed in parallel with the coding process. The algorithm can also be combined with traditional scrambling algorithm, further improve the video encryption effect. The algorithm selects the encrypted part of the video data, which reducing the amount of data to be encrypted. Thus reducing the computational complexity of the encryption system, with faster encryption speed, improve real-time and security, suitable for transfer through mobile multimedia and wireless multimedia network.

scholarly journals A centralized secure plan for detecting and mitigation incidents in hybrid SDN

2018 ◽  
Vol 189 ◽  
pp. 10015 ◽  
Author(s):  
Karim Zkik ◽  
Said EL Hajji ◽  
Ghizlane Orhanou
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
New Technologies ◽  
Software Defined Network ◽  
Centralized Control ◽  
Security Issues ◽  
Technology Sector ◽  
Network Equipment ◽  
Infrastructure Services ◽  
Hybrid Sdn

The information technology sector has experienced phenomenal growth during recent years. To follow this development many new technologies have emerged to satisfy the expectations of businesses and customers, such as Cloud Computing, mobility, virtualization, Internet of things and big data. Traditional network cannot longer support this growth and suffers more and more in terms of misconfiguration,management and configurations complexity. Software defined network (SDN) architectures can be considered as a big revolution in the field of computer networks, because they offer a centralized control on infrastructure, services and the applications deployed which facilitate configuration and management on the network. The implementation of this type of architecture is not obvious and requires great expertise and good handling and management of network equipment. To remedy this problem the SDN architectures have evolved towards distributed and hybrid architectures. Despites the advantages of using SDN, security issues remain a real obstacle in front of the deployment of this type of architecture. The centralized architecture of this type of networks makes it vulnerable to several types of attacks and intrusions, and the implementation of security equipment generally causes a decrease in performance and increase latency.

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