Big Data-Based User Data Intelligent Encryption Method in Electronic Case System

2019 ◽  
pp. 300-309
Author(s):  
Xin Liu
Keyword(s):  
Big Data ◽  
Case System ◽  
User Data ◽  
Encryption Method

scholarly journals Big data for climate action or climate action for big data?

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172098203
Author(s):  
Maria I Espinoza ◽  
Melissa Aronczyk
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Global Climate ◽  
Intergovernmental Organizations ◽  
Address Climate Change ◽  
Data Practices ◽  
Climate Action ◽  
User Data ◽  
Climate Crisis ◽  
Depth Interviews

Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability.

scholarly journals Making Inferences Using Incidentally Collected Data

2016 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Internet ◽  
User Interactions ◽  
Social Media Data ◽  
Broad Definition ◽  
User Data ◽  
The Cost ◽  
Media Data

This chapter discusses the use of large quantities of incidentallycollected data (ICD) to make inferences about politics. This type of datais sometimes referred to as “big data” but I avoid this term because of itsconflicting definitions (Monroe, 2012; Ward & Barker, 2013). ICD is datathat was created or collected primarily for a purpose other than analysis.Within this broad definition, this chapter focuses particularly on datagenerated through user interactions with websites. While ICD has beenaround for at least half a century, the Internet greatly expanded theavailability and reduced the cost of ICD. Examples of ICD include data onInternet searches, social media data, and user data from civic platforms.This chapter briefly explains some sources and uses of ICD and thendiscusses some of the potential issues of analysis and interpretation thatarise when using ICD, including the different approaches to inference thatresearchers can use.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

scholarly journals Paying Attention: Big Data and Social Advertising as Barriers to Ecological Change

2020 ◽  
Vol 12 (24) ◽  
pp. 10589
Author(s):  
Kaitlin Kish
Keyword(s):  
Big Data ◽  
Research Policy ◽  
Ecological Change ◽  
Institutional Research ◽  
Commodity Fetishism ◽  
Sustainability Research ◽  
User Data ◽  
Social Advertising ◽  
Information Ecologies ◽  
Scientific Facts

Big data and online media conglomerates have significant power over the behavior of individuals. Online platforms have become the largest canvas for advertising, and the most profitable commodity is users’ attention. Large tech companies, such as Facebook and Alphabet, use historically effective psychological advertisement tactics in tandem with enormous amounts of user data to effectively and efficiently meet the needs of their customers, who are not the end-users, but the corporations competing for advertising space on users’ screens. This commodification of attention is a serious threat to socio-ecological sustainability. In this paper, I argue that big data and social advertising platforms, such as Facebook, use commodified attention to take advantage of psycho-social neuroticisms and commodity fetishism in modern individuals to perpetuate conspicuous consumption. They also contribute to highly fragmented information ecologies that intentionally obscure scientific facts regarding ecological emergencies. The commitment to stakeholders and growth economics makes social advertising conglomerates a significant barrier to a socio-ecological future. I provide a series of solutions to this problem at the institutional, research, policy, and individual levels and areas for future sustainability research.

scholarly journals Ensure Security for Mapreduce-Hadoop Distributed File System using Encryption Method Over Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 733-740
Keyword(s):  
Big Data ◽  
File System ◽  
Data Encryption ◽  
Previous Method ◽  
Distributed File System ◽  
Sensitive Data ◽  
Encryption And Decryption ◽  
Hadoop Distributed File System ◽  
Encryption Method ◽  
Mapreduce Paradigm

Big data security is the most focused research issue nowadays due to their increased size and the complexity involved in handling of large volume of data. It is more difficult to ensure security on big data handling due to its characteristics 4V’s. With the aim of ensuring security and flexible encryption computation on big data with reduced computation overhead in this work, framework with encryption (MRS) is presented with Hadoop Distributed file System (HDFS). Development of the MapReduce paradigm needs networked attached storage in addition to parallel processing. For storing as well as handling big data, HDFS are extensively utilized. This proposed method creates a framework for obtaining data from client and after that examining the received data, excerpt privacy policy and after that find the sensitive data. The security is guaranteed in this framework using key rotation algorithm which is an efficient encryption and decryption technique for safeguarding the data over big data. Data encryption is a means to protect data in storage with containing a key encryption saved and accessible to reuse the data while required. The outcome shows that the research method guarantees greater security for enormous amount of data and gives beneficial info to related clients. Therefore the outcome concluded that the proposed method is superior to the previous method. Finally, this research can be applied effectively on the various domains such as health care domains, educational domains, social networking domains, etc which require more security and increased volume of data.

scholarly journals A Big Data Framework for Quality Assurance and Validation

2019 ◽  
Vol 8 (2) ◽  
pp. 2490-2494
Keyword(s):  
Health Care ◽  
Big Data ◽  
Relational Databases ◽  
Programming Model ◽  
New Technology ◽  
Large Data ◽  
Quality Data ◽  
Post Processing ◽  
Data Framework ◽  
User Data

Big data is a new technology, which is defined by large amount of data, so it is possible to extract value from the capturing and analysis process. Large data faced many challenges due to various features such as volume, speed, variation, value, complexity and performance. Many organizations face challenges while facing test strategies for structured and unstructured data validation, establishing a proper testing environment, working with non relational databases and maintaining functional testing. These challenges have low quality data in production, delay in execution and increase in cost. Reduce the map for data intensive business and scientific applications Provides parallel and scalable programming model. To get the performance of big data applications, defined as response time, maximum online user data capacity size, and a certain maximum processing capacity. In proposed, to test the health care big data . In health care data contains text file, image file, audio file and video file. To test the big data document, by using two concepts such as big data preprocessing testing and post processing testing. To classify the data from unstructured format to structured format using SVM algorithm. In preprocessing testing test all the data, for the purpose data accuracy. In preprocessing testing such as file size testing, file extension testing and de-duplication testing. In Post Processing to implement the map reduce concept for the use of easily to fetch the data.

The Use of Social Media for Urban Planning

2014 ◽  
pp. 113-134 ◽  
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

Sign in / Sign up

Export Citation Format

Share Document