Computational Journalism

2015 ◽  
pp. 17-31 ◽  
Author(s):  
Lady Dhyana Ziegler
Keyword(s):  
Big Data ◽  
Process Model ◽  
Social Research ◽  
News Coverage ◽  
Big Data Analytics ◽  
Academic Programs ◽  
Human Interaction ◽  
Communication Process ◽  
New Paradigm ◽  
News Industry

In this chapter, the use and application of big data in the news gathering process is discussed. The author explains how the combination of journalism, computer science, and social research introduces a new paradigm to the news industry and academic programs. The chapter explains how the impact of computational journalism on the news product and the use of big data analytics are applied to assess trends and habits of human interaction in all aspects of news coverage. The author purports that big data is essential to the news industry to make predictions and/or draw conclusions to produce a better news package. The author stresses the point that shaping news in a big data world challenges the foundation of journalistic principles and practices but the credibility and integrity of the news product must be maintained. The chapter introduces the Z Wheel communication process model as a new tool for shaping news in this big data environment.

Computational Journalism

2019 ◽  
pp. 410-423
Author(s):  
Lady Dhyana Ziegler
Keyword(s):  
Big Data ◽  
Process Model ◽  
Social Research ◽  
News Coverage ◽  
Big Data Analytics ◽  
Academic Programs ◽  
Human Interaction ◽  
Communication Process ◽  
New Paradigm ◽  
News Industry

In this chapter, the use and application of big data in the news gathering process is discussed. The author explains how the combination of journalism, computer science, and social research introduces a new paradigm to the news industry and academic programs. The chapter explains how the impact of computational journalism on the news product and the use of big data analytics are applied to assess trends and habits of human interaction in all aspects of news coverage. The author purports that big data is essential to the news industry to make predictions and/or draw conclusions to produce a better news package. The author stresses the point that shaping news in a big data world challenges the foundation of journalistic principles and practices but the credibility and integrity of the news product must be maintained. The chapter introduces the Z Wheel communication process model as a new tool for shaping news in this big data environment.

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

2017 ◽  
Vol 13 (02) ◽  
pp. 119-143 ◽  
Author(s):  
Claude E. Concolato ◽  
Li M. Chen
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Computer Science ◽  
Data Science ◽  
Big Data Analytics ◽  
Modern World ◽  
Growth Data ◽  
New Paradigm ◽  
Great Opportunity

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

scholarly journals An Overview of Milestones of Big Data Analytics in Clinical and Medical Analysis

2021 ◽  
Vol 10 (5) ◽  
pp. 416-421
Author(s):  
Manu M R ◽  
B Balamurugan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Human Interaction ◽  
Time Data ◽  
Effective Decision ◽  
Hadoop Cluster ◽  
And Behavior ◽  
Medical Analysis

The technological advancements make changes during availability of knowledge in a huge way. As the volume of data is increasing exponentially, there is a need for better management of data to research and industry. This data, referred to as Big Data, is now employed by various organizations to extract valuable information which may reanalyzed computationally to reveal patterns, trends and associations revealing the human interaction and behavior for making various industrial decisions But the data must be optimized, integrated, secured and visualized to make any effective decision. Analyzing of the large volume of data is not beneficial always unless it is analyzed properly. The existing techniques are insufficient to analyze the large Data and identify the frequent services accessed by the cloud users. Various services can be integrated to provide a better environment to work in emergency cases pretty earlier. Using these services, people become widely vulnerable to exposure. The data is large and provides an insight in to future predictions, which could definitely prevent maximum medical cases from happening. But without big data analytics techniques and therefore the Hadoop cluster, this data remains useless. Through this paper, we'll explain how real time data may be useful to research and predict severe

scholarly journals Automation of Big Data Analytics Using Robotic Process Automation

2021 ◽  
pp. 602-605
Author(s):  
G. Malini
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Human Interaction ◽  
Process Automation ◽  
Raw Data ◽  
Robotic Process Automation ◽  
Manual Intervention ◽  
Almost All

Robotic Process Automation (RPA) is now becomes a buzzword and makes it mark on almost all fields in assisting automation of repetitive human intensive tasks in a simpler manner. RPA is nothing but a software solution that mimics the human interaction with computing software and applications without manual intervention. RPA has already been adapted in almost every business processes which are repetitive. As we are in the age of information the need for retrieval of patterns from raw data is increasing unimaginably so the needs for effective tools are also in a greater need. The effectiveness of RPA can be incorporated into the ever growing data analytics to automate the process of finding patterns and predictions from big data.

scholarly journals Chinese Computing and Computing China as Global Knowledge Production [Special Section]

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Angela Xiao Wu
Keyword(s):  
Big Data ◽  
Knowledge Production ◽  
Business Models ◽  
Social Research ◽  
Special Section ◽  
Data Extraction ◽  
Big Data Analytics ◽  
Global Scale ◽  
Global Knowledge ◽  
Critical Commentary

As data-driven technologies and business models pervade on a global scale, China’s enormous digital economy often signals its dominating power by dint of data extraction. Complicating this view, this critical commentary focuses on knowledge production, an important dimension for examining the ways in which postsocialist China transpires in global political economy in the age of Big Data analytics. First, I show how Chinese commercial surveillance analytics profits from legitimation lent by the West-centric hierarchical academe. Then, I move to transnational academic repurposing of Big Data from China, which becomes increasingly common. Such social research tends to yield specters of China that are untethered to the lived realities of those whose data are taken. Drawing on decolonial thinking and feminist care ethics, this commentary concludes by urging social scientists to “stay with the trouble,” making China “legible” in their computing of Chinese Big Data.

Big data analytics for intelligent online education

2020 ◽  
pp. 1-11
Author(s):  
Rongbo Zhang ◽  
Weiyu Zhao ◽  
Yixin Wang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Online Education ◽  
Education Reform ◽  
Data Science ◽  
Evaluation Model ◽  
Big Data Analytics ◽  
New Paradigm ◽  
Positive Role ◽  
Education Practice

There are different paradigms in educational technology. Under the background of big data era, data science, learning analysis and education have made great achievements. In the field of education under big data, all kinds of new paradigms are constantly emerging and have achieved very good results in actual education. In the era of education big data, how to fully tap the value of big data for online education practice, decision-making, evaluation and research, and how to avoid the risk of big data are important issues in the current education reform and development. This paper analyzes the application of the current scientific paradigm in education, constructs the construction paradigm of online education evaluation model, and puts forward a new education concept in order to promote the development of the new paradigm of big data online education technology research. Applying this paradigm, a series of educational evaluation models are constructed from the macro, miso and micro levels, which play a positive role in the research, decision-making, practice and evaluation of related fields.

scholarly journals Law, Ethics and Tech Aspects for an Irrevocable BlockChain Based Curriculum Vitae Created by Big Data Analytics Fed by Internet of Things, Sensors and Approved Data Sources

2018 ◽  
Vol 3 (1) ◽  
pp. 50
Author(s):  
Vasilios Kanavas ◽  
Athanasios Zisopoulos ◽  
Konstantinos Spinthiropoulos
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Social Research ◽  
Big Data Analytics ◽  
Personal Data ◽  
General Idea ◽  
General Process ◽  
Business Skills ◽  
Curriculum Vitae ◽  
Voluntary Basis

Our general idea is to adopt Blockchain Ledger Technology for volunteered recording of business skills to formulate an irrevocable irresistible Curriculum Vitae to be processed by Recruiting agencies. The desperate well-educated jobless people of our times rely on recruiting agencies to analyze their qualification and find them a descent work. The general process starts with personal data feed automatically from sensors to an irrevocable BlockChain. At the final stage recruiting companies read these data, they process and offer a better job. A modified Time-Series is used to store work history and skills as the most important part of a Curriculum-Vitae. Every moment of our work life is recorded accordingly. These data are collected from various IoT sources like; Internet of things, Word, Excel computer file headers, URL fingerprints, Data Centers & Banks Big Data. It looks like self-slavery although according to GDPR the necessary consent must be freely given, specific, informed and unambiguous. In order to obtain freely given consent, it must be given on a voluntary basis. We attempted a primitive social research. In our University young people, they prefer to follow the blockchain recruiting route at a rate of 96% while in a local prefecture only 1% endorses the technology for unemployment.

4. Big data analytics

2017 ◽  
pp. 44-58
Author(s):  
Dawn E. Holmes
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Processing System ◽  
Distributed Data ◽  
New Paradigm ◽  
Distributed Data Processing ◽  
Customer Preferences ◽  
Classical Statistics ◽  
Core Functionality

‘Big data analytics’ argues that big data is only useful if we can extract useful information from it. It looks at some of the techniques used to discover useful information from big data, such as customer preferences or how fast an epidemic is spreading. Big data analytics is changing rapidly as the size of the datasets increases and classical statistics makes room for this new paradigm. An example of big data analytics is the algorithmic method called MapReduce, a distributed data processing system that forms part of the core functionality of the Hadoop Ecosystem. Amazon, Google, Facebook, and many others use Hadoop to store and process their data.

Big Data Analytics Demystified

Big Data ◽  
2016 ◽  
pp. 1859-1894
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Use Cases ◽  
Sensor Data ◽  
New Paradigm ◽  
Time Data ◽  
Real Time Data ◽  
Major Application

This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.

Big Data Analytics Demystified

2014 ◽  
pp. 38-73 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Use Cases ◽  
Sensor Data ◽  
New Paradigm ◽  
Time Data ◽  
Real Time Data ◽  
Major Application

This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.

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