Big Data and the Paradox of Diversity

2016 ◽  
Vol 2 (2) ◽  
pp. 39-54 ◽  
Author(s):  
Bernhard Rieder
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Analytical Techniques ◽  
Guiding Principle ◽  
Societal Context ◽  
Traditional Societies ◽  
The Many

Abstract This paper develops a critique of Big Data and associated analytical techniques by focusing not on errors - skewed or imperfect datasets, false positives, underrepresentation, and so forth - but on data mining that works. After a quick framing of these practices as interested readings of reality, I address the question of how data analytics and, in particular, machine learning reveal and operate on the structured and unequal character of contemporary societies, installing “economic morality” (Allen 2012) as the central guiding principle. Rather than critiquing the methods behind Big Data, I inquire into the way these methods make the many differences in decentred, non-traditional societies knowable and, as a consequence, ready for profitable distinction and decision-making. The objective, in short, is to add to our understanding of the “profound ideological role at the intersection of sociality, research, and commerce” (van Dijck 2014: 201) the collection and analysis of large quantities of multifarious data have come to play. Such an understanding needs to embed Big Data in a larger, more fundamental critique of the societal context it operates in.

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Roberto Salazar-Reyna ◽  
Fernando Gonzalez-Aleu ◽  
Edgar M.A. Granda-Gutierrez ◽  
Jenny Diaz-Ramirez ◽  
Jose Arturo Garza-Reyes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Research Area ◽  
Engineering Systems ◽  
Content Type ◽  
Healthcare Engineering

PurposeThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.FindingsFrom the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.Research limitations/implicationsThe use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.Originality/valueTo the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

scholarly journals The dynamics of traffic congestion: a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning

2019 ◽  
Vol 15 (2) ◽  
pp. 1086
Author(s):  
M. Ali ◽  
T. K. Sheng ◽  
K. M. Yusof ◽  
M. R. Suhaili ◽  
N. E. Ghazali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Traffic Congestion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Active Role ◽  
Machine Learning Algorithms ◽  
Asia Pacific ◽  
The Government

Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue.

scholarly journals Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 195-204
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Causal Relationship ◽  
Data Analytics ◽  
Statistical Tests ◽  
Analytical Techniques ◽  
Data Sources ◽  
Healthcare Personnel ◽  
Related Data ◽  
Healthcare Data

High volumes and varieties of data is piling every day from healthcare and related fields. This big data sources if managed and analysed properly will provide vital knowledge. Data mining and data analytics have been playing an important role in extracting useful information from healthcare and related data sources. The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions. Analytical techniques from statistics, functionalities from data mining and machine learning already proved their capability with significant contributions to healthcare industry. The dominant functionality of data mining is classification which has been in use in mining healthcare data. Though classification is a good learning technique it may not provide a causation model which will be a reliable model for better decision making particularly in the medical field. The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. The proposed model is going to make use of some qualities of decision trees along with statistical tests and analytics. It is proposed to build the learning models on healthcare big data sources.

Big Data Analysis for Trend Recognition Using Machine Learning Techniques

2020 ◽  
Vol 10 (4) ◽  
pp. 540-550
Author(s):  
Cerene Mariam Abraham ◽  
Mannathazhathu Sudheep Elayidom ◽  
Thankappan Santhanakrishnan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Work ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

Medidor de agua para sistema inteligente de control de agua potable

2019 ◽  
pp. 6-12
Author(s):  
Daniel Sustaita-Cruces ◽  
Elsa Verónica Martínez-Mejía
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Water Consumption ◽  
Public And Private ◽  
The North ◽  
Research Activities ◽  
The Many ◽  
The University ◽  
Technological University

Objectives: Develop an electronic prototype that respects the environment, through the use of microcontroller cards such as Arduino, Nodemcu, IoT concept and cloud for the control and measurement of water consumption. Objectives specific: Process the data provided by sensors and meters distributed in the main water supplies of the university, as well as the public and private sectors and combine them with information, such as consumption patterns, to build a sophisticated image of how the water network is behaving . through the concept of IoT, data mining, big data and cloud. Measure and evaluate the impacts of the results obtained by the smart meter and that provide the applications for the correct decision making. Objectives Methodologycs: Strengthen the research line of the work team, which sets the standard for us as part of an academic body, of proposing projects based on Internet of Things, Big-Data and data mining technologies, using as a platform the potential of the microcontroller boards (Photon, Arduino, etc.), to enter the new industrial model 4.0 - environment. Create an academic body that carries out research activities through the use of different research methods for the solution to different problems within the institution. Contribution The water resource is essential for life, however, society rarely thinks about the different ways of use that is generally given, or the many activities of daily life in which it is present, and how our life It would change if its availability were near the end of its life cycle. The present project arises from the need to have a better control in the water consumption registers mainly in the Technological University of the North of Guanajuato which is the place where this research originates and from these registers allow an analysis of the data of consumption with greater accuracy and the best decision making.

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