scholarly journals Contemporary Issues in the Ethics of Data Analytics in Ride-Hailing Service

2019 ◽  
Vol 2 (2) ◽  
pp. 44-57
Author(s):  
Victor Chang ◽  
Yujie Shi ◽  
Xuemin Li
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Transportation Network ◽  
Behavioral Analysis ◽  
Smartphone App ◽  
Ethical Problems ◽  
Independent Contractors ◽  
The Usa ◽  
Order Assignment ◽  
Big Data Technology

Big data technology has brought about the establishment of transportation network companies (TNCs), such as Uber in the USA, and Didi in China, who provide the ride hailing services (RHS's) which allows the individuals to act as independent contractors serving customers via a smartphone app. The RHS system installed gigantic databases which can store enormous data and its equipment can collect various kinds of data at any time. Additionally, data analysis technologies in TNCs such as behavioral analysis, heatmap optimization surge pricing and the automatic order assignment mechanisms, can make the RHS more efficient. However, along with achievements big data technology offers, we are struggling towards the ethical problems, including privacy, inequity and safety.

Classification of Pests for Rice Crop Using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 27-31
Author(s):  
R. P. L. Durgabai ◽  
P. Bhargavi ◽  
S. Jyothi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Andhra Pradesh ◽  
Big Data Analytics ◽  
Rice Crop ◽  
Strategic Decisions ◽  
High Productivity ◽  
Almost All ◽  
Big Data Technology

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.

Big Data Analytics for Intrusion Detection

2020 ◽  
pp. 292-316 ◽  
Author(s):  
Luis Filipe Dias ◽  
Miguel Correia
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Semantic Gap ◽  
Behavioral Analysis ◽  
Event Management ◽  
Security Monitoring ◽  
Hidden Correlations ◽  
Security Information

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security's big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.

scholarly journals The importance of big data technology

2018 ◽  
Vol 7 (4.5) ◽  
pp. 485
Author(s):  
Samson Fadiya ◽  
Arif Sari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Systems ◽  
Apache Hadoop ◽  
Industry Standard ◽  
Data Framework ◽  
Efficiency And Effectiveness ◽  
Relational Database Management ◽  
Big Data Technology

The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectiveness at a business level. Apache Hadoop is an example of such a framework. This report discusses big data, it origins, opportunities and challenges that it presents, big data analytics and the application of big data using existing big data tools or frameworks. It also discusses Apache Hadoop as a big data framework and provides a basic overview of this technology from technological and business perspectives.  

scholarly journals Machine Learning Based Mathematical Modelling for Prediction of Social Media Consumer behaviour using Big Data Analytics

2021 ◽  
Author(s):  
Kiran Chaudhary ◽  
Mansaf Alam ◽  
Mabrook S. Al-Rakhami ◽  
Abdu Gumaei
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Consumer Behaviour ◽  
Big Data Analytics ◽  
Quality Data ◽  
Volume Data ◽  
Process Data ◽  
Big Data Technology

Abstract Almost many consumers are inclined by social media to purchase the product and spend more money on purchasing. We got the data from social media to analyse the consumer behaviour. We have considered the consumer data from Facebook, Twitter, LinkedIn and YouTube. There is diversity and high-speed, high volume data is coming from social media, so we used big data technology. Big Data Technology is the recent technology is used in various field of research. In this paper we have used the concept of big data technology to process data and analyse to predict the consumer behaviour on social media. We have analysed the consumer behaviour based on certain parameter and criteria. we have analysed the consumer perception, attitude towards the social media. For doing the prediction we have pre-process the data to make the quality data so that we can take the quality decision based on outcome of our model. We have used the predictive big data analytics technique to analyse the consumer behaviour prediction in this paper.

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud

2014 ◽  
pp. 209-215
Author(s):  
Pushpa Mannava
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Evaluation Method ◽  
Big Data Analytics ◽  
Modern Technology ◽  
Data Evaluation ◽  
Storage Space ◽  
Big Data Technology

Big Data is a data evaluation method makes it possible for by recent breakthroughs in details and interactions modern technology. However, big data evaluation requires a massive quantity of calculating resources making fostering costs of big data technology is not inexpensive for lots of small to tool business. In this paper, we detail the benefits as well as obstacles associated with deploying big data analytics through cloud computing. We suggest that cloud computer can support the storage space as well as computing requirements of big data analytics. This paper provides a detailed overview of cloud computing and deployment of big data analytics in the cloud.

scholarly journals Underpinnings of Big Data Analytics and Its Applications

2020 ◽  
pp. 75-81
Author(s):  
Anand Kumar Pandey ◽  
Rashmi Pandey ◽  
Ashish Tripathi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Issues ◽  
New Developments ◽  
New Ideas ◽  
Analytical Assessment ◽  
Data Analytic ◽  
Big Data Technology

Big data and Data Mining are co-related to each other and also emphasize the phenomena of extracting and analysis useful data from considerable database. The concept of Big Data analytics plays a very significant role in several fields, such as Data Mining, Education and Training, cloud computing, E-commerce, healthcare and life science, Banking and Agriculture. Big data Analytic is a technique for looking at big set of data to expose hidden patterns. A large amount of data is continuously generated every day using modern information system and technologies. As a result this paper provides a platform to investigate applications of big data at various stages. In future, it come forward to be a required for an analytical assessment of new developments in the big data technology. In addition, it also explores a new and suitable outlook for researchers to expand the solution, based on the literature survey, challenges, new ideas and open research issues.

The marketing organization’s journey to become data-driven

2019 ◽  
Vol 13 (2) ◽  
pp. 162-178 ◽  
Author(s):  
Devon S. Johnson ◽  
Laurent Muzellec ◽  
Debika Sihi ◽  
Debra Zahay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Dynamic Learning ◽  
Marketing Practice ◽  
Content Type ◽  
Marketing Function ◽  
The Usa ◽  
Research Questions

Purpose This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation. Design/methodology/approach A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions. Findings The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization. Originality/value From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.

An Application of Big Data Analytics in Road Transportation

2018 ◽  
pp. 39-54 ◽  
Author(s):  
Rajit Nair ◽  
Amit Bhagat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Transportation Network ◽  
Big Data Analytics ◽  
Congestion Management ◽  
Road Transportation ◽  
Fine Grained ◽  
Traffic Routing ◽  
Routing Congestion ◽  
Data Analysis Methods

Data is being captured in all domains of society and one of the important aspects is transportation. Large amounts of data have been collected, which are detailed, fine-grained, and of greater coverage and help us to allow traffic and transportation to be tracked to an extent that was not possible in the past. Existing big data analytics for transportation is already yielding useful applications in the areas of traffic routing, congestion management, and scheduling. This is just the origin of the applications of big data that will ultimately make the transportation network able to be managed properly and in an efficient way. It has been observed that so many individuals are not following the traffic rules properly, especially where there are high populations, so to monitor theses types of traffic violators, this chapter proposes a work that is mainly based on big data analytics. In this chapter, the authors trace the vehicle and the data that has been collected by different devices and analyze it using some of the big data analysis methods.

scholarly journals Distributed Computing Engines for Big Data Analytics

2019 ◽  
Vol 8 (2) ◽  
pp. 5841-5845
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Block Size ◽  
Apache Spark ◽  
Distributed Programming ◽  
Programming Paradigm ◽  
Programming Framework ◽  
Configuration Changes ◽  
Big Data Technology

Technologies like cloud computing paved way for dealing with massive amounts of data. Prior to cloud, it was not possible unless you invest large amounts for computing resources. Now there is ecosystem which is conducive to storing and processing voluminous data that cannot be handled by local computing resources. With such ecosystem, big data technology came into existence. Big data is the data characterized by volume, velocity, veracity and variety. This has enabled enterprises to give more value to every piece of data. This in turn led to the increased usage of cloud for both storage and processing. For processing big data efficient technologies are required. New programming paradigm like MapReduce with Hadoop distributed programming framework is widely used. However, there are other emerging frameworks like Apache Spark and Apache Flink to handle big data more efficiently. In this paper, empirical study is made on the three frameworks like Hadoop, Apache Spark and Apache Flink with different parameters like type of network, block size of HDFS, input data size and other configuration changes. The experimental results revealed that Apache Spark and Apache Flink outperform Hadoop. This is evaluated with different benchmark big data workloads.

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