scholarly journals Recent Trends of Big Data in Precision Agriculture: a Review

2021 ◽  
Vol 1096 (1) ◽  
pp. 012081
Author(s):  
A I Sourav ◽  
A W R Emanuel
Keyword(s):  
Big Data ◽  
Precision Agriculture ◽  
Recent Trends

scholarly journals IOT And Big Data Analytics In E-Learning: A Technological Perspective and Review

2018 ◽  
Vol 7 (1.8) ◽  
pp. 164 ◽  
Author(s):  
S Kusuma ◽  
D Kasi Viswanath
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Culture ◽  
Educational Institutions ◽  
Learning Approaches ◽  
E Learning ◽  
Recent Trends ◽  
Conducting Research ◽  
The Internet Of Things

The internet of things & Big data analytics in eLearning brings tremendous challenges & opportunities to educational institutions & students. In recent trends, the growth of Pervasive computing, Social media, evolving IoT capabilities, technologies such as cloud computing, and big data and analytics are improving the core values of teaching and conducting research but also instilling a new digital culture and developing an IoT-centric society. The primary purpose of this paper is to provide an impact of IoT & Big data analytics in the area of E-learning and study on different E-learning approaches. 

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

Big Data Predictive and Prescriptive Analytics

Big Data ◽  
2016 ◽  
pp. 30-55 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Recent Development in Big Data Analytics

2021 ◽  
pp. 1640-1663
Author(s):  
M. Sandeep Kumar ◽  
Prabhu J.
Keyword(s):  
Big Data ◽  
Analytical Method ◽  
Big Data Analytics ◽  
Analysis Data ◽  
Heterogeneous Data ◽  
Distribution Management ◽  
Technology Changes ◽  
Recent Trends ◽  
Audio Video ◽  
Storage Distribution

This chapter describes how big data consist of an extreme volume of data, velocity, and more complex variable data that demands current technology changes in capturing, storage, distribution, management, analysis data. Business facing more struggles in identifying the pragmatic approach in capturing the data about customer, products, and services. Usage of big data mainly with the analytical method, but it specifically compares with features of an analytical method based on unstructured data contributed around 95% of big data. The analytical approach depends on heterogeneous data and unstructured data's like text, audio, video format. It demands new effective tool for predictive analysis for big data with the unstructured format. This chapter describes explanation of big data and characteristics of big data compress of Volume, Velocity, Variety, Variability, and Value. Recent trends in the development of big data that applies in real time application perspectives like health care agriculture, education etc.

A Study on Recent Trends in Cloud-Based Data Processing for IoT Era

2021 ◽  
pp. 2531-2557
Author(s):  
John Shiny J. ◽  
Karthikeyan P.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Storage ◽  
Mobile Phones ◽  
Advance Research ◽  
Recent Trends ◽  
Machine To Machine Communication ◽  
Machine Communication ◽  
Big Data Storage

Cloud computing has become one of the most important technologies in our day-to-day lives. The computing resources are delivered to the customers based on subscription basis via internet. Big data storage and processing are main application of cloud. Furthermore, the development of internet of things provides the platform for interconnecting devices over internet. This includes everything from mobile phones, washing machines, lamps, headphones, wearable devices, and everything else we never think of. This enables machine-to-machine communication, also applies to the components of the machine. The main objective of this chapter is to give an overview of cloud computing, big data, and internet of things and the advance research topics.

Deep Learning Applications in Agriculture

2021 ◽  
pp. 325-345
Author(s):  
Hari Kishan Kondaveeti ◽  
Gonugunta Priyatham Brahma ◽  
Dandhibhotla Vijaya Sahithi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Rapid Growth ◽  
Agricultural Sector ◽  
The Other ◽  
Learning Methods ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Recent Trends

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

scholarly journals Big Data and MapReduce Challenges, Opportunities and Trends

2016 ◽  
Vol 6 (6) ◽  
pp. 2911
Author(s):  
Sachin Arun Thanekar ◽  
K. Subrahmanyam ◽  
A. B. Bagwan
Keyword(s):  
Big Data ◽  
High Velocity ◽  
Structured Data ◽  
Unstructured Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Commodity Hardware ◽  
Survey Paper ◽  
Recent Trends ◽  
Large Clusters

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data.  The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.</p>

Sign in / Sign up

Export Citation Format

Share Document