scholarly journals Digitalization and Big Data in Smart Farming – Bibliometric and Systemic Analysis

2020 ◽  
Author(s):  
Jaqueline Iaksch ◽  
Ederson Fernandes ◽  
Milton Borsato
Keyword(s):  
Big Data ◽  
Value Chain ◽  
New Technologies ◽  
Research Area ◽  
Management Control ◽  
Digital Information ◽  
Agricultural Efficiency ◽  
Systemic Analysis ◽  
Smart Farming ◽  
Research Problems

Agriculture has always had a great significance in the civilization development. However, modern agriculture is facing increasing challenges due to population growth and environmental degradation. Commercially, farmers are looking for ways to improve profitability and agricultural efficiency to reduce costs. Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain. Thus, better decisions and efficient management control are required through generated information and knowledge at any farm. New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing, and thereby contribute to increased agricultural productivity. Therefore, this article aims to gain state-of-art insight and identify proposed solutions, trends and unfilled gaps regarding digitalization and Big Data applications in Smart Farming, through a literature review. The current study accomplished these goals through analyses based on ProKnow-C (Knowledge Development Process – Constructivist) methodology. A total of 2401 articles were found. Then, a quantitative analysis identified the most relevant ones among a total of 39 articles were included in a bibliometric and text mining analysis, which was performed to identify the most relevant journals and authors that stand out in the research area. A systemic analysis was also accomplished from these articles. Finally, research problems, solutions, opportunities, and new trends to be explored were identified.

Big Data in Cultural Tourism – Building Sustainability and Enhancing Competitiveness

2021 ◽  
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Value Chain ◽  
New Technologies ◽  
Cultural Tourism ◽  
End Users ◽  
Ethical Implications ◽  
Tourism Sector ◽  
Cultural Experiences ◽  
Selection Of

The use of big data is becoming increasingly important across the tourism sector and the value chain. With this publication, UNWTO intends to provide a baseline research on using big data by tourism and culture stakeholders, in order to improve the competitiveness of cultural tourism and reinforce its sustainability. The study sets the basis to connect tourism, culture and new technologies for mutual benefits, while calling for a reflection on the ethical implications for policymakers, businesses and end-users. The selection of case studies illustrates the most frequent case-scenarios of the use of big data in cultural tourism within destinations, compiled during the research. As the new technologies are facing ever-evolving scenarios, their use will be harnessed by the tourism sector in its endeavour to innovate and provide new cultural experiences.

scholarly journals SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE

2019 ◽  
Vol 01 (02) ◽  
pp. 77-86
Author(s):  
Smys S
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Research Area ◽  
Care Industry ◽  
The Core ◽  
The Cost

The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhancing the practical healthcare using the predictive modeling acquiring a deeper individual measures. This affords the researches to go through the huge set of data and identify the patterns along with the trends and delivering solutions improvising the medical care, minimizing the cost and he regulating the health admittance, ensuring the safety of human lives. The paper provides the survey on the predictive big data analysis and accuracy it provides in the health care system.

Big Data With IoT for Smart Farming

2021 ◽  
pp. 99-114
Author(s):  
Supriya M. S. ◽  
Meenaxy Roy
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Conventional Farming ◽  
Smart Farming ◽  
The World ◽  
Rapid Pace ◽  
Smart Agriculture ◽  
Iot Devices ◽  
Insight Into ◽  
Growing Population

Smart farming may also be called digital farming. The world is changing and digitizing at a quick rate. So all the work from agriculture to the stock market will become more productive and faster. Speed and efficiency play a key role in coping with the rapid pace of life and growing population. Smart agriculture has removed many of the problems faced by farmers during the conventional farming process. Several technologies are useful in this field, which make them work comfortably. Productivity in all areas of this sector can be increased with the aid of new technologies such as IoT and big data. Data can be accessed and analyzed from any part of the world with the help of IoT devices. The chapter offers insight into technology, such as big data and IoT, its applications in smart farming, as well as future innovations and opportunities.

scholarly journals Big Data Value Chain: Multiple Perspectives for the Built Environment

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4624
Author(s):  
Gema Hernández-Moral ◽  
Sofía Mulero-Palencia ◽  
Víctor Iván Serna-González ◽  
Carla Rodríguez-Alonso ◽  
Roberto Sanz-Jimeno ◽  
...  
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Value Chain ◽  
Energy Demand ◽  
New Technologies ◽  
Real Life ◽  
Multiple Perspectives ◽  
Energy Aware ◽  
Building Stock ◽  
Data Value

Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: “policy”, “technology” and “business”, in order to explore the main challenges and opportunities in this area.

scholarly journals ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1796 ◽  
Author(s):  
Mohamed Adel Serhani ◽  
Hadeel T. El Kassabi ◽  
Heba Ismail ◽  
Alramzana Nujum Navaz
Keyword(s):  
Value Chain ◽  
New Technologies ◽  
Relevant Literature ◽  
Research Area ◽  
Monitoring Systems ◽  
Ecg Monitoring ◽  
Current Trends ◽  
Extensive Analysis ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

scholarly journals SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE

2019 ◽  
Vol 01 (02) ◽  
pp. 77-86
Author(s):  
Smys S
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Research Area ◽  
Care Industry ◽  
The Core ◽  
The Cost

The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhancing the practical healthcare using the predictive modeling acquiring a deeper individual measures. This affords the researches to go through the huge set of data and identify the patterns along with the trends and delivering solutions improvising the medical care, minimizing the cost and he regulating the health admittance, ensuring the safety of human lives. The paper provides the survey on the predictive big data analysis and accuracy it provides in the health care system.

De-Identification of Unstructured Textual Data using Artificial Immune System for Privacy Preserving

2016 ◽  
Vol 8 (4) ◽  
pp. 34-49 ◽  
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou ◽  
Mohamed Amine Boudia ◽  
Hadj Ahmed Bouarara
Keyword(s):  
Big Data ◽  
High Efficiency ◽  
New Technologies ◽  
Artificial Immune Systems ◽  
Research Area ◽  
Heterogeneous Data ◽  
Small Scale ◽  
Artificial Immune ◽  
Critical Research ◽  
Textual Data

The development of new technologies has led the world into a tipping point. One of these technologies is the big data which made the revolution of computer sciences. Big data has come with new challenges. These challenges can be resumed in the aim of creating scalable and efficient services that can treat huge amounts of heterogeneous data in small scale of time while preserving users' privacy. Textual data occupy a wide space in internet. These data could contain information that can lead to identify users. For that, the development of such approaches that can detect and remove any identifiable information has become a critical research area known as de-identification. This paper tackle the problem of privacy in textual data. The authors' proposed approach consists of using artificial immune systems and MapReduce to detect and hide identifiable words with no matter on their variants using the personnel information of the user from his profile. After many experiments, the system shows a high efficiency in term of number of detected words, the way they are hided with, and time of execution.

scholarly journals A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey

2018 ◽  
Vol 7 (03) ◽  
pp. 23755-23760
Author(s):  
S. Dhivya ◽  
Dr.R. Shanmugavadivu
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Big Data ◽  
Edge Detection ◽  
Research Area ◽  
Detection Methods ◽  
Image Mining ◽  
Survey Paper ◽  
Research Problems ◽  
Image Pattern

In Today’s era Big Data is one of the most well-known research area that try to solve many research problems. The focus is mainly on how to come out those problems of Big Data and it could be handling in recent systems. Image mining and genetic algorithm is used to automate the process of images, patterns, data sets and etc. Image mining is used to extract the hidden images from the set of images. Genetic algorithm is also quite effective in solving certain optimization and intelligence problems and it is used in many applications, including image pattern recognition. The survey paper reviews of Big Data with edge detection methods on various types of images. In edge detection image pattern recognition is to choose the best images from the group of images by using both image mining and genetic algorithm techniques

scholarly journals The Impact of Big Data on Electronic Commerce in Profit Organisations in Saudi Arabia

2019 ◽  
Vol 10 (4) ◽  
pp. 106
Author(s):  
Bader A. Alyoubi
Keyword(s):  
Saudi Arabia ◽  
Big Data ◽  
Inventory Management ◽  
New Technologies ◽  
Predictive Analytics ◽  
Business Environment ◽  
Primary Data ◽  
Research Approach ◽  
Multiple Sources ◽  
The Impact

Big Data is gaining rapid popularity in e-commerce sector across the globe. There is a general consensus among experts that Saudi organisations are late in adopting new technologies. It is generally believed that the lack of research in latest technologies that are specific to Saudi Arabia that is culturally, socially, and economically different from the West, is one of the key factors for the delay in technology adoption in Saudi Arabia. Hence, to fill this gap to a certain extent and create awareness about Big Data technology, the primary goal of this research was to identify the impact of Big Data on e-commerce organisations in Saudi Arabia. Internet has changed the business environment of Saudi Arabia too. E-commerce is set for achieving new heights due to latest technological advancements. A qualitative research approach was used by conducting interviews with highly experienced professional to gather primary data. Using multiple sources of evidence, this research found out that traditional databases are not capable of handling massive data. Big Data is a promising technology that can be adopted by e-commerce companies in Saudi Arabia. Big Data’s predictive analytics will certainly help e-commerce companies to gain better insight of the consumer behaviour and thus offer customised products and services. The key finding of this research is that Big Data has a significant impact in e-commerce organisations in Saudi Arabia on various verticals like customer retention, inventory management, product customisation, and fraud detection.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

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