scholarly journals Practical Classification and Evaluation of Optically Recorded Food Data by Using Various Big-Data Analysis Technologies

Machines ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Tim Jarschel ◽  
Christoph Laroque ◽  
Ronny Maschke ◽  
Peter Hartmann
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Monitoring And Evaluation ◽  
Life Cycles ◽  
Automated System ◽  
Time Data ◽  
Production Technologies ◽  
Set Up ◽  
Advanced Production ◽  
Data Visualizations

An increasing shortening of product life cycles, as well as the trend towards highly individualized food products, force manufacturers to digitize their own production chains. Especially the collection, monitoring, and evaluation of food data will have a major impact in the future on how the manufacturers will satisfy constantly growing customer demands. For this purpose, an automated system for collecting and analyzing food data was set up to promote advanced production technologies in the food industry. Based on the technique of laser triangulation, various types of food were measured three-dimensionally and examined for their chromatic composition. The raw data can be divided into individual data groups using clustering technologies. Subsequent indexing of the data in a big data architecture set the ground for setting up real-time data visualizations. The cluster-based back-end system for data processing can also be used as an organization-wide communication network for more efficient monitoring of companies’ production data flows. The results not only describe the procedure for digitization of food data, they also provide deep insights into the practical application of big data analytics while helping especially small- and medium-sized enterprises to find a good introduction to this field of research.

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

scholarly journals Use of Big Data to Improve Environmental Sustainability in Developing Countries

2017 ◽  
Vol 12 (11) ◽  
pp. 249 ◽  
Author(s):  
Maged Adel Abdo Mukred ◽  
Zheng Jianguo
Keyword(s):  
Big Data ◽  
Multinational Corporations ◽  
Environmental Sustainability ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Government Sector ◽  
Wide Range ◽  
Private And Public ◽  
The Government ◽  
Set Up

Big data inhibits the ability to significantly impact a wide range of fields in an economy, from the government sector to commercial sectors like retail and healthcare. Not only has it altered the way companies assess their product’s demand and supply patterns but has also phenomenally helped in making the environment healthier in recent years. It carries the ability to identify valuable data from a huge dataset with exceptional parallel processing. This study presents the general introduction of big data bringing forth its various features and advantages along with the challenges which organizations face while using with respect to environmental sustainability. Observations have also been made on the findings of various researches, and studies and surveys performed by some international organizations in the recent years on the urgent need of taking necessary measures and initiatives to prevent further depletion of natural resources thus making the environment sustainable. Making the issue the study aim, future studies must intend to explore how multinational corporations can enhance environmental sustainability through big data analytics. Lastly, recommendations have been made to organisations– private and public in hiring adequate expertise and set-up, thereby making big data analytics more efficient and reliable.

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

scholarly journals TOWARDS EFFECTIVE CUSTOMER RELATIONSHIP MANAGEMENT IN OMAN: ROLE OF BIG DATA

2017 ◽  
Vol 5 (10) ◽  
pp. 92-100
Author(s):  
Tarek Khalil ◽  
Al-Refai Mohammad ◽  
Amer Nizar Fayez ◽  
SharafQudah Mohammed
Keyword(s):  
Big Data ◽  
Customer Relationship Management ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Relationship Management ◽  
Qualitative Evaluation ◽  
Customer Relationship ◽  
Customer Engagement ◽  
Time Data ◽  
Analytical Tools

We established a framework to explore the feasibility of enabling big data within the customer relationship management (CRM) strategies in Oman for creating sustainable business profit nationwide. A qualitative evaluation was made based on predictive analytics convergence and big data facilitated CRM. It was found that the big data analytics can meticulously alter the competitive industrial setting, and thereby proffered notable benefits to the business organization in terms of operation, strategies, and competitiveness. Results revealed that companies must introduce analytical tools, real-time data, and hire talented as well as skilled employees to improve the productivity in consistent with the new business model. Furthermore, depending on the customer engagement, an assemblage and analysis of enormous data volume together with analytical tools was discerned to assist companies towards efficient resource allocation and capital spending. The implications of using big data for CRM in Oman and way forward were emphasized.

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

Big Data Visualizations in Organizational Science

2017 ◽  
Vol 21 (3) ◽  
pp. 660-688 ◽  
Author(s):  
Louis Tay ◽  
Vincent Ng ◽  
Abish Malik ◽  
Jiawei Zhang ◽  
Junghoon Chae ◽  
...  
Keyword(s):  
Big Data ◽  
Police Officers ◽  
Performance Criteria ◽  
Time Data ◽  
Fortune 500 Companies ◽  
Language Data ◽  
Data Points ◽  
Basic Ideas ◽  
Organizational Science ◽  
Data Visualizations

Visualizations in organizational research have primarily been used in the context of traditional survey data, where individual data points (e.g., responses) can typically be plotted, and qualitative (e.g., language data) and quantitative (e.g., frequency data) information are not typically combined. Moreover, visualizations are typically used in a hypothetico-deductive fashion to showcase significant hypothesized results. With the advent of big data, which has been characterized as being particularly high in volume, variety, and velocity of collection, visualizations need to more explicitly and formally consider the issues of (a) identification (isolating or highlighting relevant data pertaining to the phenomena of interest), (b) integration (combining different modes of data to reveal insights about a phenomenon of interest), (c) immediacy (examining real-time data in a time-sensitive manner), and (d) interactivity (inductively uncovering and identifying new patterns). We discuss basic ideas for addressing these issues and provide illustrative examples of visualizations that incorporate and highlight ways of addressing these issues. Examples in our article include visualizing multiple performance criteria for police officers, publication network of organizational researchers, and social media language of Fortune 500 companies.

scholarly journals TELECOM BIG DATA FOR URBAN TRANSPORT ANALYSIS – A CASE STUDY OF SPLIT-DALMATIA COUNTY IN CROATIA

2017 ◽  
Vol XLII-4/W3 ◽  
pp. 5-10 ◽  
Author(s):  
M. Baučić ◽  
N. Jajac ◽  
M. Bućan
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
New Technologies ◽  
Big Data Analytics ◽  
Urban Transport ◽  
Time Data ◽  
Customer Data ◽  
Gis Applications ◽  
International Transport

Today, big data has become widely available and the new technologies are being developed for big data storage architecture and big data analytics. An ongoing challenge is how to incorporate big data into GIS applications supporting the various domains. International Transport Forum explains how the arrival of big data and real-time data, together with new data processing algorithms lead to new insights and operational improvements of transport. Based on the telecom customer data, the Study of Tourist Movement and Traffic in Split-Dalmatia County in Croatia is carried out as a part of the “IPA Adriatic CBC//N.0086/INTERMODAL” project. This paper briefly explains the big data used in the study and the results of the study. Furthermore, this paper investigates the main considerations when using telecom customer big data: data privacy and data quality. The paper concludes with GIS visualisation and proposes the further use of big data used in the study.

Big data: The effect of analytics on marketing and business

2021 ◽  
pp. 204388692110572
Author(s):  
Barbara A. Manko
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Bottom Line ◽  
Real Time Data ◽  
The Subject ◽  
Successful Business

Big data analytics takes raw, real-time data and uses it to predict trends. Successful use of this data can have a powerful impact on a business’s effectiveness and ultimately their bottom line. As the amount of data increases, the need for analytics is growing. This teaching study discusses the role of social media in data analytics, how to approach the subject, and the desired outcomes. Students will explore the expansion of this field of study, familiarize themselves with the concept and where they may have encountered it in their lives so far, and discuss what analytics can contribute to running a successful business.

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.

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