Big Data Analytics and Visualization for Food Health Status Determination Using Bigmart Data

2022 ◽  
pp. 520-539
Author(s):  
Sumit Arun Hirve ◽  
Pradeep Reddy C. H.
Keyword(s):  
Visual Analytics ◽  
Data Analytics ◽  
Recommendation System ◽  
Predictive Accuracy ◽  
Big Data Analytics ◽  
Huge Amount ◽  
R Language ◽  
Commercial Market ◽  
The One ◽  
Visualization Techniques

Being premature, the traditional data visualization techniques suffer from several challenges and lack the ability to handle a huge amount of data, particularly in gigabytes and terabytes. In this research, we propose an R-tool and data analytics framework for handling a huge amount of commercial market stored data and discover knowledge patterns from the dataset for conveying the derived conclusion. In this chapter, we elaborate on pre-processing a commercial market dataset using the R tool and its packages for information and visual analytics. We suggest a recommendation system based on the data which identifies if the food entry inserted into the database is hygienic or non-hygienic based on the quality preserved attributes. For a precise recommendation system with strong predictive accuracy, we will put emphasis on Algorithms such as J48 or Naive Bayes and utilize the one who outclasses the comparison based on accuracy. Such a system, when combined with R language, can be potentially used for enhanced decision making.

Big Data Analytics and Visualization for Food Health Status Determination Using Bigmart Data

2020 ◽  
pp. 179-205
Author(s):  
Sumit Arun Hirve ◽  
Pradeep Reddy C. H.
Keyword(s):  
Visual Analytics ◽  
Data Analytics ◽  
Recommendation System ◽  
Predictive Accuracy ◽  
Big Data Analytics ◽  
Huge Amount ◽  
R Language ◽  
Commercial Market ◽  
The One ◽  
Visualization Techniques

Being premature, the traditional data visualization techniques suffer from several challenges and lack the ability to handle a huge amount of data, particularly in gigabytes and terabytes. In this research, we propose an R-tool and data analytics framework for handling a huge amount of commercial market stored data and discover knowledge patterns from the dataset for conveying the derived conclusion. In this chapter, we elaborate on pre-processing a commercial market dataset using the R tool and its packages for information and visual analytics. We suggest a recommendation system based on the data which identifies if the food entry inserted into the database is hygienic or non-hygienic based on the quality preserved attributes. For a precise recommendation system with strong predictive accuracy, we will put emphasis on Algorithms such as J48 or Naive Bayes and utilize the one who outclasses the comparison based on accuracy. Such a system, when combined with R language, can be potentially used for enhanced decision making.

scholarly journals Commercial Visual Analytics Systems–Advances in the Big Data Analytics Field

2019 ◽  
Vol 25 (10) ◽  
pp. 3011-3031 ◽  
Author(s):  
Michael Behrisch ◽  
Dirk Streeb ◽  
Florian Stoffel ◽  
Daniel Seebacher ◽  
Brian Matejek ◽  
...  
Keyword(s):  
Big Data ◽  
Visual Analytics ◽  
Data Analytics ◽  
Big Data Analytics

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 Analytics in Aquaculture Using Hive and Hadoop Platform

2018 ◽  
pp. 29-35
Author(s):  
P. Venkateswara Rao ◽  
A. Ramamohan Reddy ◽  
V. Sucharita
Keyword(s):  
Big Data ◽  
Data Management ◽  
Single Machine ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Hadoop Platform ◽  
Different Sources

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.

Coding Inequality

2020 ◽  
pp. 100-117
Author(s):  
Sarah Brayne
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Social Inequalities ◽  
Big Data Analytics ◽  
The Other ◽  
Surveillance Network ◽  
Data Intensive ◽  
The One ◽  
Reproduction Of Inequality

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

scholarly journals A Big Data Analytics Approach for the Development of Advanced Cardiology Applications

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 60 ◽  
Author(s):  
Lorenzo Carnevale ◽  
Antonio Celesti ◽  
Maria Fazio ◽  
Massimo Villari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Response Times ◽  
Distributed Processing ◽  
Big Data Analytics ◽  
Reliable System ◽  
Huge Amount ◽  
Ecg Signals ◽  
Big Data Applications ◽  
Electrocardiogram Ecg

Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues. In this paper, we focus on the arrhythmia detection problem. Specifically, our objective is to address the problem of distributed processing considering big data generated by electrocardiogram (ECG) signals in order to carry out pre-processing analysis. Specifically, an algorithm for the identification of heartbeats and arrhythmias is proposed. Such an algorithm is designed in order to carry out distributed processing over the Cloud since big data could represent the bottleneck for cardiology applications. In particular, we implemented the Menard algorithm in Apache Spark in order to process big data coming form ECG signals in order to identify arrhythmias. Experiments conducted using a dataset provided by the Physionet.org European ST-T Database show an improvement in terms of response times. As highlighted by our outcomes, our solution provides a scalable and reliable system, which may address the challenges raised by big data in healthcare.

scholarly journals Survey: Big Data Analytics in Agricultural Products Logistics

2020 ◽  
pp. 14-16
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Agricultural Products ◽  
Agricultural Product ◽  
Quality And Safety ◽  
Huge Amount ◽  
Safety Issues ◽  
Food Quality And Safety ◽  
Overall Performance

Big Data Analytics is one of the most cutting edge technology in the world. Big Data Analytics will provide data management to store, process and analyze the huge amount of data. Agricultural is one of the domains that assuring big data analytics used to make a change in the field. With the consistent enhancements of the peoples living lifestyle, step by step peoples concentrate on the demand of perishable agricultural products. However, the periodic eruption of food quality and safety issues affected the concern of the end-users. To enrich the distribution performance of agricultural product logistics and to provide the freshness, quality, and safety of the agricultural products has become a thread of the current agricultural domain. Big Data Analytics in Agricultural Products Logistics has an essential prospect of optimizing the distribution path of products, prediction of product demands in the market, traceability of the products, analyzing customer feedbacks and increasing overall performance of logistics in agriculture. This paper investigates the key challenges, methods used, technologies used, algorithms for distribution of products and future of Big Data Analytics in agro-logistics.

scholarly journals A Neural Network-Inspired Approach for Improved and True Movie Recommendations

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Muhammad Ibrahim ◽  
Imran Sarwar Bajwa ◽  
Riaz Ul-Amin ◽  
Bakhtiar Kasi
Keyword(s):  
Neural Network ◽  
Data Analytics ◽  
Recommendation System ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Mobile App ◽  
Apache Hadoop ◽  
External Resources ◽  
Movie Recommendation ◽  
Day By Day

In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

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