Big Data Analytics for the Daily Living Activities of the People with Dementia

2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Dennis T. Kennedy ◽

Dennis M. Crossen ◽

Kathryn A. Szabat

Keyword(s):

Big Data ◽

Data Analytics ◽

Quantitative Methods ◽

Business Processes ◽

Predictive Analytics ◽

Big Data Analytics ◽

Database Systems ◽

Three Dimensions ◽

Business Analytics ◽

The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

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Power of Big Data System for Storing and Processing Huge Data

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst196422 ◽

2019 ◽

pp. 138-142

Author(s):

S. Natarajan ◽

S. Rajarajesware ◽

Suresh Ram R

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Data System ◽

Data Systems ◽

Data Infrastructure ◽

Work From Home ◽

Architecture Framework ◽

Huge Data ◽

The People

Big data uses storage of huge data with some approaches and techniques to manage and process them. During the past few years the number of persons using internet, email and other internet-based applications has been growing tremendously. Big Data is mainly characterized by 3V’s (Volume, Velocity and, Variety). The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem. BDAF includes components such as Big Data Infrastructure, Big Data Analytics, Data structures & models, Big Data Lifecycle Management and Big Data Security. Nowadays the volume of data used by the people throughout the world is increasing enormously and exponentially. So, the need for storing, processing and protecting large volume of data has been becoming a great challenge in the modern hyper-connected world. On the basis of work from home concept lot of software professionals are doing their jobs with their internet connected systems for development, implementation, testing and maintenance of various softwares. These professionals and experts are sending and receiving lot of data to various locations to their clients, higher authorities and other officials frequently depending upon their requirements. The traditional data management models are not efficient for today’s exponentially growing data from variety of industries. This challenging task of storing and managing huge volume of data is achieved in Big Data Systems. In this paper we try to give an overview of Big Data Analytics system for storing and processing huge volume of various types of data. Overwhelming the security threats due to various factors like viruses, worms, etc are also great challenges to protect huge volume of data in a big data system.

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BIG DATA ANALYTICS IN BANKING AND FINANCIAL SERVICES SECTOR

EPRA International Journal of Research & Development (IJRD) ◽

10.36713/epra6558 ◽

2020 ◽

pp. 92-96

Author(s):

Dr.P.V. Mohini ◽

Mr. Rohit Kumar Srivastav

Keyword(s):

Big Data ◽

Financial Services ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Analysis ◽

Valuable Data ◽

Services Sector ◽

Financial Services Sector ◽

The People ◽

Potential Benefits

‘He who holds the wealth of information, holds the solution to the toughest of the situations’ this quote is very much apt for the recent ongoing scenario, where there is an invisible struggle going on among the organizations as well as nations in the search for more and more information. Now, when the Banking and Financial services sectors are put under the scanner, it becomes evident that they are sitting on top of a humungous heap of valuable data. This data can be used for the betterment and advancement of the industry as well as the people. While it is good to have large amount of data available, it will be termed a big pile of trash if it is not analyzed properly and the results obtained from it are not put to use. With the adoption of Big Data analytics into the banking and financial services, many obvious as well as concealed problems can be addressed to and even solved quickly. The main objective of this paper is to highlight the meaning of Big Data analysis, study the types of data analytics with respect to Banking and Financial services sector and to showcase the potential benefits of embracing Big Data analytics into the Banking & Financial services sector. KEYWORDS: Information, Banking and Financial services, Advancement, Big Data, Data Analytics

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Movie Analytics for Effective Recommendation System using Pig with Hadoop

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2016040106 ◽

2016 ◽

Vol 3 (2) ◽

pp. 82-100 ◽

Cited By ~ 13

Author(s):

Arushi Jain ◽

Vishal Bhatnagar

Keyword(s):

Big Data ◽

Science Fiction ◽

Data Analytics ◽

Recommendation System ◽

18Th Century ◽

Big Data Analytics ◽

Data Set ◽

The People ◽

Late 18Th Century

Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.

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Business Analytics and Big Data

Big Data ◽

10.4018/978-1-4666-9840-6.ch082 ◽

2016 ◽

pp. 1773-1783

Author(s):

Dennis T. Kennedy ◽

Dennis M. Crossen ◽

Kathryn A. Szabat

Keyword(s):

Big Data ◽

Data Analytics ◽

Quantitative Methods ◽

Business Processes ◽

Predictive Analytics ◽

Big Data Analytics ◽

Database Systems ◽

Three Dimensions ◽

Business Analytics ◽

The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

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Current Approaches to Advanced Research in Data Science at International Top League Universities

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8944 ◽

2020 ◽

Vol 17 (6) ◽

pp. 2806-2811

Author(s):

Wahidah Hashim ◽

A/L Jayaretnam Prathees ◽

Marini Othman ◽

Andino Maseleno

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

World Data ◽

The People ◽

Software Engineers ◽

Data Scientist ◽

The Right

Data Science also known as Analytics, has a high demand in the industries right now, where professionals who are well trained in this field are being recruited by many large companies. Before the existence of data science, companies and industries search for software engineers and data analysis to sort IT related problems. However, as the internet start to being used by most of the people in the world, data keep on pouring in a large volume and velocity, software engineers and data analysis could not handle it anymore. Analyzing the tremendous size of data is called Big Data Analytics. Corporate companies have already started to realize that data scientists are the right person to tackle Big Data related problems. Low supply of data scientist has hiked in the salary of the data scientist, as the pay for a data scientist many more time higher compare to other IT related professionals. Knowledge in data science can solve any data related problems in this world. Data scientist are not only recruited by tech-giants like Google and Amazon, medium organizations also started to understand the importance of data science and they too recruit data scientist for their company. In this paper, we will explore on the requirement and knowledges of data science that can be covered in UNITEN’s computer science syllabus.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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