Fast Data vs. Big Data With IoT Streaming Analytics and the Future Applications

10.4018/978-1-6684-3662-2.ch092 ◽

2022 ◽

pp. 1876-1891

Author(s):

A. Jayanthiladevi ◽

Surendararavindhan ◽

Sakthivel

Keyword(s):

Big Data ◽

Data Streaming ◽

The Future ◽

Computing Environments ◽

Unstructured Information

Big data depicts information volume – petabytes to exabytes in organized, semi-organized, and unstructured information that can possibly be broken down for data. Fast data are facts streaming into applications and computing environments from hundreds of thousands to millions of endpoints. Fast data is totally different from big data. There is no question that we will continue generating large volumes of data, especially with the wide variety of handheld units and internet-connected devices expected to grow exponentially. Data streaming analytics is vital for disruptive applications. Streaming analytics permits the processing of terabytes of data in memory. This chapter explores fast data and big data with IoT streaming analytics.

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A Review on Big Data Analytics in Business

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841130 ◽

2018 ◽

pp. 210-214

Author(s):

Manbir Sandhu ◽

Purnima, Anuradha Saini

Keyword(s):

Big Data ◽

Big Data Analytics ◽

Smart Devices ◽

Data Streaming ◽

Huge Amount ◽

Business Units ◽

Enormous Amount ◽

Tools And Techniques ◽

Heath Care ◽

Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

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Challenges and Cloud Computing Environments Towards Big Data

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207277 ◽

2014 ◽

pp. 203-208

Author(s):

Kiran Kumar S V N Madupu

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Big Data ◽

Technology Development ◽

Computing Environments ◽

Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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Artistic Challenges within Control Societies: Big Data and Democratic Resistance

MedienJournal ◽

10.24989/medienjournal.v38i4.88 ◽

2017 ◽

Vol 38 (4) ◽

pp. 50-61 ◽

Cited By ~ 1

Author(s):

Jan Jagodzinski

Keyword(s):

Big Data ◽

Gilles Deleuze ◽

Data Archive ◽

Current State ◽

The Future ◽

The Way

This paper will first briefly map out the shift from disciplinary to control societies (what I call designer capitalism, the idea of control comes from Gilles Deleuze) in relation to surveillance and mediation of life through screen cultures. The paper then shifts to the issues of digitalization in relation to big data that have the danger of continuing to close off life as zoë, that is life that is creative rather than captured via attention technologies through marketing techniques and surveillance. The last part of this paper then develops the way artists are able to resist the big data archive by turning the data in on itself to offer viewers and participants a glimpse of the current state of manipulating desire and maintaining copy right in order to keep the future closed rather than being potentially open.

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Procurement Analytics: Enabling Data Architectures in the Age of Big Data

The Oxford Handbook of Supply Chain Management ◽

10.1093/oxfordhb/9780190066727.013.5 ◽

2020 ◽

Author(s):

Michael Goul ◽

T. S. Raghu ◽

Ziru Li

Keyword(s):

Big Data ◽

Academic Research ◽

Research Literature ◽

Structured Interviews ◽

Data Space ◽

Prescriptive Analytics ◽

Data Architecture ◽

The Future ◽

High Level ◽

Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

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FEATURES

Asia-Pacific Biotech News ◽

10.1142/s0219030315000634 ◽

2015 ◽

Vol 19 (10) ◽

pp. 17-35 ◽

Cited By ~ 1

Keyword(s):

Big Data ◽

Disease Management ◽

Clinical Research ◽

Health Informatics ◽

Healthcare Management ◽

Spatial Awareness ◽

Digital Tool ◽

Future Health ◽

Research Sector ◽

The Future

Amplifying Spatial Awareness via GIS — Tech which brings Healthcare Management, Preventative & Predictive Measures under the same Cloud When it is not just about size, you gotta' be Smart, too! Chew on It! How Singapore-based health informatics company MHC Asia Group crunches big-data to uncover your company's health Digital tool when well-used, it is Passion Carving the Digital Route to Wellness Big Data, Bigger Disease Management and Current preparations to manage the Future Health of Singaporeans A Conversation with Mr Arun Puri Extreme Networks: Health Solutions Big Data in Clinical Research Sector

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