Big Data: Challenges, practices and technologies: NIST Big Data Public Working Group workshop at IEEE Big Data 2014

Grady, Nancy W.; Underwood, Mark; Roy, Arnab; Chang, Wo L.

doi:10.1109/bigdata.2014.7004470

Cited by 21 publications

(7 citation statements)

References 1 publication

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“…The idea is to use RFID tags on each product, so whenever a customer buys a product, he/she will get an instant recommendation on his/her mobile or nearby screens while paying to the cashier. The smart AI and IoT-based RFID system will work together to personalize the shopping experience by suggesting the best recommendations to the customer [4] [6].…”

Section: Frameworkmentioning

confidence: 99%

An Advanced Framework to Design A Smart Store System using IoT, AI, and Data Analytics that Improves Business Processes for Micro and Small Businesses

Ramgir*¹

2019

IJITEE

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Small and Microbusinesses struggle for their survival as they are always short of resources, clients and data to make informed decisions. Only if they knew what to sell, when to sell and whom to sell, they would not only survive but also thrive. This is where they need some help of technology like IoT and AI to take their business to the next level. IoT and AI can be used as a combination to improve various business processes for small and micro businesses. This led to the invention of a new Framework which uses IoT, AI and Data Analytics to improve business processes for various types of businesses. Let us take a service example like selling personal use stuff such as clothes, jewelry, shoes, etc. These types of businesses could use printable near field RFID tags that can be bought for as low as $0.01 each (price may go down for bulk purchases) to put on the products. This tag will carry various information like purchase history, price history, discounts, availability, etc. With the use of these tags, businesses would be able to personalize the buying experience for the customers and learning their shopping habits. This could open up greater revenue opportunities by improving the existing customer base [1][4]. In this paper, we will discuss this framework and an implementation of it in a retail world.

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Section: Frameworkmentioning

confidence: 99%

An Advanced Framework to Design A Smart Store System using IoT, AI, and Data Analytics that Improves Business Processes for Micro and Small Businesses

Ramgir*¹

2019

IJITEE

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“…Hence, not surprisingly, it has been observed that most data projects are managed in an ad hoc fashion, that is, at a low level of process maturity [12]. Indeed, it has been argued that data science projects need to focus on people, process and technology [13,14] and not just on algorithms used by data scientists.…”

Section: The Need For Improved Coordinationmentioning

confidence: 99%

Helping Data Science Students Develop Task Modularity

Saltz

Heckman

Crowston

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper explores the skills needed to be a data scientist. Specifically, we report on a mixed method study of a project-based data science class, where we evaluated student effectiveness with respect to dividing a project into appropriately sized modular tasks, which we termed task modularity. Our results suggest that while data science students can appreciate the value of task modularity, they struggle to achieve effective task modularity. As a first step, based our study, we identified six task decomposition best practices. However, these best practices do not fully address this gap of how to enable data science students to effectively use task modularity. We note that while computer science/information system programs typically teach modularity (e.g., the decomposition process and abstraction), and there remains a need identify a corresponding model to that used for computer science / information system students, to teach modularity to data science students.

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“…The explosive growth and dispersion of digital data producers in edge environments is a highly multidimensional problem. Looking at just the properties of the data flows being generated, the published lists of the challenging characteristics they exhibit include their volume, velocity, value, variety, variability, and veracity (Bethel et al, 2016; Chang, 2015; Grady et al, 2014; Hashem et al, 2015; ur Rehman et al, 2016). The size dimension tends to be highlighted first, if only because the volumes are so striking.…”

Section: Challenges To Converged Infrastructure In Edge Environmentsmentioning

confidence: 99%

Big data and extreme-scale computing

Asch

Moore

Badía

et al. 2018

The International Journal of High Performance Computing Applica

103

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Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.

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Big Data: Challenges, practices and technologies: NIST Big Data Public Working Group workshop at IEEE Big Data 2014

Cited by 21 publications

References 1 publication

An Advanced Framework to Design A Smart Store System using IoT, AI, and Data Analytics that Improves Business Processes for Micro and Small Businesses

An Advanced Framework to Design A Smart Store System using IoT, AI, and Data Analytics that Improves Business Processes for Micro and Small Businesses

Helping Data Science Students Develop Task Modularity

Big data and extreme-scale computing

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