“Data-driven” papers · AI for Research

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Data Driven vs. Metric Driven Data Warehouse Design

John M. Artz · IGI Global eBooks · 2005

Although data warehousing theory and technology have been around for well over a decade, they may well be the next hot technologies. How can it be that a technology sleeps for so long and then begins to move rapidly to the foreground? This question can have several answers. Perhaps the technology had not yet caught up to the theory or that computer technology 10 years ago did not have the capacity to delivery what the theory promised. Perhaps the ideas and the products were just ahead of their time. All these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical and paradigmatic shift, and that shift will reposition data warehousing to meet future demands.

Cited by 2

Data Driven vs. Metric Driven Data Warehouse Design

John M. Artz · IGI Global eBooks · 2009

Although data warehousing theory and technology have been around for well over a decade, they may well be the next hot technologies. How can it be that a technology sleeps for so long and then begins to move rapidly to the foreground? This question can have several answers. Perhaps the technology had not yet caught up to the theory or that computer technology 10 years ago did not have the capacity to delivery what the theory promised. Perhaps the ideas and the products were just ahead of their time. All these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical and paradigmatic shift, and that shift will reposition data warehousing to meet future demands.

Cited by 6

Data-driven Innovation or Innovation-driven Data Generation?

Youshan Yu · 2023

With the emergence of artificial intelligence (AI), data-driven innovation has become a new paradigm in innovation management. As these solutions are increasingly integrated into complex, safety-critical systems, data becomes crucial in these contexts. However, safety-critical applications require high-quality and verified data in areas where sometimes only sparse data is available. This paper explores the role of data in these contexts and sheds light on data and innovation in complex intelligent systems (CoIS). The findings reveal that a lack of available data may hinder the innovation process and that the necessary training data must be generated through the innovation itself. The results thus show that the emerging data-driven innovation paradigm needs to be complemented with a focus on understanding innovation-driven data generation in the context of systems with safety-critical applications.

Cited by 2

Data Driven vs. Metric Driven Data Warehouse Design

John M. Artz · IGI Global eBooks · 2011

Although data warehousing theory and technology have been around for well over a decade, they may well be the next hot technologies. How can it be that a technology sleeps for so long and then begins to move rapidly to the foreground? This question can have several answers. Perhaps the technology had not yet caught up to the theory or that computer technology 10 years ago did not have the capacity to delivery what the theory promised. Perhaps the ideas and the products were just ahead of their time. All these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical and paradigmatic shift, and that shift will reposition data warehousing to meet future demands. Request access from your librarian to read this chapter's full text.

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D3 data-driven documents pocket primer: data driven documents

Campesato, Oswald · CERN Document Server (European Organization for Nuclear Research) · 2015

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Data-driven theory formulation or theory-driven data interpretation?

Imre Lahdelma, Tuomas Eerola · Physics of Life Reviews · 2023

Cited by 2Open access

Data-Driven Editorial?

Caitlin Petre · Amsterdam University Press eBooks · 2021

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Data-Driven Tests

Owen Heaume · Apress eBooks · 2024

Efficiency in testing is not just about ensuring the functionality of individual components but also about managing your testing suite intelligently. As your PowerShell scripts evolve and diversify, you may encounter scenarios where numerous tests share a common structure or objective. Writing these tests individually can be time-consuming and may lead to redundancy in your codebase.

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Data Driven Mobility

Slik, Jesper Siem · Data Archiving and Networked Services (DANS) · 2022

Each minute in 2020, over 250,000 online meetings were held, more than 500 hours of video were uploaded, and USD 1M was spent online. At the end of the year, over 64ZB of data were created. Data is becoming a major part of our lives, and a continued growth is expected. In this dissertation, we demonstrate how to drive decisions based on data. We propose various methodology and contribute to different fields. Additionally, we implement part of the work through industry partnerships and achieve real-life results. Data is the oil of the 21st century, according to various experts around the world. Its promise is to support any organization in making better decisions. The comparison with a natural resource, like oil, seems to make sense, as data does little on its own. It needs to be converted to information, knowledge, or wisdom in order to deliver value. By 2022, organizations will have figured a way to deliver part of its promised value, resulting in an estimated 274 billion USD global industry. However, unlike a natural resource, data is practically infinite, reusable, and becoming increasingly available. Therefore, novel challenges arise. A main, current challenge lies in identifying decisions and designing methodology for direct support. Often, substantial investment is required before the value of these supportive analyses is certain or even recognized. Auxiliary challenges include data integration, analytical skills, security and privacy, infrastructure, and synchronization. Through nine independent chapters we propose methodology in which these challenges are tackled. The first three chapters of this dissertation concern descriptive analyses. These analyses aim to describe what happened. Intuitively, this seems a simple task, however, various technical or human errors can establish a challenge. The consecutive three chapters concern predictive analyses. These analyses build on descriptive analyses and aim to predict what is going to happen. A main challenge is to find meaningful, robust patterns and prevent overfitting. The final two chapters concern prescriptive analyses. These build on predictive analyses and aim to prescribe what to do. A main challenge is to balance the exploration of new knowledge and the exploitation of current knowledge in order to prescribe an optimal strategy.

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