Big Data Collection and Analysis Framework Research for Public Digital Culture Sharing Service

2015 ◽  
Author(s):  
Guigang Zhang ◽  
Jian Wang ◽  
Weixin Huang ◽  
Haixia Su ◽  
Zhi Lv ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Digital Culture ◽  
Analysis Framework ◽  
Data Collection And Analysis

scholarly journals Thinking about police data: Analysts’ perceptions of data quality in Canadian policing

2021 ◽  
pp. 0032258X2110214
Author(s):  
Christopher D O’Connor ◽  
John Ng ◽  
Dallas Hill ◽  
Tyler Frederick
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Quality ◽  
Research Culture ◽  
Police Services ◽  
Police Data ◽  
Data Collection And Analysis ◽  
Quality Issues

Policing is increasingly being shaped by data collection and analysis. However, we still know little about the quality of the data police services acquire and utilize. Drawing on a survey of analysts from across Canada, this article examines several data collection, analysis, and quality issues. We argue that as we move towards an era of big data policing it is imperative that police services pay more attention to the quality of the data they collect. We conclude by discussing the implications of ignoring data quality issues and the need to develop a more robust research culture in policing.

Applied tensional analysis: Engaging practitioners and the constitutive shift

2019 ◽  
Vol 50 (4) ◽  
pp. 409-426 ◽  
Author(s):  
Jennifer J Mease
Keyword(s):  
Data Collection ◽  
Methodological Approach ◽  
Organizational Knowledge ◽  
Methodological Framework ◽  
Analysis Framework ◽  
Data Collection And Analysis ◽  
The Everyday ◽  
Organizational Experiences

This article introduces applied tensional analysis as a methodological framework that integrates constitutive ontologies (that depict organizations as processes in constant states of emerging or becoming) with the applied need for practitioners to understand and navigate the everyday exigencies of their organizational experiences. Applied tensional analysis centers analysis on tensions as the key to understanding organizational becoming in contrast to approaches that assume organizations are stable entities and consequently focus on patterns, themes, or laws. The applied tensional analysis framework offers four analytical foci (context, tensions, enacted responses, and repertoires) organized into two loops (analytical and change) as guides for data collection and analysis. While the analytical loop orients scholars to the current and past configurations of an organization’s emergence, the change loop emphasizes the multitude of available responses to a particular tension and the constitutive implications of those responses for organizational becoming. As a new methodological approach, applied tensional analysis suggests that organizational knowledge requires more than awareness of what an organization is and includes awareness of organizational potential and what an organization might become.

Regional Ways of Seeing: A Big-Data Approach for Measuring Ancient Visualscapes

2020 ◽  
Vol 8 (2) ◽  
pp. 174-191
Author(s):  
Natalie M. Susmann
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Natural World ◽  
Natural Landscape ◽  
Analysis Methodology ◽  
Data Collection And Analysis ◽  
Geospatial Analyses ◽  
Human Decision ◽  
Topographic Characteristics ◽  
Religious Landscape

AbstractArchaeologists have long acknowledged the significance of mountains in siting Greek cult. Mountains were where the gods preferred to make contact and there people constructed sanctuaries to inspire intervention. Greece is a land full of mountains, but we lack insight on the ancient Greeks’ view—what visible and topographic characteristics made particular mountains ideal places for worship over others, and whether worshiper preferences ever changed. This article describes a data collection and analysis methodology for landscapes where visualscape was a significant factor in situating culturally significant activities. Using a big-data approach, four geospatial analyses are applied to every cultic place in the Peloponnesian regions of the Argolid and Messenia, spanning 2800–146 BC. The fully described methodology combines a number of experiences—looking out, looking toward, and climbing up—and measures how these change through time. The result is an active historic model of Greek religious landscape, describing how individuals moved, saw, and integrated the built and natural world in different ways. Applied elsewhere, and even on nonreligious locales, this is a replicable mode for treating the natural landscape as an artifact of human decision: as a space impacting the siting of meaningful locales through history.

On Building Better Mousetraps and Understanding the Human Condition

2015 ◽  
Vol 659 (1) ◽  
pp. 33-47 ◽  
Author(s):  
Jimmy Lin
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Scientific Reasoning ◽  
Explanatory Power ◽  
Data Use ◽  
Human Condition ◽  
Science And Engineering ◽  
Disruptive Technologies ◽  
The Past ◽  
Data Collection And Analysis

Over the past few years, we have seen the emergence of “big data”: disruptive technologies that have transformed commerce, science, and many aspects of society. Despite the tremendous enthusiasm for big data, there is no shortage of detractors. This article argues that many criticisms stem from a fundamental confusion over goals: whether the desired outcome of big data use is “better science” or “better engineering.” Critics point to the rejection of traditional data collection and analysis methods, confusion between correlation and causation, and an indifference to models with explanatory power. From the perspective of advancing social science, these are valid reservations. I contend, however, that if the end goal of big data use is to engineer computational artifacts that are more effective according to well-defined metrics, then whatever improves those metrics should be exploited without prejudice. Sound scientific reasoning, while helpful, is not necessary to improve engineering. Understanding the distinction between science and engineering resolves many of the apparent controversies surrounding big data and helps to clarify the criteria by which contributions should be assessed.

scholarly journals A Big Data Platform for heterogeneous data collection and analysis in large-scale data centres

2021 ◽  
Author(s):  
Simone Rossi Tisbeni ◽  
Daniele CESINI ◽  
Barbara Martelli ◽  
Arianna Carbone ◽  
Claudia Cavallaro ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Large Scale Data ◽  
Data Collection And Analysis ◽  
Data Platform ◽  
Data Centres ◽  
Scale Data

scholarly journals Big data collection and analysis for manufacturing organisations

2017 ◽  
Vol 2 (2) ◽  
pp. 127-139 ◽  
Author(s):  
Pankaj Sharma ◽  
◽  
David Baglee ◽  
Jaime Campos ◽  
Erkki Jantunen ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Collection And Analysis

scholarly journals Improving Food safety in Supply Chain based on Big Data

2018 ◽  
Vol 53 ◽  
pp. 03084
Author(s):  
Gang Liu ◽  
Guang Li ◽  
Rui Yang ◽  
Li Guo
Keyword(s):  
Big Data ◽  
Food Safety ◽  
Data Collection ◽  
Safety Management ◽  
Rapid Development ◽  
Warning System ◽  
Risk Warning ◽  
Data Collection And Analysis ◽  
Data Source ◽  
Food Safety Risk

With the rapid development of big data collection and analysis, these tools are increasingly applied to food safety and quality. Big data can play an important role in improving food safety management. This paper will deeply analyze the food safety risk warning system based on big data management. The research results show that the food safety management system based on big data includes data source, data collection and storage, data analysis and application of analysis results.

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